Distributed non-negative RESCAL with automatic model selection for exascale data
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97 MATHEMATICS AND COMPUTING↗
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Community detection robustness of graph neural networks
Graph neural networks (GNNs) are increasingly widely used for community detection in attributed networks. They combine structural topology with node attributes through message passing and pooling. However, their robustness or lack thereof with respect to different perturbations and targeted attacks in conjunction with community detection tasks is not well understood. To shed light on latent mechanisms behind GNN sensitivity on community detection tasks, we conduct a systematic computational evaluation of six widely adopted GNN architectures graph convolutional network, graph attention network, graph sample and aggregate (GraphSAGE), differentiable pooling (DiffPool), minimum cut pooling (MinCUT), and deep modularity networks (DMoN). The analysis covers three perturbation categories: node attribute manipulations, edge topology distortions, and adversarial attacks. We use element-centric similarity as the evaluation metric on synthetic benchmarks and real-world citation networks. Our findings indicate that supervised GNNs tend to achieve higher baseline accuracy, while unsupervised methods, particularly DMoN, maintain stronger resilience under targeted and adversarial perturbations. Furthermore, robustness appears to be strongly influenced by community strength, with well-defined communities reducing performance loss. Across all models, node attribute perturbations associated with targeted edge deletions and shifts in attribute distributions tend to cause the largest degradation in community recovery. These findings highlight important trade-offs between accuracy and robustness in GNN-based community detection and offer insights into selecting architectures resilient to noise and adversarial attacks.
Goel, Jaidev [Virginia Polytechnic Inst. and State↗
Effects of radiation on immune system and latent virus reactivation
The purpose of this NCRP commentary is to provide the current state of knowledge on the effects of ionizing radiation on the immune system and on latent herpes virus reactivation to the scientific community and government agencies. Its purpose is to better understand radiation-induced latent virus reactivation, which is possibly an underestimated consequence of ionizing radiation exposure. This activity should involve the radiation research community (academia, industry and regulatory agencies) and government agencies (NASA, DOD, CDC).
Plante, Ianik↗
A Latent Heat Retrieval and its Effects on the Intensity and Structure Change of Hurricane Guillermo (1997). Part I: The Algorithm and Observations.
The release of latent heat in clouds is an essential part of the formation and I intensification o~hurricanes. The community knows very little about the intensity and structure of latent heating due largely to inadequate observations. In this paper, a new method for retrieving the latent heating field in hurricanes from airborne Dopple~ radar is presented and fields from rapidly intensifying Hurricane Guillermo (1997) are shown.
Guimond, Stephen R.↗
Latent Dirichlet Allocation modeling of environmental microbiomes
Interactions between stressed organisms and their microbiome environments may provide new routes for understanding and controlling biological systems. However, microbiomes are a form of high-dimensional data, with thousands of taxa present in any given sample, which makes untangling the interaction between an organism and its microbial environment a challenge. Here we apply Latent Dirichlet Allocation (LDA), a technique for language modeling, which decomposes the microbial communities into a set of topics (non-mutually-exclusive sub-communities) that compactly represent the distribution of full communities. LDA provides a lens into the microbiome at broad and fine-grained taxonomic levels, which we show on two datasets. In the first dataset, from the literature, we show how LDA topics succinctly recapitulate many results from a previous study on diseased coral species. We then apply LDA to a new dataset of maize soil microbiomes under drought, and find a large number of significant associations between the microbiome topics and plant traits as well as associations between the microbiome and the experimental factors, e.g. watering level. This yields new information on the plant-microbial interactions in maize and shows that LDA technique is useful for studying the coupling between microbiomes and stressed organisms.
