Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring

Inferring the source information of greenhouse gases, such as methane, from spatially sparse sensor observations is an essential element in mitigating climate change. While it is well understood that the complex behavior of the atmospheric dispersion of such pollutants is governed by the Advection-Diffusion equation, it is difficult to directly apply the governing equations to identify the source location and magnitude (inverse problem) because of the spatially sparse and noisy observations, i.e., the pollution concentration is known only at the sensor locations and sensors sensitivity is limited. Here, we develop a multi-task learning framework that can provide high-fidelity reconstruction of the concentration field and identify emission characteristics of the pollution sources such as their location, emission strength, etc. from sparse sensor observations. We demonstrate that our proposed framework is able to achieve accurate reconstruction of the methane concentrations from sparse sensor measurements as well as precisely pin-point the location and emission strength of these pollution sources.Comment: 7 pages, 8 figures, 1 tabl

Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation

In recent years, several Weakly Supervised Semantic Segmentation (WS3) methods have been proposed that use class activation maps (CAMs) generated by a classifier to produce pseudo-ground truths for training segmentation models. While CAMs are good at highlighting discriminative regions (DR) of an image, they are known to disregard regions of the object that do not contribute to the classifier's prediction, termed non-discriminative regions (NDR). In contrast, attribution methods such as saliency maps provide an alternative approach for assigning a score to every pixel based on its contribution to the classification prediction. This paper provides a comprehensive comparison between saliencies and CAMs for WS3. Our study includes multiple perspectives on understanding their similarities and dissimilarities. Moreover, we provide new evaluation metrics that perform a comprehensive assessment of WS3 performance of alternative methods w.r.t. CAMs. We demonstrate the effectiveness of saliencies in addressing the limitation of CAMs through our empirical studies on benchmark datasets. Furthermore, we propose random cropping as a stochastic aggregation technique that improves the performance of saliency, making it a strong alternative to CAM for WS3.Comment: 24 pages, 13 figures, 4 table

Mitigating Propagation Failures in PINNs using Evolutionary Sampling

Despite the success of physics-informed neural networks (PINNs) in approximating partial differential equations (PDEs), it is known that PINNs can sometimes fail to converge to the correct solution in problems involving complicated PDEs. This is reflected in several recent studies on characterizing and mitigating the ``failure modes'' of PINNs. While most of these studies have focused on balancing loss functions or adaptively tuning PDE coefficients, what is missing is a thorough understanding of the connection between failure modes of PINNs and sampling strategies used for training PINNs. In this paper, we provide a novel perspective of failure modes of PINNs by hypothesizing that the training of PINNs rely on successful ``propagation'' of solution from initial and/or boundary condition points to interior points. We show that PINNs with poor sampling strategies can get stuck at trivial solutions if there are propagation failures. We additionally demonstrate that propagation failures are characterized by highly imbalanced PDE residual fields where very high residuals are observed over very narrow regions. To mitigate propagation failures, we propose a novel evolutionary sampling (Evo) method that can incrementally accumulate collocation points in regions of high PDE residuals with little to no computational overhead. We provide an extension of Evo to respect the principle of causality while solving time-dependent PDEs. We theoretically analyze the behavior of Evo and empirically demonstrate its efficacy and efficiency in comparison with baselines on a variety of PDE problems.Comment: 34 pages, 46 figures, 2 table

Search for post-merger gravitational waves from the remnant of the binary neutron star merger GW170817

In Advanced LIGO, detection and astrophysical source parameter estimation of the binary black hole merger GW150914 requires a calibrated estimate of the gravitational-wave strain sensed by the detectors. Producing an estimate from each detector's differential arm length control loop readout signals requires applying time domain filters, which are designed from a frequency domain model of the detector's gravitational-wave response. The gravitational-wave response model is determined by the detector's opto-mechanical response and the properties of its feedback control system. The measurements used to validate the model and characterize its uncertainty are derived primarily from a dedicated photon radiation pressure actuator, with cross-checks provided by optical and radio frequency references. We describe how the gravitational-wave readout signal is calibrated into equivalent gravitational-wave-induced strain and how the statistical uncertainties and systematic errors are assessed. Detector data collected over 38 calendar days, from September 12 to October 20, 2015, contain the event GW150914 and approximately 16 of coincident data used to estimate the event false alarm probability. The calibration uncertainty is less than 10% in magnitude and 10 degrees in phase across the relevant frequency band 20 Hz to 1 kHz

