11 research outputs found
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
Motion Enhanced MultiâLevel Tracker (MEMTrack): A Deep LearningâBased Approach to Microrobot Tracking in Dense and LowâContrast Environments
Tracking microrobots is challenging due to their minute size and high speed. In biomedical applications, this challenge is exacerbated by the dense surrounding environments with feature sizes and shapes comparable to microrobots. Herein, Motion Enhanced Multiâlevel Tracker (MEMTrack) is introduced for detecting and tracking microrobots in dense and lowâcontrast environments. Informed by the physics of microrobot motion, synthetic motion features for deep learningâbased object detection and a modified Simple Online and Realâtime Tracking (SORT)algorithm with interpolation are used for tracking. MEMTrack is trained and tested using bacterial micromotors in collagen (tissue phantom), achieving precision and recall of 76% and 51%, respectively. Compared to the stateâofâtheâart baseline models, MEMTrack provides a minimum of 2.6âfold higher precision with a reasonably high recall. MEMTrack's generalizability to unseen (aqueous) media and its versatility in tracking microrobots of different shapes, sizes, and motion characteristics are shown. Finally, it is shown that MEMTrack localizes objects with a rootâmeanâsquare error of less than 1.84âÎŒm and quantifies the average speed of all tested systems with no statistically significant difference from the laboriously produced manual tracking data. MEMTrack significantly advances microrobot localization and tracking in dense and lowâcontrast settings and can impact fundamental and translational microrobotic research
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>
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>