59 BASIC BIOLOGICAL SCIENCES↗
Using TRMM Data To Understand Interannual Variations In the Tropical Water Balance
A significant element of the science rationale for TRMM centered on assembling rainfall data needed to validate climate models-- climatological estimates of precipitation, its spatial and temporal variability, and vertical modes of latent heat release. Since the launch of TRMM, a great interest in the science community has emerged for quantifying interannual variability (IAV) of precipitation and its relationship to sea-surface temperature (SST) changes. The fact that TRMM has sampled one strong warm/ cold ENSO couplet, together with the prospect for a mission lifetime approaching ten years, has bolstered this interest in these longer time scales. Variability on a regional basis as well as for the tropics as a whole is of concern. Our analysis of TRMM results so far has shown surprising lack of concordance between various algorithms in quantifying IAV of precipitation. The first objective of this talk is to quantify the sensitivity of tropical precipitation to changes in SSTs. We analyze performance of the 3A11, 3A25, and 3B31 algorithms and investigate their relationship to scattering-- based algorithms constructed from SSM/I and TRMM 85 kHz data. The physical basis for the differences (and similarities) in depicting tropical oceanic and land rainfall will be discussed. We argue that scattering-based estimates of variability constitute a useful upper bound for precipitation variations. These results lead to the second question addressed in this talk-- How do TRMM precipitation / SST sensitivities compare to estimates of oceanic evaporation and what are the implications of these uncertainties in determining interannual changes in large-scale moisture transport? We summarize results of an analysis performed using COADS data supplemented by SSM/I estimates of near-surface variables to assess evaporation sensitivity to SST. The response of near 5 W sq m/K is compared to various TRMM precipitation sensitivities. Implied moisture convergence over the tropics and its sensitivity to errors of these algorithms is discussed.
Robertson, Franklin R.↗
Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part II: Evaluation of Estimates Using Independent Data
Rainfall rate estimates from spaceborne microwave radiometers are generally accepted as reliable by a majority of the atmospheric science community. One of the Tropical Rainfall Measuring Mission (TRMM) facility rain-rate algorithms is based upon passive microwave observations from the TRMM Microwave Imager (TMI). In Part I of this series, improvements of the TMI algorithm that are required to introduce latent heating as an additional algorithm product are described. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, 0.5 deg. -resolution estimates of surface rain rate over ocean from the improved TMI algorithm are well correlated with independent radar estimates (r approx. 0.88 over the Tropics), but bias reduction is the most significant improvement over earlier algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly 2.5 -resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data is limited, TMI-estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain-rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with (a) additional contextual information brought to the estimation problem and/or (b) physically consistent and representative databases supporting the algorithm. A model of the random error in instantaneous 0.5 deg. -resolution rain-rate estimates appears to be consistent with the levels of error determined from TMI comparisons with collocated radar. Error model modifications for nonraining situations will be required, however. Sampling error represents only a portion of the total error in monthly 2.5 -resolution TMI estimates; the remaining error is attributed to random and systematic algorithm errors arising from the physical inconsistency and/or nonrepresentativeness of cloud-resolving-model-simulated profiles that support the algorithm.
Yang, Song↗
Protecting the Health of Astronauts: Enhancing Occupational Health Monitoring and Surveillance for Former NASA Astronauts to Understand Long-Term Outcomes of Spaceflight-Related Exposures
The astronaut community is unique, and may be disproportionately exposed to occupational hazards not commonly seen in other communities. The extent to which the demands of the astronaut occupation and exposure to spaceflight-related hazards affect the health of the astronaut population over the life course is not completely known. A better understanding of the individual, population, and mission impacts of astronaut occupational exposures is critical to providing clinical care, targeting occupational surveillance efforts, and planning for future space exploration. The ability to characterize the risk of latent health conditions is a significant component of this understanding. Provision of health screening services to active and former astronauts ensures individual, mission, and community health and safety. Currently, the NASA-Johnson Space Center (JSC) Flight Medicine Clinic (FMC) provides extensive medical monitoring to active astronauts throughout their careers. Upon retirement, astronauts may voluntarily return to the JSC FMC for an annual preventive exam. However, current retiree monitoring includes only selected screening tests, representing an opportunity for augmentation. The potential long-term health effects of spaceflight demand an expanded framework of testing for former astronauts. The need is two-fold: screening tests widely recommended for other aging populations are necessary to rule out conditions resulting from the natural aging process (e.g., colonoscopy, mammography); and expanded monitoring will increase NASA's ability to better characterize conditions resulting from astronaut occupational exposures. To meet this need, NASA has begun an extensive exploration of the overall approach, cost, and policy implications of e an Astronaut Occupational Health program to include expanded medical monitoring of former NASA astronauts. Increasing the breadth of monitoring services will ultimately enrich the existing evidence base of occupational health risks to astronauts. Such an expansion would therefore improve the understanding of the health of the astronaut population as a whole, and the ability to identify, mitigate, and manage such risks in preparation for deep space exploration missions.
