Patient Identification with ECG and SaO2 Time Series

Sudden cardiac death is the most common cause of death in United States. Primary prevention implantable cardioverter defibrillators (ICDs) have been the first line to reduce mortality for high-risk patients. Previous work of identifying subjects at greater risk is neither sensitive nor specific. The development of more reliable predictors that could help identify patients that could benefit from these devices is of both academic and public health interest. In this thesis, we study the time series data of both electrocardiogram (ECG) and oxygen saturation (SaO2) signals from patients who received ICD implantation. This study is part of Prospective Observational Study of Implantable Cardioverter Defibrillators (PROSE-ICD). The features for each subject are generated from some statistics of the ECG and SaO2 signals respectively. For ECG signal, the analysis is from both geometry and dynamics perspective. For SaO2 signal, multivariate and dynamics analysis is applied. Our results showed an overall accuracy of 93.2% for patient classification, with no bias towards healthy or HF patients. Further analysis does not show a clear relationship between ECG and SaO2 signals

Fast Adversarial Training with Smooth Convergence

Fast adversarial training (FAT) is beneficial for improving the adversarial robustness of neural networks. However, previous FAT work has encountered a significant issue known as catastrophic overfitting when dealing with large perturbation budgets, \ie the adversarial robustness of models declines to near zero during training. To address this, we analyze the training process of prior FAT work and observe that catastrophic overfitting is accompanied by the appearance of loss convergence outliers. Therefore, we argue a moderately smooth loss convergence process will be a stable FAT process that solves catastrophic overfitting. To obtain a smooth loss convergence process, we propose a novel oscillatory constraint (dubbed ConvergeSmooth) to limit the loss difference between adjacent epochs. The convergence stride of ConvergeSmooth is introduced to balance convergence and smoothing. Likewise, we design weight centralization without introducing additional hyperparameters other than the loss balance coefficient. Our proposed methods are attack-agnostic and thus can improve the training stability of various FAT techniques. Extensive experiments on popular datasets show that the proposed methods efficiently avoid catastrophic overfitting and outperform all previous FAT methods. Code is available at \url{https://github.com/FAT-CS/ConvergeSmooth}

Partitioning of Kinetic Energy in the Arctic Ocean's Beaufort Gyre

Kinetic energy (KE) in the Arctic Ocean's Beaufort Gyre is dominated by the mesoscale eddy field that plays a central role in the transport of freshwater, heat, and biogeochemical tracers. Understanding Beaufort Gyre KE variability sheds light on how this freshwater reservoir responds to wind forcing and sea ice and ocean changes. The evolution and fate of mesoscale eddies relate to energy pathways in the ocean (e.g., the exchange of energy between barotropic and baroclinic modes). Mooring measurements of horizontal velocities in the Beaufort Gyre are analyzed to partition KE into barotropic and baroclinic modes and explore their evolution. We find that a significant fraction of water column KE is in the barotropic and the first two baroclinic modes. We explain this energy partitioning by quantifying the energy transfer coefficients between the vertical modes using the quasi‐geostrophic potential vorticity conservation equations with a specific background stratification observed in the Beaufort Gyre. We find that the quasi‐geostrophic vertical mode interactions uphold the persistence of KE in the first two baroclinic modes, consistent with observations. Our results explain the specific role of halocline structure on KE evolution in the gyre and suggest depressed transfer to the barotropic mode. This limits the capacity for frictional dissipation at the sea floor and suggests that energy dissipation via sea ice‐ocean drag may be prominent

Mitigating Shortcuts in Language Models with Soft Label Encoding

Recent research has shown that large language models rely on spurious correlations in the data for natural language understanding (NLU) tasks. In this work, we aim to answer the following research question: Can we reduce spurious correlations by modifying the ground truth labels of the training data? Specifically, we propose a simple yet effective debiasing framework, named Soft Label Encoding (SoftLE). We first train a teacher model with hard labels to determine each sample's degree of relying on shortcuts. We then add one dummy class to encode the shortcut degree, which is used to smooth other dimensions in the ground truth label to generate soft labels. This new ground truth label is used to train a more robust student model. Extensive experiments on two NLU benchmark tasks demonstrate that SoftLE significantly improves out-of-distribution generalization while maintaining satisfactory in-distribution accuracy