First narrow-band search for continuous gravitational waves from known pulsars in advanced detector data

In Advanced LIGO, detection and astrophysical source parameter estimation of the binary black hole merger GW150914 requires a calibrated estimate of the gravitational-wave strain sensed by the detectors. Producing an estimate from each detector's differential arm length control loop readout signals requires applying time domain filters, which are designed from a frequency domain model of the detector's gravitational-wave response. The gravitational-wave response model is determined by the detector's opto-mechanical response and the properties of its feedback control system. The measurements used to validate the model and characterize its uncertainty are derived primarily from a dedicated photon radiation pressure actuator, with cross-checks provided by optical and radio frequency references. We describe how the gravitational-wave readout signal is calibrated into equivalent gravitational-wave-induced strain and how the statistical uncertainties and systematic errors are assessed. Detector data collected over 38 calendar days, from September 12 to October 20, 2015, contain the event GW150914 and approximately 16 of coincident data used to estimate the event false alarm probability. The calibration uncertainty is less than 10% in magnitude and 10 degrees in phase across the relevant frequency band 20 Hz to 1 kHz

Software and data release v1.0.0: Modular compositional learning improves 1D hydrodynamic lake model performance by merging process-based modeling with deep learning

<p>Data and software release for publication in Journal of Advances in Modeling Earth Systems (JAMES).</p&gt

Advancing Understanding of Lake Metabolism using Modular Compositional Learning

<p>Water quality in lakes can be understood through the lens of metabolism, the balance of primary production and respiration. The challenge of understanding lake metabolism at regional to continental scales is due in part to sparse data availability and a lack of knowledge regarding factors controlling processes at broad scales. To address issues with the scalability of contemporary lake metabolism models, we are leveraging Ecology Knowledge-guided Machine Learning (Eco-KGML). Within the Eco-KGML paradigm, the Modular Compositional Learning (MCL) framework enables the segmentation of a model into smaller modules that can be either process-based or machine learning. Different combinations of modules can be tested to create the most effective predictor of a target variable, which in the case of our metabolism model is water quality. MCL metabolism models can be trained on well-studied systems, then applied to lakes with sparse data. While our MCL metabolism model is still in early development, examples of MCL for simulating lake physics have shown better prediction skill than purely process-based or purely machine learning models. The integration of machine learning into ecological modeling is a novel concept that is made possible only by the ecological insights and unique data collected by the North Temperate Lakes Long-Term Ecological Research (NTL-LTER) program and the National Ecological Observatory Network (NEON).</p&gt

Erratum: First narrow-band search for continuous gravitational waves from known pulsars in advanced detector data [Phys. Rev. D 96, 122006 (2017)]

In Advanced LIGO, detection and astrophysical source parameter estimation of the binary black hole merger GW150914 requires a calibrated estimate of the gravitational-wave strain sensed by the detectors. Producing an estimate from each detector's differential arm length control loop readout signals requires applying time domain filters, which are designed from a frequency domain model of the detector's gravitational-wave response. The gravitational-wave response model is determined by the detector's opto-mechanical response and the properties of its feedback control system. The measurements used to validate the model and characterize its uncertainty are derived primarily from a dedicated photon radiation pressure actuator, with cross-checks provided by optical and radio frequency references. We describe how the gravitational-wave readout signal is calibrated into equivalent gravitational-wave-induced strain and how the statistical uncertainties and systematic errors are assessed. Detector data collected over 38 calendar days, from September 12 to October 20, 2015, contain the event GW150914 and approximately 16 of coincident data used to estimate the event false alarm probability. The calibration uncertainty is less than 10% in magnitude and 10 degrees in phase across the relevant frequency band 20 Hz to 1 kHz