Rossi, Meredith↗
Safeguarding the Health of the NASA Astronaut Community: the Need for Expanded Medical Monitoring for Former NASA Astronauts Under the Astronaut Occupational Health Program
The astronaut community is unique, and may be disproportionately exposed to occupational hazards not commonly seen in other communities. The extent to which the demands of the astronaut occupation and exposure to spaceflight-related hazards affect the health of the astronaut population over the life course is not completely known. Provision of health screening services to active and former astronauts ensures individual, mission, and community health and safety. Currently, the NASA Johnson Space Center (JSC) Flight Medicine Clinic (FMC) provides extensive medical monitoring to active astronauts throughout their careers. Upon retirement, astronauts may voluntarily return to the JSC FMC for an annual preventive exam. However, current retiree monitoring includes only selected screening tests, representing an opportunity for augmentation. The potential latent health effects of spaceflight demand an expanded framework of testing for former astronauts. The need is two-fold: screening tests widely recommended for other aging communities are necessary for astronauts to rule out conditions resulting from the natural aging process (e.g., colonoscopy, mammography), as opposed to conditions resulting directly from the astronaut occupation; and increased breadth of monitoring services will improve the understanding of occupational health risks and longitudinal health of the astronaut community, past, present, and future. To meet this need, NASA has begun an extensive exploration of the overall approach, cost, and policy implications of expanding existing medical monitoring under the Astronaut Occupational Health program for former NASA astronauts.
Rossi, Meredith↗
A numerical model of nonmigrating diurnal tides between the surface and 65 km
Observations of surface pressure and middle atmosphere temperatures and winds indicate that a substantial nonmigrating component is present in the diurnal tide. The nonmigrating tides, which propagate with a zonal phase speed that is different from the earth's rotation, are attributed to the diurnal heating of geographically fixed sources. In this study we utilize a classical tidal model to examine the propagation characteristics of diurnal tides. The global fields of tropospheric sensible, radiative, and latent heating used to drive the model are supplied from summer and winter diurnal climatologies of the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM2). A novel aspect of this study is the focus on the relative importance of the nonmigrating components. The classical model successfully reproduces many observed features of the low-latitude diurnal surface pressure tides. In the middle atmosphere, the simulated migrating (or sun-synchronous) tide shows qualitative agreement with November-March Limb Infrared Monitor of the Stratosphere (LIMS) observations. Tropospheric solar heating is clearly the dominant driving force for the migrating tide, with secondary contributions from boundary-layer sensible heating and tropospheric latent heat release. The leading modes of the zonal mean tide are also driven chiefly by tropospheric solar heating. The higher-order modes of the zonal mean and eastward propagating tides may be attributed to the joint effects of tropospheric solar heating, sensible heating, and latent heat release. The LIMS and other data reveal features that cannot be explained or examined within the context of the classical model used in the present study. These include upward phase propagation, vertical attenuation, and temporal variations in the migrating diurnal tide.
