16 research outputs found
Improving our Understanding of High-latitude Tropospheric Marine Aerosols using NASA Earth-Observing satellites and the Biogeochemical Southern Ocean State Estimate (B-SOSE)
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Improving our Understanding of High-latitude Tropospheric Marine Aerosols using NASA Earth-Observing satellites and the Biogeochemical Southern Ocean State Estimate (B-SOSE)
No full textTropospheric marine aerosol presence in the western Antarctic is coupled to physical and biological processes. These aerosols may be biogenic, formed from the activity of primary producers, and can be associated with seasonal dynamics of sea ice melt and phytoplankton blooms. These aerosols may also influence local environments of polar regions by absorbing and scattering solar radiation and by initiating cloud formation. To study tropospheric marine aerosol in the remote marine Bellingshausen Sea environment, we used a specialized instrument onboard the NASA Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) capable of detecting the presence of aerosols at altitudes close to the sea surface. We termed this measurement marine aerosol optical depth (MAOD). To further support these observations, we also examined coarse-mode aerosol optical depth (AODC), often used as a proxy for sea spray aerosol (SSA). We used MAOD and AODC to examine trends in marine tropospheric aerosol and undertook a multi-year remote sensing analysis in the Bellingshausen Sea from 2006-2018.Across open ocean to coastal regions, daily fluctuations in nighttime and daytime winds, respectively, drove increasing MAOD and AODC. MAOD depicted strong correlations with wind speed across the open ocean and weak correlations in coastal regions, suggesting that daily fluxes in wind speed drive the production of SSA. In the open ocean, we further observed that warmer SST enhanced AODC and the associated production of SSA, supporting prior studies. We also observed seasonal increases in MAOD alongside a seasonal increase in chl-a and the melt of sea ice; these patterns suggest that biological activity of primary producers likely contributed towards magnitudes of marine tropospheric aerosol. This work is also the first to distinguish a late winter to early spring temporal MAOD signal, likely tied to an aerosol source from either venting of biogenic aerosol from breaks in sea ice or to the production of SSA resulting from pulses in wind speed. Our work extends upon previous findings of marine aerosol in polar environments and more fully characterizes interactions during polar winter
Improving our Understanding of High-latitude Tropospheric Marine Aerosols using NASA Earth-Observing satellites and the Biogeochemical Southern Ocean State Estimate (B-SOSE)
No full textMicrobial communities in methane- and short chain alkane-rich hydrothermal sediments of Guaymas Basin
No full textThe hydrothermal sediments of Guaymas Basin, an active spreading center in the Gulf of California (Mexico), are rich in porewater methane, short-chain alkanes, sulfate and sulfide, and provide a model system to explore habitat preferences of microorganisms, including sulfate-dependent, methane- and short chain alkane-oxidizing microbial communities. In this study, sediments (above 60˚C) covered with sulfur-oxidizing microbial mats surrounding a hydrothermal mound (termed Mat Mound) were characterized by porewater geochemistry of methane, C2-C6 short-chain alkanes, sulfate, sulfide, sulfate reduction rate measurements, in-situ temperature gradients, bacterial and archaeal 16S rRNA gene clone libraries and V6 tag pyrosequencing. The most abundantly detected groups in the Mat mound sediments include anaerobic methane-oxidizing archaea of the ANME-1 lineage and its sister clade ANME-1Guaymas, the uncultured bacterial groups SEEP-SRB2 within the Deltaproteobacteria and the separately branching HotSeep-1 Group; these uncultured bacteria are candidates for sulfate-reducing alkane oxidation and for sulfate-reducing syntrophy with ANME archaea. The archaeal dataset indicates distinct habitat preferences for ANME-1, ANME-1-Guaymas and ANME-2 archaea in Guaymas Basin hydrothermal sediments. The bacterial groups SEEP-SRB2 and HotSeep-1 co-occur with ANME-1 and ANME-1Guaymas in hydrothermally active sediments underneath microbial mats in Guaymas Basin. We propose the working hypothesis that this mixed bacterial and archaeal community catalyzes the oxidation of both methane and short-chain alkanes, and constitutes a microbial community signature that is characteristic for hydrothermal and/or cold seep sediments containing both substrates
Adaptive Lighting for Data-Driven Non-Line-Of-Sight 3D Localization
No full textabstract: Non-line-of-sight (NLOS) imaging of objects not visible to either the camera or illumina-
tion source is a challenging task with vital applications including surveillance and robotics.
