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This presentation will be used to develop a level of partnership that is not within the current NASA program for N+3 engine operations.
Wildfires represent a critical environmental hazard with multifaceted implications for ecosystems, communities, and public health [1]. The escalating frequency and intensity of wildfires globally have intensified the urgency for robust segmentation methodologies to facilitate effective mitigation, response, and recovery strategies [2]. Accurate wildfire segmentation is pivotal for delineating fire boundaries, assessing progression patterns, and prioritizing resource allocation during emergency scenarios. Furthermore, precise segmentation enables stakeholders, including policymakers, environmental scientists, and emergency responders, to formulate evidence-based strategies, thereby minimizing socio-economic disruptions and ecological degradation. Consequently, advancing wildfire segmentation techniques through innovative technological interventions remains a paramount research imperative. Although foundational in wildfire segmentation, traditional deterministic models exhibit inherent limitations that compromise their efficacy in dynamic and uncertain environments. These models often operate on rigid algorithms prioritizing deterministic classifications, thereby overlooking the inherent complexities and uncertainties associated with wildfire behavior and satellite data variability. Such deterministic frameworks tend to produce oversimplified representations that fail to capture the intricate nuances of evolving fire dynamics, spatial heterogeneity, and environmental interactions [1]. Consequently, the deterministic approach’s propensity for uncertainty collapsing [1, 3] hampers the accuracy, reliability, and applicability of segmentation outcomes in real-world scenarios. Contrastingly, stochastic models offer a more nuanced and adaptable framework for wildfire segmentation. By integrating probabilistic elements into the modeling paradigm, stochastic approaches, particularly probabilistic approaches such as variational auto encoders (VAEs) [4], facilitate comprehensive uncertainty assessment, enabling researchers to quantify and incorporate uncertainties into segmentation outcomes effectively. This probabilistic nature empowers stochastic models to encapsulate variability, account for data inconsistencies, and adapt to evolving environmental conditions, enhancing segmentation accuracy, reliability, and robustness. Embracing stochastic methodologies thus catalyzes advancements in wildfire science by fostering a more holistic, adaptive, and resilient segmentation framework. Despite VAEs demonstrating significant promise in various applications, they come with inherent limitations that have garnered attention within the machine learning community. One of the primary drawbacks lies in their reliance on static priors, which essentially assume a fixed distribution for latent variables, thereby limiting the model’s flexibility to capture complex data structures effectively [5]. This static nature leads to suboptimal representations, especially when dealing with complex and high-dimensional data. Additionally, VAEs often struggle with generating sharp and realistic samples, a phenomenon commonly referred to as mode collapse [5, 7, 6]. Furthermore, the optimization process in VAEs, which involves balancing the reconstruction loss and the regularization term, can sometimes be challenging to fine-tune [7]. In recent efforts to address these shortcomings, alternative approaches like Vector Quantized Variational Auto encoders(VQ-VAEs) [7], address the challenges by incorporating discrete latent variables and leveraging techniques that enhance the quality and diversity of generated samples while maintaining efficient training dynamics. VQ-VAEs propose a dynamic prior distribution generation mechanism that diverges from the static priors commonly associated with traditional VAEs. This dynamic approach allows for more adaptive and context-aware latent variable representations, thereby potentially capturing complex data structures more effectively. Unlike autoregressive prior models such as PixelCNN, which, despite their ability to model dependencies across data dimensions, suffer from significant computational inefficiencies and lack flexibility in handling diverse datasets. In our work, we propose to use a generative quantum-compatible approach to help alleviate the shortcomings of autoregressive prior model in VQ-VAEs. Restricted Boltzmann Machines (RBMs) are a viable alternative prior model that can learn prior distributions in a faster and more flexible manner. In this research endeavor, we meticulously curate a state-of-the-art dataset leveraging satellite MODIS data in conjunction with VIIRS fire masks, derived from Fire Radiative Power (FRP), thereby encapsulating diverse wildfire scenarios and environmental contexts. We developed a conditional VQ-VAE architecture with the RBM prior model that is trained in a supervised manner for segmenting wildfire masks. This innovative approach synergistically harnesses deep learning capabilities, enabling the generation of segmentation maps characterized by heightened precision, granularity, and contextual relevance. Furthermore, replacing the autoregressive prior learning method proposed by the original VQ-VAE with a prior density approximation via quantum-compatible RBM facilitates expedited inference processes, augments flexibility in prior sampling, optimizes computational efficiency and establishes a groundbreaking benchmark in wildfire segmentation methodologies.
