Math formula leads researchers to source of pollution

ScienceDaily (June 25, 2012) — The leaking of environmentally damaging pollutants into our waters and atmosphere could soon be counteracted by a simple mathematical algorithm, according to researchers.

Presenting their research June 26, in IOP Publishing's journal Inverse Problems, the researchers, from Université de Technologie de Compiègne, believe their work could aid efforts to avoid environmental catastrophes by identifying the exact location where pollutants have been leaked as early as possible.

In the event of an oil spill across a region of the sea, researchers could collect samples of pollutants along certain sections of the body of water and then feed this information into their algorithm.

The algorithm is then able to determine two things: the rate at which the pollutant entered the body of water and where the pollutant came from.

This isn't the first time that mathematical algorithms have been used to solve this problem; however, this new approach is unique in that it could allow researchers to 'track' the source of a pollutant if it is moving or changing in strength.

Co-author of the study, Mr Mike Andrle, said: "In the unfortunate event of a pollutant spill, either by purposeful introduction into our waters or atmosphere, or by purely accidental fate, collaboration with scientists and engineers and application of this work may save precious moments to avert more environmental damage."

The algorithm itself is modelled on the general transport of a pollutant and takes three phenomena into account: diffusion, convection and reaction.

Diffusion is where the pollutant flows naturally from high concentrations to low concentrations and convection is where other factors cause the pollutant to displace, such as a current in the sea. A pollutant may also react with other materials in the water or settle on a seabed or lake floor: this is classified as 'reaction'.

The researchers add that other terms could also be added into the algorithm to account for the properties of different pollutants; for example, oil may not dissolve entirely in water and may form droplets, in which case the buoyancy and settling would need to be accounted for.

Their theoretical results have already shown that the result is unique; that is, the solution found is the only possible one given the observable data. The results were also shown to be very robust, which is extremely important in practice where such measurements often have relatively large errors associated with them.

Mr Andrle continued: "Growing up on Lake Erie, I heard of the previous shape it had been in where industry resulted in much of the lake being declared dead at one time. Though I was not alive to see it at its worst, I did witness how lots of legislation and new policies had turned its fate around.

"I saw a chance to contribute to research that may help mitigate causes of similar future events. We hope that the results of this work are substantially circulated so that those involved in pollution spill localisation and clean-up are aware of this solution."

The paper's second author, Professor Abdellatif El-Badia, said: "Inverse problems are very important in science, engineering and bioengineering. It is very interesting that we've been able to apply this topic to the very big problem of pollution."

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The above story is reprinted from materials provided by Institute of Physics, via EurekAlert!, a service of AAAS.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

M Andrle and A El Badia. Identification of multiple moving pollution sources in surface waters or atmospheric media with boundary observations. Inverse Problems, 2012 DOI: 10.1088/0266-5611/28/7/075009

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Math formula leads researchers to source of pollution

ScienceDaily (June 25, 2012) — The leaking of environmentally damaging pollutants into our waters and atmosphere could soon be counteracted by a simple mathematical algorithm, according to researchers.

Presenting their research June 26, in IOP Publishing's journal Inverse Problems, the researchers, from Université de Technologie de Compiègne, believe their work could aid efforts to avoid environmental catastrophes by identifying the exact location where pollutants have been leaked as early as possible.

In the event of an oil spill across a region of the sea, researchers could collect samples of pollutants along certain sections of the body of water and then feed this information into their algorithm.

The algorithm is then able to determine two things: the rate at which the pollutant entered the body of water and where the pollutant came from.

This isn't the first time that mathematical algorithms have been used to solve this problem; however, this new approach is unique in that it could allow researchers to 'track' the source of a pollutant if it is moving or changing in strength.

Co-author of the study, Mr Mike Andrle, said: "In the unfortunate event of a pollutant spill, either by purposeful introduction into our waters or atmosphere, or by purely accidental fate, collaboration with scientists and engineers and application of this work may save precious moments to avert more environmental damage."

The algorithm itself is modelled on the general transport of a pollutant and takes three phenomena into account: diffusion, convection and reaction.

Diffusion is where the pollutant flows naturally from high concentrations to low concentrations and convection is where other factors cause the pollutant to displace, such as a current in the sea. A pollutant may also react with other materials in the water or settle on a seabed or lake floor: this is classified as 'reaction'.

The researchers add that other terms could also be added into the algorithm to account for the properties of different pollutants; for example, oil may not dissolve entirely in water and may form droplets, in which case the buoyancy and settling would need to be accounted for.

Their theoretical results have already shown that the result is unique; that is, the solution found is the only possible one given the observable data. The results were also shown to be very robust, which is extremely important in practice where such measurements often have relatively large errors associated with them.

Mr Andrle continued: "Growing up on Lake Erie, I heard of the previous shape it had been in where industry resulted in much of the lake being declared dead at one time. Though I was not alive to see it at its worst, I did witness how lots of legislation and new policies had turned its fate around.

