Tyler O'Neal, Staff Editor ECONOMICS

SMU develops efficient methods to simulate how electromagnetic waves interact with devices

 It takes a tremendous amount of supercomputer simulations to create a device like an MRI scanner that can image your brain by detecting electromagnetic waves propagating through tissue. The tricky part is figuring out how electromagnetic waves will react when they come in contact with the materials in the device.

SMU researchers have developed an algorithm that can be used in a wide range of fields - from biology and astronomy to military applications and telecommunications - to create equipment more efficiently and accurately.

Currently, it can take days or months to do simulations. And because of cost, there is a limit to the number of simulations typically done for these devices. SMU math researchers have revealed a way to do a faster algorithm for these simulations with the help of grants from the U.S. Army Research Office and the National Science Foundation.

"We can reduce the simulation time from one month to maybe one hour," said lead researcher Wei Cai, Clements Chair of Applied Mathematics at SMU. "We have made a breakthrough in these algorithms."

"This work will also help create a virtual laboratory for scientists to simulate and explore quantum dot solar cells, which could produce extremely small, efficient and lightweight solar military equipment," said Dr. Joseph Myers, Army Research Office mathematical sciences division chief. CAPTION (From Left) Wei Cai, Dr. Bo Wang and Wenzhong Zhang. CREDIT Photo courtesy of SMU (Southern Methodist University), Hillsman S. Jackson{module INSIDE STORY}

Dr. Bo Wang, a postdoctoral researcher at SMU (Southern Methodist University) and Wenzhong Zhang, a graduate student at the university, also contributed to this research. The study was published today by the SIAM Journal on Scientific Computing.

The algorithm could have significant implications in a number of scientific fields.

"Electromagnetic waves exist as radiation of energies from charges and other quantum processes," Cai explained. 

They include things like radio waves, microwaves, light, and X-rays. Electromagnetic waves are also the reason you can use a mobile phone to talk to someone in another state and why you can watch TV. In short, they're everywhere.

An engineer or mathematician would be able to use the algorithm for a device whose job is to pick out a certain electromagnetic wave. For instance, she or he could potentially use it to design a solar light battery that lasts longer and is smaller than currently exists.

"To design a battery that is small in size, you need to optimize the material so that you can get the maximum conversion rate from the light energy to electricity," Cai said. "An engineer could find that maximum conversion rate by going through simulations faster with this algorithm."

Or the algorithm could help an engineering design a seismic monitor to predict earthquakes by tracking elastic waves in the earth, Cai noted.

"These are all waves, and our method applies for different kinds of waves," he said. "There is a wide range of applications with what we have developed."

Supercomputer simulations map out how materials in a device like semiconductor materials will interact with light, in turn giving a sense of what a particular wave will do when it comes in contact with that device.

The manufacturing of many devices involving light interactions uses a fabrication process by layering material on top of each other in a lab, just like Legos. This is called layered media. Computer simulations then analyze the layered media using mathematical models to see how the material in question is interacting with light.

SMU researchers have found a more efficient and less expensive way to solve Helmholtz and Maxwell's equations - difficult to solve but essential tools to predict the behavior of waves.

The problem of the wave source and material interactions in the layer structure has been a very challenging one for the mathematicians and engineers for the last 30 years.

Professor Weng Cho Chew from Electrical and Computer Engineering at Purdue, a world-leading expert on computational electromagnetics, said the problem "is notoriously difficult."

Commenting on the work of Cai and his team, Chew said, "Their results show excellent convergence to small errors. I hope that their results will be widely adopted."

The new algorithm modifies a mathematical method called the fast multipole method, or FMM, which was considered one of the top 10 algorithms in the 20th century.

To test the algorithm, Cai and the other researchers used SMU's ManeFrame II - which is one of the fastest academic supercomputers in the nation - to run many different simulations.

Swedish radiologist develops AI that improves breast cancer risk prediction

Tyler O'Neal, Staff Editor ECONOMICS

A sophisticated type of artificial intelligence (AI) can outperform existing models at predicting which women are at future risk of breast cancer, according to a study published in the journal Radiology.

Most existing breast cancer screening programs are based on mammography at similar time intervals--typically, annually or every two years--for all women. This "one size fits all" approach is not optimized for cancer detection on an individual level and may hamper the effectiveness of screening programs.

"Risk prediction is an important building block of an individually adapted screening policy," said study lead author Karin Dembrower, M.D., breast radiologist and Ph.D. candidate from the Karolinska Institute in Stockholm, Sweden. "Effective risk prediction can improve attendance and confidence in screening programs."