Current therapy for massive venous air embolism (VAE) includes the use of the left lateral recumbent (LLR) position. This recommendation is based on animal studies, conducted 50 years ago, which looked primarily at survival. Little is known, however, about the concomitant hemodynamic response after VAE in various body positions. The purpose of this study was to investigate the hemodynamic and cardiovascular changes in various body positions after VAE. Twenty-two mechanically ventilated supine mongrel dogs received a venous air infusion of 2.5 mL/kg at a rate of 5 mL/s. One minute after the infusion, 100% oxygen ventilation was commenced and the body position of the dogs was changed to either the LLR (n = 6), the LLR with the head 10 deg down (LLR-10 deg; n = 6) or the right lateral recumbent (RLR; n = 5) position. Five dogs were maintained in the supine position (SUP; n = 5). One dog died in every group except in the SUP group, where all the dogs recovered. There were no significant differences among the various body positions in terms of heart rate, mean arterial pressure, pulmonary artery pressure, central venous pressure, left ventricular end-diastolic pressure, or cardiac output. The acute hemodynamic changes occurring during the first 5-15 min after VAE recovered to 80% of control within 60 min. Our data suggest that body repositioning does not influence the cardiovascular response to VAE. Specifically, our data do not support the recommendation of repositioning into the LLR position for the treatment of VAE.
Current therapy for massive venous air embolism (VAE) includes the use of the left lateral recumbent (LLR) position. This recommendation is based on animal studies, conducted 50 yr ago, which looked primarily at survival. Little is known, however, about the concomitant hemodynamic response after VAE in various body positions. The purpose of this study was to investigate the hemodynamic and cardiovascular changes in various body positions after VAE. Twenty-two mechanically ventilated supine mongrel dogs received a venous air infusion of 2.5 mL/kg at a rate of 5 mL/s. One minute after the infusion, 100% oxygen ventilation was commenced and the body position of the dogs was changed to either the LLR (n = 6), the LLR with the head 10 degrees down (LLR-10 degrees; n = 6) or the right lateral recumbent (RLR; n = 5) position. Five dogs were maintained in the supine position (SUP; n = 5). One dog died in every group except in the SUP group, where all the dogs recovered. There were no significant differences among the various body positions in terms of heart rate, mean arterial pressure, pulmonary artery pressure, central venous pressure, left ventricular end-diastolic pressure, or cardiac output. The acute hemodynamic changes occurring during the first 5-15 min after VAE recovered to 80% of control within 60 min. Our data suggest that body repositioning does not influence the cardiovascular response to VAE. Specifically, our data do not support the recommendation of repositioning into the LLR position for the treatment of VAE.
Two experiments are presented assessing the contributions of the rate of change of disparity (CD) and interocular velocity difference (IOVD) cues to stereomotion speed perception. Using a two-interval forced-choice paradigm, the perceived speed of directly approaching and receding stereomotion and of monocular lateral motion in random dot stereogram (RDS) targets was measured. Prior adaptation using dysjunctively moving random dot stimuli induced a velocity aftereffect (VAE). The degree of interocular correlation in the adapting images was manipulated to assess the effectiveness of each cue. While correlated adaptation involved a conventional RDS stimulus, containing both IOVD and CD cues, uncorrelated adaptation featured an independent dot array in each monocular half-image, and hence lacked a coherent disparity signal. Adaptation produced a larger VAE for stereomotion than for monocular lateral motion, implying effects at neural sites beyond that of binocular combination. For motion passing through the horopter, correlated and uncorrelated adaptation stimuli produced equivalent stereomotion VAEs. The possibility that these results were due to the adaptation of a CD mechanism through random matches in the uncorrelated stimulus was discounted in a control experiment. Here both simultaneous and sequential adaptation of left and right eyes produced similar stereomotion VAEs. Motion at uncrossed disparities was also affected by both correlated and uncorrelated adaptation stimuli, but showed a significantly greater VAE in response to the former. These results show that (1) there are two separate, specialised mechanisms for encoding stereomotion: one through IOVD, the other through CD; (2) the IOVD cue dominates the perception of stereomotion speed for stimuli passing through the horopter; and (3) at a disparity pedestal both the IOVD and the CD cues have a significant influence.
Engineering constraints that may be encountered when implementing interactive virtual acoustic displays are examined In particular, system parameters such as the update rate and total system latency are defined and the impact they may have on perception is discussed. For example, examination of the head motions that listeners used to aid localization in a previous study suggests that some head motions may be as fast as about 400 degrees/sec for short time periods. Analysis of latencies in virtual acoustic environments (VAEs) suggests that: (1) commonly-specified parameters such as the audio update rate determine only the "best-case" latency possible in a VAE, (2) total system latency and individual latencies of system components, including head-trackers, are frequently not measured by VAE developers, and (3) typical system latencies may result in under-sampling of relative listener-source motion of 400 degrees/sec as well as positional "jitter" in the simulated source. To clearly specify the dynamic performance of a particular VAE, users and developers need to make measurements of average system latency, update rate, and their variability using standardized rendering scenarios. a parameters such as the minimum audible movement angle can then be used as target guidelines to assess whether a given system meets perceptual requirements.
