arXiv subjects

Christopher X. Ren

Publications and source records attributed to Christopher X. Ren.

11 recordsLinked to original sources

arXiv2022

Learning Continuous Exponential Families Beyond Gaussian

We address the problem of learning of continuous exponential family distributions with unbounded support. While a lot of progress has been made on learning of Gaussian graphical models, we still lack scalable algorithms for reconstructing general continuous exponential families modeling higher-order moments of the data beyond the mean and the covariance. Here, we introduce a computationally efficient method for learning continuous graphical models based on the Interaction Screening approach. Through a series of numerical experiments, we show that our estimator maintains similar requirements in terms of accuracy and sample complexity scalings compared to alternative approaches such as maximization of conditional likelihood, while considerably improving upon the algorithm's run-time.

cs.LG↗

arXiv2020

Tremor Waveform Denoising and Automatic Location with Neural Network Interpretation

Active faults release tectonic stress imposed by plate motion through a spectrum of slip modes, from slow, aseismic slip, to dynamic, seismic events. Slow earthquakes are often associated with tectonic tremor, non-impulsive signals that can easily be buried in seismic noise and go undetected. We present a new methodology aimed at improving the detection and location of tremors hidden within seismic noise. After identifying tremors with a classic convolutional neural network, we rely on neural network attribution to extract core tremor signatures and denoise input waveforms. We then use these cleaned waveforms to locate tremors with standard array-based techniques. We apply this method to the Cascadia subduction zone, where we identify tremor patches consistent with existing catalogs. In particular, we show that the cleaned signals resulting from the neural network attribution analysis correspond to a waveform traveling in the Earth's crust and mantle at wavespeeds consistent with local estimates. This approach allows us to extract small signals hidden within the noise, and therefore to locate more tremors than in existing catalogs.

physics.geo-ph↗

arXiv2020

Deep Snow: Synthesizing Remote Sensing Imagery with Generative Adversarial Nets

In this work we demonstrate that generative adversarial networks (GANs) can be used to generate realistic pervasive changes in remote sensing imagery, even in an unpaired training setting. We investigate some transformation quality metrics based on deep embedding of the generated and real images which enable visualization and understanding of the training dynamics of the GAN, and may provide a useful measure in terms of quantifying how distinguishable the generated images are from real images. We also identify some artifacts introduced by the GAN in the generated images, which are likely to contribute to the differences seen between the real and generated samples in the deep embedding feature space even in cases where the real and generated samples appear perceptually similar.

eess.IV↗

arXiv2020

Cycle-Consistent Adversarial Networks for Realistic Pervasive Change Generation in Remote Sensing Imagery

This paper introduces a new method of generating realistic pervasive changes in the context of evaluating the effectiveness of change detection algorithms in controlled settings. The method, a cycle-consistent adversarial network (CycleGAN), requires low quantities of training data to generate realistic changes. Here we show an application of CycleGAN in creating realistic snow-covered scenes of multispectral Sentinel-2 imagery, and demonstrate how these images can be used as a test bed for anomalous change detection algorithms.

eess.IV↗

arXiv2020

Feature Augmentation Improves Anomalous Change Detection for Human Activity Identification in Synthetic Aperture Radar Imagery

Anomalous change detection (ACD) methods separate common, uninteresting changes from rare, significant changes in co-registered images collected at different points in time. In this paper we evaluate methods to improve the performance of ACD in detecting human activity in SAR imagery using outdoor music festivals as a target. Our results show that the low dimensionality of SAR data leads to poor performance of ACD when compared to simpler methods such as image differencing, but augmenting the dimensionality of our input feature space by incorporating local spatial information leads to enhanced performance.

cs.CV↗

arXiv2020

On the Detectability of Conflict: a Remote Sensing Study of the Rohingya Conflict

The detection and quantification of conflict through remote sensing modalities represents a challenging but crucial aspect of human rights monitoring. In this work we demonstrate how utilizing multi-modal data sources can help build a comprehensive picture of conflict and human displacement, using the Rohingya conflict in the state of Rakhine, Myanmar as a case study. We show that time series analysis of fire detections from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) can reveal anomalous spatial and temporal distributions of fires related to conflict. This work also shows that Synthetic Aperture Radar (SAR) backscatter and coherence data can detect the razing and burning of buildings and villages, even in cloudy conditions. These techniques may be further developed in the future to enable the monitoring and detection of signals originating from these types of conflict.

