June 2009 Newsletter

Visualization of a lifted autoigniting hydrogen/air jet flame with hydroperxy radical (ignition marker) and hydroxyl radical (flame marker). The simulation was performed on the Cray XT4 Jaguar supercomputer at the Oak Ridge National laboratory by Dr. Chen and her colleagues at the Sandia National Laboratories.

Scientists across the board, from chemists to astrophysicists, today have the tools to explore and model phenomenon at a mind-blowing level of fidelity and resolution. But the data sets they generate not only blow minds, they also blow the gaskets of the computers and the traditional schemas that scientists try to examine them on.  

In Professor Kwan-Liu Ma’s UC Davis VIDI (Visualization and Interface Design Innovation) lab, the objective is to take data sets that can be on the peta or tera scale and turn them into explorable, workable, and visualizable units.

“In the past, most data was viewed as a two-dimensional cross-section or at best as so-called isosurfaces,” says Ma. “But by employing our visualization techniques we are able to let researchers see the full extent of their data at the highest possible resolution and in both three-dimensional space and the temporal domain. So scientists can begin to visualize things they just couldn’t see in the past.”

Computer scientists have been helping other types of researchers visualize their work since the early days of computer graphics. What is new, and the subject of Ma’s Department of Energy-supported research, is working with vast oceans of data. In some cases, the data is so detailed that visualization techniques like Ma’s are needed before hypotheses can be validated. In other cases, the visualizations allow researchers to connect dots they might not even suspect existed.

As part of a five-year SciDAC grant, Ma’s lab has worked with Dr. Jaqueline Chen’s research team at Sandia National Labs in Livermore, CA, on depicting the properties of turbulent combustion with detailed chemistry. At first, Ma simply helped them to visualize their data in three dimensions over time. But then Dr. Chen said that they wanted to see multiple variables, and how these variables related to each other over time, in the same visualization.  So Ma and his colleagues had to find ways to superimpose several elements in one image so that they were distinct, but so that their relationships, as they changed over time, were clear.

“In the past, they did most of the data analysis as a post process. But now they are able to interact with their data, to explore, because we’ve developed techniques that have allowed them to go into these different domains of the data. That has become very powerful.”

“We even developed a user interface so they could move between different spaces. They can shift from the temporal space to the spatial domain, and then look at the interaction between different variables at different levels.”

Professor Ma at UC Davis helps researchers exploit the largest available computers to process and visualize their data.

For example, there were delicate properties of turbulent combustion that were hidden in the data due to the multi-scale nature of turbulence flow, Ma says. His visualizations were able to draw some of those features out, allowing, for instance, researchers to view small turbulence eddies that are very close to a known feature surface of interest. Until now, those features have usually been eclipsed by other, more salient phenomena.  Ma’s visualization software allows the Sandia scientists to zoom in on the feature surfaces and move closer and further away from the surfaces, examining these different features at different scales, including very detailed and subtle micro-eddies near the surface.

Powerful as computer visualizations are, there is always some uncertainty introduced when looking at data indirectly, says Ma. Depicting that level of uncertainty, making it a part of the visualization itself, is another subject of his team’s research.

Ma wants users of his tools to be able to “visualize the process of visualization itself,” he says. “If we can convey what has been done to the data to generate the image they’re working with, -- not just the information loss but also the important mapping done to the data to get the image-- then the user can have much greater control.”

Ma and his colleagues at VIDI work with scientists from a wide range of disciplines: climate science, chemistry, astrophysics and practical sciences like groundwater engineering and combustion studies.  For Ma and his colleagues and students, the first phase of any project is a total-immersion crash course in the language and basic principles of the science itself. Only then can the task of translating data generated by the science into manipulatable, computer-generated images begin.

On the one hand, Ma helps some groups to exploit the largest available computers to process and visualize their data. He has created algorithms that work on massively parallel computers and cluster computers, for groups such as the Stanford Linear Accelerator Center, Argonne National Laboratory, and Sandia National Laboratories that have large computing power needs. On the other hand, he also goes the other direction, taking huge data sets and making them visualizable on smaller computers. He recently worked with the Harvard-Smithsonian Center for Astrophysics, taking vast amounts of observational data viewable only on computers with hundreds of gigabytes of processing power, and scaling it down--without losing important information, of course—so that it can be examined and explored on a single-gigabyte desktop.

Ma is clearly driven by a desire to empower scientists to better do their most important work, like finding ways to model climate change and to develop technologies that will mitigate global warming, but he also seems driven by the pure aesthetics of his field.

As John Keats wrote, “Truth is beauty,” and, when they reveal the subtle secrets of their subjects, Ma’s true visualizations can be stunningly beautiful as well.

By Gordy Slack 

Biologists probably know more about the habits and natural history of African lions on the Serengeti Plain than they do about the mountain lions in Northern California. There is so much unknown about mountain lions, in fact, that people cannot even seem to settle on a good common name for them. Puma concolor, is their Latin binomial, but they regularly are called everything from puma and catamount, to panther.