BACKGROUND: Current therapy for massive venous air embolism (VAE) may include the use of the left lateral recumbent (LLR) position, although its effectiveness has been questioned. This study used transesophageal echocardiography to evaluate the effect of body repositioning on intracardiac air and acute cardiac dimension changes. METHODS: Eighteen anesthetized dogs in the supine position received a venous air injection of 2.5 ml/kg at a rate of 5 ml/ s. After 1 min the dogs were repositioned into either the LLR, LLR 10 degrees head down (LLR-10 degrees), right lateral recumbence, or remained in the supine position. RESULTS: Repositioning after VAE resulted in relocation of intracardiac air to nondependent areas of the right heart. Peak right ventricular (RV) diameter increase and mean arterial pressure decrease were greater in the repositioned animals compared with those in the supine position (P < 0.05). Right ventricular diameter and mean arterial pressure showed an inverse correlation (r = 0.81). Peak left atrial diameter decrease was greater in the LLR and LLR-10 degrees positions compared with the supine position (P < 0.05). Repositioning did not influence peak pulmonary artery pressure increase, and no correlation was found between RV diameter and pulmonary artery pressure. All animals showed electrocardiogram and echocardiographic changes reconcilable with myocardial ischemia. CONCLUSIONS: In dogs, body repositioning after VAE provided no benefit in hemodynamic performance or cardiac dimension changes, although relocation of intracardiac air was demonstrated. Right ventricular air did not appear to result in significant RV outflow obstruction, as pulmonary artery pressure increased uniformly in all groups and was not influenced by the relocation of intracardiac air. The combination of increased RV afterload and arterial hypotension, possibly with subsequent RV ischemia rather than RV outflow obstruction by an airlock appeared to be the primary mechanism for cardiac dysfunction after VAE.
Observational data obtained from the Visible Airglow Experiment (VAE) on the Atmospheric Explorer D satellite is used to deduce the production efficiency of the excited 2P state of the O(+) ion in the electron impact ionization of O under auroral conditions. Measurements of the airglow intensity in the O(+)(2P-2D) transition band 7320-7330 A by the upward looking VAE photometer were compared with the intensity predicted on the basis of electron spectra measured at heights from 211 to 235 km within the emission. A production efficiency of 18% is obtained, which is close to theoretical predictions and supports the theoretical value of 40% for the production of the 2D state.
Visible spacecraft glow was first observed on the Atmospheric Explorer spacecraft (AE-E) and studied in some detail with the Visible Airglow Experiment (VAE). The AE-E was a spin-stabilized spacecraft without thrusters at an altitude of 140 to 280 km. The VAE contained six visible wavelength photometers that measured a glow spectrum which: (1) rose steeply in the red, (2) decreased with a cos cubed PH1 dependence from pointing into the ram direction of the spacecraft orbital motion, and (3) decreased in intensity with increasing altitude with the same dependence as the measured atomic oxygen number atmospheric density (O) and not with the measured molecular nitrogen density (N sub 2). It is proposed that the glow is produced by chemical reactions on the spacecraft surface as it sweeps through the atmospheric O, with roughly 5-8 eV per O atom available for excitation from the orbital motion of the spacecraft. This glow may in principal be produced by any of a number of species, including molecular band emission from OH, NO, and NO2. An attempt is made to scale the observed glow to the Hubble space telescope.
Contributions of the Visible Airglow Experiment (VAE) to the understanding of various airglow and auroral processes are reviewed. The impact of instrumental design and operation on the observations is discussed, and emphasis is placed on the relationship between observations and inversion for optical measurements of light emissions from a diffuse medium. VAE data are used to explain the physical mechanisms responsible for the production and destruction of excited species including O(+)(1P), O(1D), O(1S), N(2D), and Mg(+)2P1/2.
Over the last decade, the incidence of wildfires has surged, causing widespread destruction globally. To better comprehend and manage these incidents, remote sensing and aerial missions have been implemented in recent efforts. However, this has resulted in an exponential rise in the amount of remote sensing data utilization, leading to a need for intelligent automation of data extraction in wildfire studies. Machine learning provides an accurate automated approach for detecting these natural anomalies and facilitates decision-makers to take prompt actions. To make insightful decisions in wildfire management, it is imperative to move beyond simple detection and explore the potential of probabilistic generative machine learning for creating "what-if" scenarios for various wildfire conditions. Such models offer improved representation of the stochastic nature of wildfire events. However, the optimization of these models can be computationally expensive, especially when using classical computers. Quantum computers have recently emerged as a promising solution to reduce the computational cost of training such models and improve their performance. In this study, we aim to utilize quantum-compatible machine learning techniques to implement our probabilistic generative approach. To that end, we propose a supervised probabilistic variational model consisting of a U-NET-based image-to-image component along with encoder and decoder networks which work as a variational autoencoder (VAE) component. Additionally, we explore the type of latent distribution type in the VAE component and implement different means for modeling the prior distribution. We further investigate the quantum-compatible versions of the model compared to the classical counterpart and benchmark potential benefits of quantum compatibility over the classical model.