Recent NLOS reconstruction advances have been achieved using time-resolved measure-
ments. Acquiring these time-resolved measurements requires expensive and specialized
detectors and laser sources. In work proposes a data-driven approach for NLOS 3D local-
ization requiring only a conventional camera and projector. The localisation is performed
using a voxelisation and a regression problem. Accuracy of greater than 90% is achieved
in localizing a NLOS object to a 5cm × 5cm × 5cm volume in real data. By adopting
the regression approach an object of width 10cm to localised to approximately 1.5cm. To
generalize to line-of-sight (LOS) scenes with non-planar surfaces, an adaptive lighting al-
gorithm is adopted. This algorithm, based on radiosity, identifies and illuminates scene
patches in the LOS which most contribute to the NLOS light paths, and can factor in sys-
tem power constraints. Improvements ranging from 6%-15% in accuracy with a non-planar
LOS wall using adaptive lighting is reported, demonstrating the advantage of combining
the physics of light transport with active illumination for data-driven NLOS imaging.Dissertation/ThesisMasters Thesis Electrical Engineering 201
Machine Learning-based Analysis of the Relationship Between the Human Gut Microbiome and Bone Health
No full textabstract: The Human Gut Microbiome (GM) modulates a variety of structural, metabolic, and protective functions to benefit the host. A few recent studies also support the role of the gut microbiome in the regulation of bone health. The relationship between GM and bone health was analyzed based on the data collected from a group of twenty-three adolescent boys and girls who participated in a controlled feeding study, during which two different doses (0 g/d fiber and 12 g/d fiber) of Soluble Corn Fiber (SCF) were added to their diet. This analysis was performed by predicting measures of Bone Mineral Density (BMD) and Bone Mineral Content (BMC) which are indicators of bone strength, using the GM sequence of proportions of 178 microbes collected from 23 subjects, by building a machine learning regression model. The model developed was evaluated by calculating performance metrics such as Root Mean Squared Error, Pearson’s correlation coefficient, and Spearman’s rank correlation coefficient, using cross-validation. A noticeable correlation was observed between the GM and bone health, and it was observed that the overall prediction correlation was higher with SCF intervention (r ~ 0.51). The genera of microbes that played an important role in this relationship were identified. Eubacterium (g), Bacteroides (g), Megamonas (g), Acetivibrio (g), Faecalibacterium (g), and Paraprevotella (g) were some of the microbes that showed an increase in proportion with SCF intervention.Dissertation/ThesisMasters Thesis Electrical Engineering 202
Differentiable Programming for Physics-based Hyperspectral Unmixing
No full textabstract: Hyperspectral unmixing is an important remote sensing task with applications including material identification and analysis. Characteristic spectral features make many pure materials identifiable from their visible-to-infrared spectra, but quantifying their presence within a mixture is a challenging task due to nonlinearities and factors of variation. In this thesis, physics-based approaches are incorporated into an end-to-end spectral unmixing algorithm via differentiable programming. First, sparse regularization and constraints are implemented by adding differentiable penalty terms to a cost function to avoid unrealistic predictions. Secondly, a physics-based dispersion model is introduced to simulate realistic spectral variation, and an efficient method to fit the parameters is presented. Then, this dispersion model is utilized as a generative model within an analysis-by-synthesis spectral unmixing algorithm. Further, a technique for inverse rendering using a convolutional neural network to predict parameters of the generative model is introduced to enhance performance and speed when training data are available. Results achieve state-of-the-art on both infrared and visible-to-near-infrared (VNIR) datasets as compared to baselines, and show promise for the synergy between physics-based models and deep learning in hyperspectral unmixing in the future.Dissertation/ThesisMasters Thesis Electrical Engineering 202
A Study on the Analysis of Treadmill Perturbation Data for the Design of Active Ankle Foot Orthosis to Prevent Falls and Gait Rehabilitation
No full textabstract: According to the Center for Disease Control and Prevention report around 29,668 United States residents aged greater than 65 years had died as a result of a fall in 2016. Other injuries like wrist fractures, hip fractures, and head injuries occur as a result of a fall. Certain groups of people are more prone to experience falls than others, one of which being individuals with stroke. The two most common issues with individuals with strokes are ankle weakness and foot drop, both of which contribute to falls. To mitigate this issue, the most popular clinical remedy given to these users is thermoplastic Ankle Foot Orthosis. These AFO's help improving gait velocity, stride length, and cadence. However, studies have shown that a continuous restraint on the ankle harms the compensatory stepping response and forward propulsion. It has been shown in previous studies that compensatory stepping and forward propulsion are crucial for the user's ability to recover from postural perturbations. Hence, there is a need for active devices that can supply a plantarflexion during the push-off and dorsiflexion during the swing phase of gait. Although advancements in the orthotic research have shown major improvements in supporting the ankle joint for rehabilitation, there is a lack of available active devices that can help impaired users in daily activities. In this study, our primary focus is to build an unobtrusive, cost-effective, and easy to wear active device for gait rehabilitation and fall prevention in individuals who are at risk. The device will be using a double-acting cylinder that can be easily incorporated into the user's footwear using a novel custom-designed powered ankle brace. The device will use Inertial Measurement Units to measure kinematic parameters of the lower body and a custom control algorithm to actuate the device based on the measurements. The study can be used to advance the field of gait assistance, rehabilitation, and potentially fall prevention of individuals with lower-limb impairments through the use of Active Ankle Foot Orthosis.Dissertation/ThesisMasters Thesis Electrical Engineering 202
Quantifying Information Leakage via Adversarial Loss Functions: Theory and Practice
No full textabstract: Modern digital applications have significantly increased the leakage of private and sensitive personal data. While worst-case measures of leakage such as Differential Privacy (DP) provide the strongest guarantees, when utility matters, average-case information-theoretic measures can be more relevant. However, most such information-theoretic measures do not have clear operational meanings. This dissertation addresses this challenge.