These cats work hard to preserve their mystery. Their camouflage and the dense shrub- and tree-covered habitat they prefer conspire to keep them out of sight, explaining another of their common names, “ghost cat.” A group of biologists at UC Santa Cruz is working to fill in key parts of the puzzle about the life history, physiology, energetics, and population dynamics of the hundred or so mountain lions that live in the Santa Cruz Mountains. To do this, the researchers are employing sensor and communications technology, partially supported by CITRIS, that they have built into radio collars that they are fitting onto some of the lions’ necks.

The collars, still under development, will not only track the whereabouts of the lions, but will also gather data about what they are doing as they move.  As more of the Santa Cruz Mountains are converted to human uses, it will be increasingly important to understand the needs and ways of mountain lions, says ecologist Chris Wilmers, an assistant professor of environmental studies at UC Santa Cruz. Which routes are essential corridors connecting the large tracts of habitat, for example, needed for mating, hunting, and raising cubs?

The cats, which radically declined in population until the early 1990s, when the state prohibited hunting them, have been on the rise since and now number between 60 and 100 in the Santa Cruz mountains, says Wilmers. But to protect them and to minimize potentially hazardous encounters with humans, it will be important to know where they are going and what kinds of places are most important for their hunting and courtship. “We need to learn what kinds of development they can withstand and what types maybe they cannot,” he says.

Matt Rutishauser, formerly a graduate student in engineering at UCSC, is the collar designer for Wilmers. Rutishauser is now an engineer at the sensor network manufacturer Intelesense, and remains a contractor on the puma project. He is experimenting with several approaches to the new collars. In addition to GPS, which uses satellite signals to chart location, the collars also include accelerometers, which measure the force with which the collar is changing its velocity. Rutishauser is also adding a sensor that can give the orientation of the animal to the Earth’s magnetic poles at any given time; that sensor will help analyze the real position of the animal, regardless of the accelerometer’s position on the neck of the animal. Finally, he is implementing software and hardware changes that will conserve power in the collars and prolong the life of their batteries.

Professor Chris Wilmers is using innovative radio collars to track pumas in the Santa Cruz mountains .

Different behaviors can be matched with different acceleration patterns. Prolonged loping, for instance, may indicate the cat is stalking prey. Quick bursts of running followed by high G-forces and rapid orientation changes from impacts with prey and the subsequent struggle, may indicate a kill. (Researchers can later investigate kill sites to see how much and what kind of prey is eaten.)

"Their movement during a strike is so different from other movements that it will leave a unique signature in the data," says Terri Williams, an animal behaviorist and UCSC professor of ecology and evolutionary biology. She is now studying captive animals and examining the accelerometer “signatures” of different behaviors that will help them recognize those activities in the data they collect. Knowing what kinds of prey they take and how often they eat, says Williams, will allow the team to calculate the calorie consumption of specific cats and their energy requirements from day to day, information that will help land managers assess the cats’ vulnerabilities to habitat fragmentation and other kinds of disturbance.

The collars generate a lot of data, says Wilmers. Collecting that data in a timely way, without having to recapture the animals and download it, is tricky. The team has tried several methods. The most innovative, perhaps, involves making an ad hoc network of the collars. Whenever the animals come within a few hundred yards of each other each collar will transmit its data to the others. Then, whenever any one of the animals comes within range of a stationary base station, all the data that collar holds, both from the animal wearing it and all others that have come within range of it, will be downloaded for collection by the research team.

This method could be useful for studying a whole range of animals, says Rutishauser, especially social ones like zebras, wild dogs, or coyotes, which regularly congregate at, say, a watering hole.

Ironically, the team is unsure how effective this networking approach will ultimately be for the mountain lions, because of their solitary ways, says Wilmers.  Unless they are in heat, females stay far away from males, who will sometimes kill and eat their kittens.  If they do not get within range, the data will not transfer.  The males, however, do frequent centralized locations, presumably, Wilmers says, so that females that are in heat will know where to find them.

Another technique for transmitting data is to equip each collar with a modified cell phone programmed to call in and text GPS data “home” to the lab.

“It is good to get a text message from your mountain lion every day,” says Wilmers. “But first we must figure out whether the lion being tracking has good cell phone access. And there are large parts of the Santa Cruz mountains where the cell phone service is totally absent or pretty spotty,” he says.

The group is also experimenting with Argos, a satellite tracking system that resembles GPS except, whereas in GPS the receiver in the collar “looks” for the known position of satellites in order to read its own position, the Argos satellites seek the collars, record their positions, and send that data to a central location where it can be downloaded by researchers. 

Then there is the old-fashioned way of collecting data, which uses UHF transmissions and requires researchers getting within half a mile of an animal and downloading data with an antenna attached to a receiver. In the rough mountainous puma habitat around Santa Cruz, that method can be a lot of frustrating and backbreaking work.

Today only five cats are wearing prototype collars.  In October 2009, when the weather cools enough to catch cats again, the team plans to tag a total of 20 pumas that will, one way or another, soon be sending valuable new kinds of data back home for analysis.