Atmospheric optical emission measurements by the Visible Airglow Experiment (VAE) on board the Atmosphere Explorer (AE-C, D and E) satellites have been analyzed and found to be contaminated at low altitudes. The contamination maximizes in the forward direction along the spacecraft velocity and is sensitive to the composition and density of the ambient atmosphere. Analysis at two different wavelengths suggests that the contamination is likely to have a diffuse band spectrum which is brighter toward the red. Some unknown processes which involve satellite surface materials and the incoming ambient particles are believed to be responsible for the contamination. A simulation model is presented here to account for the observed angular dependence.
Doppler technique was used to measure the pulmonary vascular pressure gradients in dogs that received venous air emboli (VAE) at the rate of 0.35 ml/kg per min, at which the spillover of bubbles into the systemic arteries occur. It was found that, in 60 percent of dogs, venous bubbles spilled over into the arterial circulation at the pulmonary vascular pressure gradient of about 34.7 mm Hg. When the pulmonary vascular pressure gradient was raised to about 52 mm Hg, the spillover of venous bubbles occurred 100 percent of time. It is concluded that venous bubbles can cross the lungs of anesthetized dogs when the driving pressures are sufficient to overcome the normal filtering function.
The Visible Airglow Experiment (VAE) was designed to provide detailed profiles of the distribution of excited states of atoms and molecules in the upper atmosphere. The studies supported during the funding period (1983 - 1989) have made significant contributions in the area of thermospheric aeronomy, and the progress during the first four years of this period has been reviewed by Hays et al. (1988). The investigations carried out have resulted in more than 20 publications, and these are summarized.
The aim of this research project was to test and truth some recently developed methods for recovering thermospheric oxygen atom densities and thermospheric temperatures from ground-based observations of the 7320 A O(+)((sup 2)D - (sup 2)P) twilight air glow emission. The research plan was to use twilight observations made by the Visible Airglow Experiment (VAE) on the Atmosphere Explorer 'E' satellite as proxy ground based twilight observations. These observations were to be processed using the twilight inversion procedures, and the recovered oxygen atom densities and thermospheric temperatures were then to be examined to see how they compared with the densities and temperatures that were measured by the Open Source Mass Spectrometer and the Neutral Atmosphere Temperature Experiment on the satellite.
Recent progress in quantum algorithms and hardware is indicator of the potential importance of quantum computing in the next future. However, finding suitable application areas remains an active area of research. Quantum machine learning [1] is touted as a potential approach to demonstrate quantum advantage within both the gate-model [2,3] and the adiabatic [4,5] schemes. For instance, the Quantum-assisted Variational Autoencoder (QVAE) [6] has been proposed as a quantum enhancement to the discrete VAE [7]. We extend on previous work and study the real-world applicability of a QVAE, specifically, for similarity search in large-scale high dimensional datasets. While similarity search algorithms are available for low dimensional datasets, scaling to billion-scale datasets with thousands of dimensions is non-trivial. We show how the latent-space representation of a QVAE can be used to construct a space-efficient search index. We back up our claims by experimental results which show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. Further, we show real-world speedups compared to linear search and demonstrate memory efficient scaling to large-scale datasets.
Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes. For instance, the QVAE has been proposed as a quantum enhancement to the discrete VAE. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity search in large-scale high-dimensional datasets. While exact and fast similarity search algorithms are available for low dimensional datasets, scaling to high-dimensional data is non-trivial. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. Our experiments show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the MODIS dataset. Further, we find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points.
Earth-observing satellites carrying multispectral sensors are widely used to monitor the physical and biological states of the atmosphere, land, and oceans. These satellites have different vantage points above the Earth and different spectral imaging bands resulting in inconsistent imagery from one to another. This presents challenges in building downstream applications. What if we could generate synthetic bands for existing satellites from the union of all domains? We tackle the problem of generating synthetic spectral imagery for multispectral sensors as an unsupervised image-to-image translation problem modeled with a variational autoencoder (VAE) and generative adversarial network (GAN) architecture. Our approach introduces a novel shared spectral reconstruction loss to constrain the high-dimensional feature space of multispectral images. Simulated experiments performed by dropping one or more spectral bands show that cross-domain reconstruction outperforms measurements obtained from a second vantage point. Our proposed approach enables the synchronization of multispectral data and provides a basis for more homogeneous remote sensing datasets.