Computing Community Consortium Blog

The goal of the Computing Community Consortium (CCC) is to catalyze the computing research community to debate longer range, more audacious research challenges; to build consensus around research visions; to evolve the most promising visions toward clearly defined initiatives; and to work with the funding organizations to move challenges and visions toward funding initiatives. The purpose of this blog is to provide a more immediate, online mechanism for dissemination of visioning concepts and community discussion/debate about them.

Highlights: Predicting Hurricanes, Visualizing Research, and Perceiving Leaning

December 12th, 2011 / in Research News / by Erwin Gianchandani

A series of computing research advances making the news in the past week:

A New Forecasting Algorithm to Predict Hurricane Intensity and Wind Speed

The devastating impact of hurricanes can be seen from this image showing the aftermath of Hurricane Katrina in the Lower Ninth Ward, in New Orleans, Louisiana. The Prediction Intensity Interval model for Hurricanes (PIIH) can ultimately help improve hurricane readiness and reduce the risk to property and human life. [Image courtesy Kenzie Schott, Southern Methodist University, via NSF.]Scientists at Southern Methodist University’s (SMU) Intelligent Data Analysis Lab (IDA) [have] developed a new forecasting algorithm called the Prediction Intensity Interval model for Hurricanes (PIIH) to help better predict hurricane intensity.


PIIH also predicts the potential ranges, from high to low, of maximum hurricane wind speeds, specifying the likelihood of wind speeds in varying ranges.


“Accurately predicting intensity means vastly improving hurricane readiness and reducing the risk to property and human life,” said Michael Hahsler, visiting assistant professor for Computer Science and Engineering at SMU. “With more accurate predicting of intensity, governments and the communities they serve will be able to make better decisions on the extent of an evacuation and when to evacuate. This will result in real dollar savings as well as saving lives.”


The PIIH algorithm is based on an aggregate hurricane model that uses previous data, including current maximum intensity, potential for increase in intensity, time of year, various temperature measurements, direction of storm movement and wind shear — the difference in wind speed and direction over a relatively short distance in the atmosphere. PIIH applies this model of past hurricane behavior to predict the intensity of current hurricanes up to five days from any given time point.


“When a future intensity is to be predicted for a current storm, similar states in the life cycle model are found,” said Margaret Dunham, Computer Science and Engineering professor at SMU. “A forecast is created by constructing a weighted average of forecasts from similar storm states found in previous storms. Confidence bands are constructed based upon observing the frequency distributions of intensity values found in previous storms. Based on these and the current intensity value, confidence intervals for future predictions are created.”


By analyzing 2011 storms, through Hurricane Nate, which struck in September 2011, researchers observed that just over 96 percent of the PIIH observations fell within the 95 percent confidence band, which is a very high probability that the PIIH prediction confidence bands were accurate.

To learn more, check out the full press release from NSF.

A New Method for Visualizing Research

A large-screen window layout of the overall interface of ASE [image courtesy Cody Dunne, Robert Gove, Ben Shneiderman, Bonnie Dorr and Judith Klavans, University of Maryland-College Park, via NSF].Action Science Explorer (ASE) allows users to simultaneously search through thousands of academic papers, using a visualization method that determines how papers are connected, for instance, by topic, date, authors, etc. The goal is to use these connections to identify emerging scientific trends and advances.


“We are creating an early warning system for scientific breakthroughs,” said Ben Shneiderman, a professor at the University of Maryland and founding director of the Human-Computer Interaction Lab [there].


“Such a system would dramatically improve the capability of academic researchers, government program managers and industry analysts to understand emerging scientific topics so as to recognize breakthroughs, controversies and centers of activity,” said Shneiderman. “This would enable appropriate allocation of funds, encourage new collaborations among groups that unknowingly were working on similar topics and accelerate research progress.”


ASE is not itself a product, but rather “a scientific research study that shows some potent new features that could be added to bibliographic systems to support more powerful functions,” said Shneiderman.


This project is unique and provides “powerful network visualization, integrated with search techniques, statistical methods and text analytics to provide automatic summarization of closely related document clusters,” he said.


Shneiderman explained that ASE would be especially helpful to those who explore emerging topics, such as computer scientists who want to understand quantum computing or environmental researchers who want to explore new visualization techniques for encouraging energy conservation. ASE extends beyond science papers to include topics in any knowledge domain. The ideas of ASE can be built into any language, beyond English.

More here.

“Decoded Neurofeedback”

 In the future, a person may be able to watch a computer screen and have his or her brain patterns modified to improve physical or mental performance. Researchers say an innovative learning method that uses decoded functional magnetic resonance imaging could modify brain activities to help people recuperate from an accident or disease, learn a new language or even fly a plane. [Image courtesy Nicolle Rager Fuller, National Science Foundation.]New research published [last Friday] in the journal Science suggests it may be possible to use brain technology to learn to play a piano, reduce mental stress or hit a curve ball with little or no conscious effort. It’s the kind of thing seen in Hollywood’s “Matrix” franchise.


Experiments conducted at Boston University and ATR Computational Neuroscience Laboratories in Kyoto, Japan, recently demonstrated that through a person’s visual cortex, researchers could use decoded functional magnetic resonance imaging (fMRI) to induce brain activity patterns to match a previously known target state and thereby improve performance on visual tasks.


Think of a person watching a computer screen and having his or her brain patterns modified to match those of a high-performing athlete or modified to recuperate from an accident or disease. Though preliminary, researchers say such possibilities may exist in the future.


“Adult early visual areas are sufficiently plastic to cause visual perceptual learning,” said lead author and BU neuroscientist Takeo Watanabe of the part of the brain analyzed in the study.


Neuroscientists have found that pictures gradually build up inside a person’s brain, appearing first as lines, edges, shapes, colors and motion in early visual areas. The brain then fills in greater detail to make a red ball appear as a red ball, for example.


Researchers studied the early visual areas for their ability to cause improvements in visual performance and learning.


“Some previous research confirmed a correlation between improving visual performance and changes in early visual areas, while other researchers found correlations in higher visual and decision areas,” said Watanabe, director of BU’s Visual Sciences Laboratory. “However, none of these studies directly addressed the question of whether early visual areas are sufficiently plastic to cause visual perceptual learning.” Until now.


Boston University post-doctoral fellow Kazuhisa Shibata designed and implemented a method using decoded fMRI neurofeedback to induce a particular activation pattern in targeted early visual areas that corresponded to a pattern evoked by a specific visual feature in a brain region of interest. The researchers then tested whether repetitions of the activation pattern caused visual performance improvement on that visual feature.


The result, say researchers, is a novel learning approach sufficient to cause long-lasting improvement in tasks that require visual performance.


What’s more, the approached worked even when test subjects were not aware of what they were learning.

Learn all about it here.

More Highlights Every Week

Be sure to check out the CCC’s Computing Research Highlight of the Week, updated every Thursday, for more advances. And if you have an interesting research result you would like featured here, submit a Highlight today!

(Contributed by Erwin Gianchandani, CCC Director)

Highlights: Predicting Hurricanes, Visualizing Research, and Perceiving Leaning

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