stat.AP↗

arXiv2020

BUDD: Multi-modal Bayesian Updating Deforestation Detections

The global phenomenon of forest degradation is a pressing issue with severe implications for climate stability and biodiversity protection. In this work we generate Bayesian updating deforestation detection (BUDD) algorithms by incorporating Sentinel-1 backscatter and interferometric coherence with Sentinel-2 normalized vegetation index data. We show that the algorithm provides good performance in validation AOIs. We compare the effectiveness of different combinations of the three data modalities as inputs into the BUDD algorithm and compare against existing benchmarks based on optical imagery.

stat.AP↗

arXiv2019

From Stress Chains to Acoustic Emission

A numerical scheme using the combined finite-discrete element methods (FDEM) is employed to study a model of an earthquake system comprising a granular layer embedded in a formation. When the formation is driven so as to shear the granular layer, a system of stress chains emerges. The stress chains endow the layer with resistance to shear and on failure launch broadcasts into the formation. These broadcasts, received as acoustic emission, provide a remote monitor of the state of the granular layer, of the earthquake system.

physics.geo-ph↗

arXiv2018

Estimating the Physical State of a Laboratory Slow Slipping Fault from Seismic Signals

Over the last two decades, strain and GPS measurements have shown that slow slip on earthquake faults is a widespread phenomenon. Slow slip is also inferred from correlated small amplitude seismic signals known as nonvolcanic tremor and low frequency earthquakes (LFEs). Slow slip has been reproduced in laboratory and simulation studies, however the fundamental physics of these phenomena and their relationship to dynamic earthquake rupture remains poorly understood. Here we show that, in a laboratory setting, continuous seismic waves are imprinted with fundamental signatures of the fault's physical state. Using machine learning on continuous seismic waves, we can infer several bulk characteristics of the fault (friction, shear displacement, gouge thickness), at any time during the slow slip cycle. This analysis also allows us to infer many properties of the future behavior of the fault, including the time remaining before the next slow slip event. Our work suggests that by applying machine learning approaches to continuous seismic data, new insight into the physics of slow slip could be obtained in Earth.

physics.geo-ph↗

arXiv2018

Estimating Fault Friction from Seismic Signals in the Laboratory

Nearly all aspects of earthquake rupture are controlled by the friction along the fault that progressively increases with tectonic forcing, but in general cannot be directly measured. We show that fault friction can be determined at any time, from the continuous seismic signal. In a classic laboratory experiment of repeating earthquakes, we find that the seismic signal follows a specific pattern with respect to fault friction, allowing us to determine the fault's position within its failure cycle. Using machine learning, we show that instantaneous statistical characteristics of the seismic signal are a fingerprint of the fault zone shear stress and frictional state. Further analysis of this fingerprint leads to a simple equation of state quantitatively relating the seismic signal power and the friction on the fault. These results show that fault zone frictional characteristics and the state of stress in the surroundings of the fault can be inferred from seismic waves, at least in the laboratory.

physics.geo-ph↗

arXiv2016

Ultrafast, polarized, single-photon emission from m-plane InGaN Quantum Dots on GaN nanowires

We demonstrate single photon emission from self-assembled m-plane InGaN quantum dots (QDs) embedded on the side-walls of GaN nanowires. A combination of electron microscopy, cathodoluminescence, time-resolved micro-PL and photon autocorrelation experiments give a thorough evaluation of the QDs structural and optical properties. The QD exhibits anti-bunched emission up to 100 K, with a measured autocorrelation function of g^2(0) = 0.28 (0.03) at 5 K. Studies on a statistically significant number of QDs show that these m-plane QDs exhibit very fast radiative lifetimes (260 +/- 55 ps) suggesting smaller internal fields than any of the previously reported c-plane and a-plane QDs. Moreover, the observed single photons are almost completely linearly polarized aligned perpendicular to the crystallographic c-axis with a degree of linear polarization of 0.84 +/- 0.12. Such InGaN QDs incorporated in a nanowire system meet many of the requirements for implementation into quantum information systems and could potentially open the door to wholly new device concepts.

cond-mat.mes-hall↗