Partitioning of kinetic energy in the Arctic Ocean's Beaufort Gyre

Author Posting. © American Geophysical Union, 2018. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Oceans 123 (2018): 4806-4819, doi:10.1029/2018JC014037.Kinetic energy (KE) in the Arctic Ocean's Beaufort Gyre is dominated by the mesoscale eddy field that plays a central role in the transport of freshwater, heat, and biogeochemical tracers. Understanding Beaufort Gyre KE variability sheds light on how this freshwater reservoir responds to wind forcing and sea ice and ocean changes. The evolution and fate of mesoscale eddies relate to energy pathways in the ocean (e.g., the exchange of energy between barotropic and baroclinic modes). Mooring measurements of horizontal velocities in the Beaufort Gyre are analyzed to partition KE into barotropic and baroclinic modes and explore their evolution. We find that a significant fraction of water column KE is in the barotropic and the first two baroclinic modes. We explain this energy partitioning by quantifying the energy transfer coefficients between the vertical modes using the quasi‐geostrophic potential vorticity conservation equations with a specific background stratification observed in the Beaufort Gyre. We find that the quasi‐geostrophic vertical mode interactions uphold the persistence of KE in the first two baroclinic modes, consistent with observations. Our results explain the specific role of halocline structure on KE evolution in the gyre and suggest depressed transfer to the barotropic mode. This limits the capacity for frictional dissipation at the sea floor and suggests that energy dissipation via sea ice‐ocean drag may be prominent.National Science Foundation Division of Polar Programs Grant Number: 11076232019-01-1

Sparse Recovery over Graph Incidence Matrices

Classical results in sparse recovery guarantee the exact reconstruction of $s$-sparse signals under assumptions on the dictionary that are either too strong or NP-hard to check. Moreover, such results may be pessimistic in practice since they are based on a worst-case analysis. In this paper, we consider the sparse recovery of signals defined over a graph, for which the dictionary takes the form of an incidence matrix. We derive necessary and sufficient conditions for sparse recovery, which depend on properties of the cycles of the graph that can be checked in polynomial time. We also derive support-dependent conditions for sparse recovery that depend only on the intersection of the cycles of the graph with the support of the signal. Finally, we exploit sparsity properties on the measurements and the structure of incidence matrices to propose a specialized sub-graph-based recovery algorithm that outperforms the standard $\ell_1$-minimization approach.Comment: Accepted to 57th IEEE Conference on Decision and Contro

Evolution of the eddy field in the Arctic Ocean's Canada Basin, 2005–2015

Author Posting. © American Geophysical Union, 2016. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 43 (2016): 8106–8114, doi:10.1002/2016GL069671.The eddy field across the Arctic Ocean's Canada Basin is analyzed using Ice-Tethered Profiler (ITP) and moored measurements of temperature, salinity, and velocity spanning 2005 to 2015. ITPs encountered 243 eddies, 98% of which were anticyclones, with approximately 70% of these having anomalously cold cores. The spatially and temporally varying eddy field is analyzed accounting for sampling biases in the unevenly distributed ITP data and caveats in detection methods. The highest concentration of eddies was found in the western and southern portions of the basin, close to topographic margins and boundaries of the Beaufort Gyre. The number of lower halocline eddies approximately doubled from 2005–2012 to 2013–2014. The increased eddy density suggests more active baroclinic instability of the Beaufort Gyre that releases available potential energy to balance the wind energy input; this may stabilize the Gyre spin-up and associated freshwater increase.National Science Foundation Division of Polar Programs Grant Number: 13500462017-02-0

Low-frequency dynamic ocean response to barometric-pressure loading

Author Posting. © American Meteorological Society, 2022. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Physical Oceanography 52(11), (2022): 2627-2641, https://doi.org/10.1175/jpo-d-22-0090.1.Changes in dynamic manometric sea level ζm represent mass-related sea level changes associated with ocean circulation and climate. We use twin model experiments to quantify magnitudes and spatiotemporal scales of ζm variability caused by barometric pressure pa loading at long periods (≳1 month) and large scales (≳300km) relevant to Gravity Recovery and Climate Experiment (GRACE) ocean data. Loading by pa drives basin-scale monthly ζm variability with magnitudes as large as a few centimeters. Largest ζm signals occur over abyssal plains, on the shelf, and in marginal seas. Correlation patterns of modeled ζm are determined by continental coasts and H/f contours (H is ocean depth and f is Coriolis parameter). On average, ζm signals forced by pa represent departures of ≲10% and ≲1% from the inverted-barometer effect ζib on monthly and annual periods, respectively. Basic magnitudes, spatial patterns, and spectral behaviors of ζm from the model are consistent with scaling arguments from barotropic potential vorticity conservation. We also compare ζm from the model driven by pa to ζm from GRACE observations. Modeled and observed ζm are significantly correlated across parts of the tropical and extratropical oceans, on shelf and slope regions, and in marginal seas. Ratios of modeled to observed ζm magnitudes are as large as ∼0.2 (largest in the Arctic Ocean) and qualitatively agree with analytical theory for the gain of the transfer function between ζm forced by pa and wind stress. Results demonstrate that pa loading is a secondary but nevertheless important contributor to monthly mass variability from GRACE over the ocean.The authors acknowledge support from the National Aeronautics and Space Administration through the GRACE Follow-On Science Team (Grant 80NSSC20K0728) and the Sea Level Change Team (Grant 80NSSC20K1241). The contribution from I. F. and O. W. represents research carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (Grant 80NM0018D0004)