This work introduces a tunable leakage measure called maximal -leakage which quantifies the maximal gain of an adversary in inferring any function of a data set. The inferential capability of the adversary is modeled by a class of loss functions, namely, -loss. The choice of determines specific adversarial actions ranging from refining a belief for to guessing the best posterior for , and for the two specific values maximal -leakage simplifies to mutual information and maximal leakage, respectively. Maximal -leakage is proved to have a composition property and be robust to side information.
There is a fundamental disjoint between theoretical measures of information leakages and their applications in practice. This issue is addressed in the second part of this dissertation by proposing a data-driven framework for learning Censored and Fair Universal Representations (CFUR) of data. This framework is formulated as a constrained minimax optimization of the expected -loss where the constraint ensures a measure of the usefulness of the representation. The performance of the CFUR framework with is evaluated on publicly accessible data sets; it is shown that multiple sensitive features can be effectively censored to achieve group fairness via demographic parity while ensuring accuracy for several \textit{a priori} unknown downstream tasks.
Finally, focusing on worst-case measures, novel information-theoretic tools are used to refine the existing relationship between two such measures, -DP and R\'enyi-DP. Applying these tools to the moments accountant framework, one can track the privacy guarantee achieved by adding Gaussian noise to Stochastic Gradient Descent (SGD) algorithms. Relative to state-of-the-art, for the same privacy budget, this method allows about 100 more SGD rounds for training deep learning models.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 202
Bayesian Nonparametric Modeling and Inference for Multiple Object Tracking
No full textabstract: The problem of multiple object tracking seeks to jointly estimate the time-varying cardinality and trajectory of each object. There are numerous challenges that are encountered in tracking multiple objects including a time-varying number of measurements, under varying constraints, and environmental conditions. In this thesis, the proposed statistical methods integrate the use of physical-based models with Bayesian nonparametric methods to address the main challenges in a tracking problem. In particular, Bayesian nonparametric methods are exploited to efficiently and robustly infer object identity and learn time-dependent cardinality; together with Bayesian inference methods, they are also used to associate measurements to objects and estimate the trajectory of objects. These methods differ from the current methods to the core as the existing methods are mainly based on random finite set theory.
The first contribution proposes dependent nonparametric models such as the dependent Dirichlet process and the dependent Pitman-Yor process to capture the inherent time-dependency in the problem at hand. These processes are used as priors for object state distributions to learn dependent information between previous and current time steps. Markov chain Monte Carlo sampling methods exploit the learned information to sample from posterior distributions and update the estimated object parameters.
The second contribution proposes a novel, robust, and fast nonparametric approach based on a diffusion process over infinite random trees to infer information on object cardinality and trajectory. This method follows the hierarchy induced by objects entering and leaving a scene and the time-dependency between unknown object parameters. Markov chain Monte Carlo sampling methods integrate the prior distributions over the infinite random trees with time-dependent diffusion processes to update object states.
The third contribution develops the use of hierarchical models to form a prior for statistically dependent measurements in a single object tracking setup. Dependency among the sensor measurements provides extra information which is incorporated to achieve the optimal tracking performance. The hierarchical Dirichlet process as a prior provides the required flexibility to do inference. Bayesian tracker is integrated with the hierarchical Dirichlet process prior to accurately estimate the object trajectory.
The fourth contribution proposes an approach to model both the multiple dependent objects and multiple dependent measurements. This approach integrates the dependent Dirichlet process modeling over the dependent object with the hierarchical Dirichlet process modeling of the measurements to fully capture the dependency among both object and measurements. Bayesian nonparametric models can successfully associate each measurement to the corresponding object and exploit dependency among them to more accurately infer the trajectory of objects. Markov chain Monte Carlo methods amalgamate the dependent Dirichlet process with the hierarchical Dirichlet process to infer the object identity and object cardinality.
Simulations are exploited to demonstrate the improvement in multiple object tracking performance when compared to approaches that are developed based on random finite set theory.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
