Contrasting Climate Ensembles: A Model-based Visualization Approach for Analyzing Extreme Events

AbstractThe use of increasingly sophisticated means to simulate and observe natural phenomena has led to the production of larger and more complex data. As the size and complexity of this data increases, the task of data analysis becomes more challeng- ing. Determining complex relationships among variables requires new algorithm development. Addressing the challenge of handling large data necessitates that algorithm implementations target high performance computing platforms. In this work we present a technique that allows a user to study the interactions among multiple variables in the same spatial extents as the underlying data. The technique is implemented in an existing parallel analysis and visualization framework in order that it be applicable to the largest datasets. The foundation of our approach is to classify data points via inclusion in, or distance to, multivariate representations of relationships among a subset of the variables of a dataset. We abstract the space in which inclusion is calculated and through various space transformations we alleviate the necessity to consider variables’ scales and distributions when making comparisons. We apply this approach to the problem of highlighting variations in climate model ensembles

Exposure to Advertisement Calls of Reproductive Competitors Activates Vocal-Acoustic and Catecholaminergic Neurons in the Plainfin Midshipman Fish, Porichthys notatus

While the neural circuitry and physiology of the auditory system is well studied among vertebrates, far less is known about how the auditory system interacts with other neural substrates to mediate behavioral responses to social acoustic signals. One species that has been the subject of intensive neuroethological investigation with regard to the production and perception of social acoustic signals is the plainfin midshipman fish, Porichthys notatus, in part because acoustic communication is essential to their reproductive behavior. Nesting male midshipman vocally court females by producing a long duration advertisement call. Females localize males by their advertisement call, spawn and deposit all their eggs in their mate’s nest. As multiple courting males establish nests in close proximity to one another, the perception of another male’s call may modulate individual calling behavior in competition for females. We tested the hypothesis that nesting males exposed to advertisement calls of other males would show elevated neural activity in auditory and vocal-acoustic brain centers as well as differential activation of catecholaminergic neurons compared to males exposed only to ambient noise. Experimental brains were then double labeled by immunofluorescence (-ir) for tyrosine hydroxylase (TH), an enzyme necessary for catecholamine synthesis, and cFos, an immediate-early gene product used as a marker for neural activation. Males exposed to other advertisement calls showed a significantly greater percentage of TH-ir cells colocalized with cFos-ir in the noradrenergic locus coeruleus and the dopaminergic periventricular posterior tuberculum, as well as increased numbers of cFos-ir neurons in several levels of the auditory and vocal-acoustic pathway. Increased activation of catecholaminergic neurons may serve to coordinate appropriate behavioral responses to male competitors. Additionally, these results implicate a role for specific catecholaminergic neuronal groups in auditory-driven social behavior in fishes, consistent with a conserved function in social acoustic behavior across vertebrates

Interactive Selection of Multivariate Features in Large Spatiotemporal Data

Selecting meaningful features is central in the analysis of scientific data. Today’s multivariate scientific datasets are often large and complex making it difficult to define general features of interest significant to scientific applications. To address this problem, we propose three general, spatiotemporal metrics to quantify the significant properties of data features–concentration, continuity and co-occurrence, named collectively as CO3. We implemented an interactive visualization system to investigate complex multivariate time-varying data from satellite remote sensing with great spatial resolutions, as well as from real-time continental-scale power grid monitoring with great temporal resolutions. The system integrates CO3 metrics with an elegant multi-space user interaction tool to provide various forms of quantitative user feedback. Through these, the system supports an iterative user-driven analysis process. Our findings demonstrate that the CO3 metrics are useful for simplifying the problem space and revealing potential unknown possibilities of scientific discoveries by assisting users to effectively select significant features and groups of features for visualization and analysis. Users can then comprehend the problem better and design future studies using newly discovered scientific hypotheses

Saccular-specific hair cell addition correlates with reproductive state-dependent changes in the auditory saccular sensitivity of a vocal fish

The plainfin midshipman fish, Porichthys notatus, is a seasonal breeding teleost fish for which vocal-acoustic communication is essential for its reproductive success. Female midshipman use the saccule as the primary end organ for hearing to detect and locate "singing" males that produce multiharmonic advertisement calls during the summer breeding season. Previous work has shown that female auditory sensitivity changes seasonally with reproductive state; summer reproductive females become better suited than winter nonreproductive females to detect and encode the dominant higher harmonic components in the male's advertisement call, which are potentially critical for mate selection and localization. Here, we test the hypothesis that these seasonal changes in female auditory sensitivity are concurrent with seasonal increases in saccular hair cell receptors. We show that there is increased hair cell density in reproductive females and that this increase is not dependent on body size since similar changes in hair cell density were not found in the other inner ear end organs. We also observed an increase in the number of small, potentially immature saccular hair bundles in reproductive females. The seasonal increase in saccular hair cell density and smaller hair bundles in reproductive females was paralleled by a dramatic increase in the magnitude of the evoked saccular potentials and a corresponding decrease in the auditory thresholds recorded from the saccule. This demonstration of correlated seasonal plasticity of hair cell addition and auditory sensitivity may in part facilitate the adaptive auditory plasticity of this species to enhance mate detection and localization during breeding

A Nonintrusive, Adaptable and User-Friendly In Situ Visualization Framework

Reducing the amount of data stored by simulations will be of utmost importance for the next generation of large-scale computing. Accordingly, there is active research to shift analysis and visualization tasks to run in situ, i.e. closer to the simulation via the sharing of some resources. This is beneficial as it can avoid the necessity of storing large amounts of data for post-processing. In this paper, we focus on the specific case of in situ visualization where analysis codes are collocated with the simulation's code and run on the same resources. It is important for an in situ technique to require minimum modifications to existing codes, be adaptable and have a low impact on both run times and resource usage. We accomplish this through the Damaris/Viz framework, which provides in situ visualization support to the Damaris I/O middleware. The use of Damaris as a bridge to existing visualization packages allows us to (1) reduce code moditications to a minimum for existing simulations, (2) gather capabilities of several visualization tools to offer a unified data management interface, (3) use dedicated cores to hide the run time impact of in situ visualization and (4) efficiently use memory through a shared-memory-based communication model. Experiments are conducted on Blue Waters and Grid5000 to visualize the CM1 atmospheric simulation and the Nek5000 CFD solver

Damaris/Viz: a Nonintrusive, Adaptable and User-Friendly In Situ Visualization Framework

International audienceReducing the amount of data stored by simulations will be of utmost importance for the next generation of large-scale computing. Accordingly, there is active research to shift analysis and visualization tasks to run in situ, that is, closer to the simulation via the sharing of some resources. This is beneficial as it can avoid the necessity of storing large amounts of data for post-processing. In this paper, we focus on the specific case of in situ visualization where analysis codes are collocated with the simulation's code and run on the same resources. It is important for an in situ technique to require minimum modifications to existing codes, be adaptable, and have a low impact on both run times and resource usage. We accomplish this through the Damaris/Viz framework, which provides in situ visualization support to the Damaris I/O middleware. The use of Damaris as a bridge to existing visualization packages allows us to (1) reduce code moditication to a minimum for existing simulations, (2) gather capabilities of several visualization tools to offer a unified data management interface, (3) use dedicated cores to hide the run time impact of in situ visualization and (4) efficiently use memory through a shared-memory-based communication model. Experiments are conducted on Blue Waters and Grid'5000 to visualize the CM1 atmospheric simulation and the Nek5000 CFD solver