Lieberman, Ruth S.↗
Wildfire Segmentation From Remotely Sensed Data Using Quantum-Compatible Conditional Vector Quantized-Variational Autoencoders
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.
quantum machine learning↗
Observing ocean-atmosphere exchanges with space-borne sensors, appendix C
At present, space-borne sensors cannot give accurate measurements of net longwave radiation and sensible heat flux at ocean surface. The variability of ocean surface net insolation was successfully estimated from observations using geostationary satellites. Global coverages of the low frequency variation of major atmosphere-ocean exchanges, momentum, latent heat, and insolation are within reach. Data from research and defense satellites should be made available in useful form to the user community. High quality in-situ measurements are required to calibrate space-borne sensors and numerical models are needed to integrate satellite and in-situ data as well as interpolate both in space and time. Techniques that can resolve boundary layer parameters should be developed.
Liu, W. T.↗
Regional program for acquisition of medical experiments
A U.S. company was contracted to cover different regions of the country. A moderately detailed description of the highlights of the company activities along with some conclusions and recommendations are reported. In summary, the regional program effectively: (1) informed segments of the medical community of research opportunities; (2) validated formats for regional workshops; (3) assisted potential investigators with follow-up consultations and proposal preparations; and (4) identified a latent intersect requiring continual dialog at the scientist/engineer interface for successful cultivation and integration.
Vannordstrand, P. C.↗
Improving the Representation of Snow Crystal Properties Within a Single-Moment Microphysics Scheme
As computational resources continue their expansion, weather forecast models are transitioning to the use of parameterizations that predict the evolution of hydrometeors and their microphysical processes, rather than estimating the bulk effects of clouds and precipitation that occur on a sub-grid scale. These parameterizations are referred to as single-moment, bulk water microphysics schemes, as they predict the total water mass among hydrometeors in a limited number of classes. Although the development of single moment microphysics schemes have often been driven by the need to predict the structure of convective storms, they may also provide value in predicting accumulations of snowfall. Predicting the accumulation of snowfall presents unique challenges to forecasters and microphysics schemes. In cases where surface temperatures are near freezing, accumulated depth often depends upon the snowfall rate and the ability to overcome an initial warm layer. Precipitation efficiency relates to the dominant ice crystal habit, as dendrites and plates have relatively large surface areas for the accretion of cloud water and ice, but are only favored within a narrow range of ice supersaturation and temperature. Forecast models and their parameterizations must accurately represent the characteristics of snow crystal populations, such as their size distribution, bulk density and fall speed. These properties relate to the vertical distribution of ice within simulated clouds, the temperature profile through latent heat release, and the eventual precipitation rate measured at the surface. The NASA Goddard, single-moment microphysics scheme is available to the operational forecast community as an option within the Weather Research and Forecasting (WRF) model. The NASA Goddard scheme predicts the occurrence of up to six classes of water mass: vapor, cloud ice, cloud water, rain, snow and either graupel or hail.
Molthan, Andrew L.↗
A Graphical Model for Fusing Diverse Microbiome Data
This paper develops a Bayesian graphical model for fusing disparate types of count data. The motivating application is the study of bacterial communities from diverse high-dimensional features, in this case, transcripts, collected from different treatments. In such datasets, there are no explicit correspondences between the communities and each corresponds to different factors, making data fusion challenging. We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data. This latent variable model jointly characterizes the observed data through a common multivariate Gaussian latent space that parameterizes the set of multinomial probabilities of the transcriptome counts. The covariance matrix of the latent variables induces a covariance matrix of co-dependencies between all the transcripts, effectively fusing multiple data sources. We present a computationally scalable variational Expectation-Maximization (EM) algorithm for inferring the latent variables and the parameters of the model. Here, the inferred latent variables provide a common dimensionality reduction for visualizing the data and the inferred parameters provide a predictive posterior distribution. In addition to simulation studies that demonstrate the variational EM procedure, we apply our model to a bacterial microbiome dataset.