"I saw a chance to contribute to research that may help mitigate causes of similar future events. We hope that the results of this work are substantially circulated so that those involved in pollution spill localisation and clean-up are aware of this solution."

The paper's second author, Professor Abdellatif El-Badia, said: "Inverse problems are very important in science, engineering and bioengineering. It is very interesting that we've been able to apply this topic to the very big problem of pollution."

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Story Source:

The above story is reprinted from materials provided by Institute of Physics, via EurekAlert!, a service of AAAS.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

M Andrle and A El Badia. Identification of multiple moving pollution sources in surface waters or atmospheric media with boundary observations. Inverse Problems, 2012 DOI: 10.1088/0266-5611/28/7/075009

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Computer scientist leads the way to the next revolution in artificial intelligence

ScienceDaily (Apr. 2, 2012) — As computer scientists this year celebrate the 100th anniversary of the birth of the mathematical genius Alan Turing, who set out the basis for digital computing in the 1930s to anticipate the electronic age, they still quest after a machine as adaptable and intelligent as the human brain.

Now, computer scientist Hava Siegelmann of the University of Massachusetts Amherst, an expert in neural networks, has taken Turing's work to its next logical step. She is translating her 1993 discovery of what she has dubbed "Super-Turing" computation into an adaptable computational system that learns and evolves, using input from the environment in a way much more like our brains do than classic Turing-type computers. She and her post-doctoral research colleague Jeremie Cabessa report on the advance in the current issue of Neural Computation.

"This model is inspired by the brain," she says. "It is a mathematical formulation of the brain's neural networks with their adaptive abilities." The authors show that when the model is installed in an environment offering constant sensory stimuli like the real world, and when all stimulus-response pairs are considered over the machine's lifetime, the Super Turing model yields an exponentially greater repertoire of behaviors than the classical computer or Turing model. They demonstrate that the Super-Turing model is superior for human-like tasks and learning.

"Each time a Super-Turing machine gets input it literally becomes a different machine," Siegelmann says. "You don't want this for your PC. They are fine and fast calculators and we need them to do that. But if you want a robot to accompany a blind person to the grocery store, you'd like one that can navigate in a dynamic environment. If you want a machine to interact successfully with a human partner, you'd like one that can adapt to idiosyncratic speech, recognize facial patterns and allow interactions between partners to evolve just like we do. That's what this model can offer."

Classical computers work sequentially and can only operate in the very orchestrated, specific environments for which they were programmed. They can look intelligent if they've been told what to expect and how to respond, Siegelmann says. But they can't take in new information or use it to improve problem-solving, provide richer alternatives or perform other higher-intelligence tasks.

In 1948, Turing himself predicted another kind of computation that would mimic life itself, but he died without developing his concept of a machine that could use what he called "adaptive inference." In 1993, Siegelmann, then at Rutgers, showed independently in her doctoral thesis that a very different kind of computation, vastly different from the "calculating computer" model and more like Turing's prediction of life-like intelligence, was possible. She published her findings in Science and in a book shortly after.

"I was young enough to be curious, wanting to understand why the Turing model looked really strong," she recalls. "I tried to prove the conjecture that neural networks are very weak and instead found that some of the early work was faulty. I was surprised to find out via mathematical analysis that the neural models had some capabilities that surpass the Turing model. So I re-read Turing and found that he believed there would be an adaptive model that was stronger based on continuous calculations."

Each step in Siegelmann's model starts with a new Turing machine that computes once and then adapts. The size of the set of natural numbers is represented by the notation aleph-zero, ?0, representing also the number of different infinite calculations possible by classical Turing machines in a real-world environment on continuously arriving inputs. By contrast, Siegelmann's most recent analysis demonstrates that Super-Turing computation has 2?0, possible behaviors. "If the Turing machine had 300 behaviors, the Super-Turing would have 2300, more than the number of atoms in the observable universe," she explains.

The new Super-Turing machine will not only be flexible and adaptable but economical. This means that when presented with a visual problem, for example, it will act more like our human brains and choose salient features in the environment on which to focus, rather than using its power to visually sample the entire scene as a camera does. This economy of effort, using only as much attention as needed, is another hallmark of high artificial intelligence, Siegelmann says.

"If a Turing machine is like a train on a fixed track, a Super-Turing machine is like an airplane. It can haul a heavy load, but also move in endless directions and vary its destination as needed. The Super-Turing framework allows a stimulus to actually change the computer at each computational step, behaving in a way much closer to that of the constantly adapting and evolving brain," she adds.

Siegelmann and two colleagues recently were notified that they will receive a grant to make the first ever Super-Turing computer, based on Analog Recurrent Neural Networks. The device is expected to introduce a level of intelligence not seen before in artificial computation.

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The above story is reprinted from materials provided by University of Massachusetts at Amherst.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

Jérémie Cabessa, Hava T. Siegelmann. The Computational Power of Interactive Recurrent Neural Networks. Neural Computation, 2012; 24 (4): 996 DOI: 10.1162/NECO_a_00263

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Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here