High breast density, or a greater amount of glandular and connective tissue compared to fat, is considered a risk factor for cancer. While density may be incorporated into risk assessment, current prediction models may fail to fully take advantage of all the rich information found in mammograms. This information has the potential to identify women who would benefit from additional screening with MRI. Patient inclusion flowchart shows selection of women in the training and validation samples used for deep neural network development, as well as in the test sample (current study sample). Exclusions are detailed in the footnote. PACS = picture archiving and communication system.{module INSIDE STORY}

Dr. Dembrower and colleagues developed a risk model that relies on a deep neural network, a type of AI that can extract vast amounts of information from mammographic images. It has inherent advantages over other methods like visual assessment of mammographic density by the radiologist that may not be able to capture all risk-relevant information in the image.

The new model was developed and trained on mammograms from cases diagnosed between 2008 and 2012 and then studied on more than 2,000 women ages 40 to 74 who had undergone mammography in the Karolinska University Hospital system. Of the 2,283 women in the study, 278 were later diagnosed with breast cancer.

The deep neural network showed a higher risk association for breast cancer compared to the best mammographic density model. The false negative rate--the rate at which women who were not categorized as high-risk were later diagnosed with breast cancer--was lower for the deep neural network than for the best mammographic density model.

"The deep neural network overall was better than density-based models," Dr. Dembrower said. "And it did not have the same bias as the density-based model. Its predictive accuracy was not negatively affected by more aggressive cancer subtypes."

The study findings support a future role for AI in breast cancer risk assessment.

"We are not reporting mammographic density currently," Dr. Dembrower said. "In the introduction of individually adapted screening, we use deep learning networks trained to predict cancer rather than taking the indirect route that density offers."

As an additional benefit, the AI approach can continually be improved with exposure to more high-quality data sets.

"Our deep learning experts at the Royal Institute of Technology in Stockholm are working on an update to the model," Dr. Dembrower said. "After that, we aim to test the model clinically next year by offering MRI to the women who stand to benefit the most."

New Russian built system transmits high-speed unrepeated signal over 520 kilometers

Tyler O'Neal, Staff Editor ECONOMICS

Researchers from the Moscow Institute of Physics and Technology in Russia have partnered up with engineers from Corning Inc., U.S., and T8, Russia, and developed a system for high-throughput data transfer over great distances without the need for signal repeating along the way. Systems of this kind could be used to provide internet connection and other communication services in remote communities. The study is reported in an educational journal.

Countries with large underpopulated areas -- such as Russia and Canada -- or those comprised by numerous islands, like Indonesia, face difficulties in providing communication services. Without intermediate electricity-powered repeater stations, the signal gets attenuated and does not arrive at the destination. To make long-haul data transmission cheaper, engineers come up with fiber optic systems that amplify the signal along the link without the need for electrical power sources. Top transmission systems available today enable data transfer at the rate of 100 gigabits per second across 500 kilometers (311 miles).

The authors of the letter successfully transmitted a signal over 520 km (323 mi) at 200 Gbps. This had only been done in research labs before, but those results could not be carried over to actual applications. This time commercial cables developed by Corning were used, making the technology applicable under realistic conditions. To avoid the attenuation of the signal, it was amplified initially upon transmission and then two more times remotely, along the way. The newly developed Volga platform enables high-speed transmission lines spanning over 520 kilometers, or 323 miles.{module INSIDE STORY}

"To amplify the signal in the passive fiber, the stimulated Raman scattering effect and remote optically pumped amplifiers were used. The Raman scattering effect allowed us to use the passive optical fiber as an amplification medium, considerably increasing the signal-to-noise ratio at link output," explained the study's lead author Dimitriy Starykh, a PhD student at MIPT's Phystech School of Radio Engineering and Computer Technology.

The transmission line comprised three sections, each consisting of fiber optic cables of two types connected in series. Remote optically pumped erbium amplifiers (ROPA) were installed at the points of junction between the sections. ROPAs consume optical pump and use this energy to amplify signal. The team optimized the junction positions to increase output signal quality, placing the two ROPAs 122 km (76 mi) from the transmitter and 130 km (81 mi) from the receiver, respectively.

The researchers set the signal symbol rate to slightly short of 57 billion pulses per second; the transmitter allowed the transfer of five bits per symbol, enabling a total bit rate of 284 Gbps. While the system potentially supported data transfer at up to 400 Gbps, the engineers ran it at a reduced speed to increase the transmission distance.

"We are already working on a fiber optic system that would achieve higher transfer rates. While the current speed tops at about 400 Gbps, we aim to reach 600 Gbps per channel with the new system," T8 CEO Vladimir Treshchikov commented. "We achieved signal improvement for rates of 200 Gbps and even 400 Gbps per channel. I think, next year we could set a further transmission distance record."

The results achieved by the researchers can already be employed to provide communication services in sparsely populated areas, such as the Russian island Sakhalin.