59 BASIC BIOLOGICAL SCIENCES↗
Diurnal heating and cloudiness in the NCAR Community Climate Model (CCM2)
In this paper, the authors assess the suitability of the heating fileds in the latest version of the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM2) for modeling the thermal forcing of atmospheric tides. Accordingly, diurnal variations of the surface pressure, outgoing longwave radiation (OLR), cloudiness, and precipitation are examined in the CCM2. The fields of radiative, sensible, and latent heating are similarly analyzed. These results are subjectively compared with available data. Equatorial diurnal surface pressure tides are fairly well simulated by CCM2. The model successfully reproduces the semidiurnal surface pressure tides; however, this may result in part from reflection of wave energy at the upper boundary. The CCM2 large-scale diurnal OLR is generally consistent with observations. The moist-convective scheme in the model is able to reproduce the diurnally varying cloudiness and precipitation patterns associated with land-sea contrasts; however, the amplitudes of CCM2 diurnal continental convective cloudiness are weaker than observations. The CCM2 boundary-layer sensible heating is consistent with a very limited set of observations, and with estimates obtained from simple models of diffusive heating. Although the CCM2 tropospheric solar radiative heating is similar in magnitude to previous estimates, there are substantial differences in the vertical structures. A definitive assessment of the validity of the CCM2 diurnal cycle is precluded by the lack of detailed observations and the limitations of our CCM2 sample. Nevertheless, the authors conclude that the global-scale components of the CCM2 diurnal heating are useful proxies for the true diurnal forcing of the tides.
Lieberman, Ruth S.↗
Simultaneous global and local clustering in multiplex networks with covariate information
Understanding both global and layer-specific group structures is useful for uncovering complex patterns in networks with multiple interaction types. In this work, we introduce a new model, the hierarchical multiplex stochastic blockmodel, which simultaneously detects communities within individual layers of a multiplex network while inferring a global node clustering across the layers. A stochastic blockmodel is assumed in each layer, with probabilities of layer-level group memberships determined by a node’s global group assignment. Our model uses a Bayesian framework, employing a probit stick-breaking process to construct node-specific mixing proportions over a set of shared Griffiths–Engen–McCloseky distributions. These proportions determine layer-level community assignment, allowing for an unknown and varying number of groups across layers, while incorporating nodal covariate information to inform the global clustering. We propose a scalable variational inference procedure with parallelisable updates for application to large networks. Extensive simulation studies demonstrate our model’s ability to accurately recover both global and layer-level clusters in complicated settings, and applications to real data showcase the model’s effectiveness in uncovering interesting latent network structure.
community detection↗
Sensitivity of Terrestrial Water and Energy Budgets to CO2-Physiological Forcing: An Investigation Using an Offline Land Model
Increasing concentrations of atmospheric carbon dioxide (CO2) influence climate by suppressing canopy transpiration in addition to its well-known greenhouse gas effect. The decrease in plant transpiration is due to changes in plant physiology (reduced opening of plant stomata). Here, we quantify such changes in water flux for various levels of CO2 concentrations using the National Center for Atmospheric Research s (NCAR) Community Land Model. We find that photosynthesis saturates after 800 ppmv (parts per million, by volume) in this model. However, unlike photosynthesis, canopy transpiration continues to decline at about 5.1% per 100 ppmv increase in CO2 levels. We also find that the associated reduction in latent heat flux is primarily compensated by increased sensible heat flux. The continued decline in canopy transpiration and subsequent increase in sensible heat flux at elevated CO2 levels implies that incremental warming associated with the physiological effect of CO2 will not abate at higher CO2 concentrations, indicating important consequences for the global water and carbon cycles from anthropogenic CO2 emissions. Keywords: CO2-physiological effect, CO2-fertilization, canopy transpiration, water cycle, runoff, climate change 1.
Gopalakrishnan, Ranjith↗