Winning aerodynamic design by a team of students at the University of Leicester – Team Mansell - in the inaugural race of the UniFi Motorsport international competition. Colour scale shows the surface distribution of the coefficient of static pressure over the car body. Image credit: Dr Aldo Rona, University of Leicester

A team from the University of Leicester has taken pride of place in an international Formula 1 aerodynamics competition.

Team Mansell, supervised by Dr. Aldo Rona, senior lecturer in the Department of Engineering, and Professor  Ivor Annetts, Royal Academy of Engineering Visiting Professor in Motorsport Aerodynamics, won the inaugural race of the UniFi Motorsport 2015/16 season.

UniFi Motorsport was formed in 2014 to provide a range of support services to Engineering students seeking their first role in Motorsport Aerodynamic Design.

The University of Leicester student team fended off competitors from other universities and international teams from Italy and South Africa to snatch first place with their redesigned Formula 1 car body.

This consisted of a bespoke front wing (new cascade, turning vanes), a new barge board and sidepod vane, and rear wing and diffuser packages. The team used SolidWorks to design the new surfaces and Computational Fluid Dynamics (CFD) from TotalSim Ltd to evaluate and refine their aerodynamic performance.

The aerodynamic designs from all teams were tested on a simulated medium downforce circuit, analogous to Race 5 of the Formula 1 calendar, on Bramble, and Team Mansell won this sprint race with the lap time of 1’32.68’’.

Dr Rona, Senior Lecturer in the Thermofluids Research Group at Leicester, said: “I was delighted to see Team Mansell topping the scoreboard at the inaugural race. This achievement is testimony to their hard work and to the mentoring that they received, by having a RAEng Visiting Professor in Motorsport Aerodynamics on board.”

Team Mansell consists of fourth year MEng students at the University of Leicester: Amar Patel (team leader), Harnish Rathod, Mmoloki A Machacha, Sahil Patel, and Haroon Habib.  They were delighted by the good news and were congratulated by the Head of College, Professor Martin Barstow.

Amar Patel said: “I am very pleased with the teams’ performance, having used skills obtained on our respective courses to develop the aerodynamic package and achieve the right set-up for the inaugural race. We feel privileged to have obtained this opportunity and will use it to further develop our interest into the field. Currently, our mind is focused on a new set-up to enhance our design and achieve better results than that what was previously achieved. As team leader, I want to thank all my team members for their continued contribution and am looking forward to the final race results”

The UniFi Motorsport activity at the University of Leicester is supported by a Visiting Professor award from the Royal Academy of Engineering. 

CAPTION From left: John Robinson, Senior Cyber Security Advisor at the Department of Homeland Security; Bo Luo, Associate Professor, EECS; Fengjun Li, Assistant Professor, EECS; Victor Frost, Dan F. Servey Distinguished Professor and Chair; Dr. Joan Ferrini-Mundy, Assistant Director, Directorate of Education and Human Resources, National Science Foundation (NSF); Clif Triplett, Senior Cyber and Information Technology Advisor, U.S. Office of Personnel Management.

A new $4.7 million, five-year grant from the National Science Foundation will enable the University of Kansas School of Engineering to educate cyberdefense experts dedicated to public service, making America stronger in an era of rising cyberattacks.

The initiative, called CyberCorps: New Scholarship for Service Program at the University of Kansas -- Jayhawk SFS," will support dozens of undergraduate, master's and doctoral students, who following graduation commit in turn to work at government cybersecurity jobs safeguarding critical infrastructure.

Agencies where Jayhawk SFS graduates might serve include the Central Intelligence Agency, National Security Agency, Department of Defense and the National Laboratories, as well as state and local departments.

"We'll produce students to protect the cyberspace of the United States after they graduate," said the program's leader, Bo Luo, associate professor of electrical engineering and computer science at KU. "While they're here, they'll be conducting cybersecurity research, and this helps protect the cyberspace of good people anywhere in the world."

Luo said the Jayhawk SFS program addresses a nationwide shortfall in highly expert cybersecurity personnel.

"Cybersecurity is extremely critical," Luo said. "This grant is a very small portion of that shortage, supporting 36 students. That's far from what is needed, but this is part of a national program -- and KU is proud to be part of it. All together, CyberCorps will provide thousands of graduates with cybersecurity expertise. Ours is one of the leading efforts."

Recently, Luo traveled to Washington, D.C., to attend a ceremony marking the beginning of the Jayhawk SFS grant with co-primary investigators at KU, professors Fengjun Li and Victor Frost. Perry Alexander, AT&T Distinguished Professor of Electrical Engineering & Computer Science and director of KU's Information and Telecommunication Technology Center, is the third co-PI of the new grant.

Luo said the first Jayhawk SFS students would begin training this fall.

"We'll recruit students who are really interested in cybersecurity and who plan to work in cybersecurity after they graduate," he said. "They have to be U.S. citizens or permanent residents. We'll be working with many different programs to reach out to underrepresented communities, like KU's Center for Educational Opportunity Programs and McNair Scholars Program, which assists first-generation and underrepresented minority undergraduates to prepare for doctoral study."

Additionally, Luo said the Jayhawk SFS program would recruit transitioning soldiers and veterans through partnerships with the 1st Infantry Division at Fort Riley, Kansas National Guard, U.S. Army Cyber Command and the Graduate Military Program at KU. Michael Denning, director of the Office of Graduate Military Programs at KU, will act as a key liaison to soldiers and veterans.

Indeed, the Jayhawk SFS program builds on years of partnership with national security entities, including longstanding KU designation by the National Security Agency and Department of Homeland Security as a National Center for Academic Excellence in Cyber Defense -- a stepping stone to earning the new grant. Moreover, the work embodies the Harnessing Information-Multiplying Knowledge initiative of Bold Aspirations, the university's strategic plan.

"We have a comprehensive, student-centered cybersecurity program and we're engaged in advanced cybersecurity research," Luo said. He pointed out KU's research strengths in information security and privacy, communication and network systems security, and high-assurance software development and verification.

Moreover, a successful information-security student club, dubbed the "JayHackers," could serve as a recruitment source for Jayhawk SFS program. The student club already has won competitive cyberdefense competitions.

"We hosted won the CANSec Invitational cybersecurity competition in 2014, and then in 2015 attended the national cybersecurity competition," Luo said. "Students were trained in information security there, got industrial sponsors, bought servers, installed virtual networks on those servers and planned their own cyber defense. This is really valuable hands-on experience."

Ultimately, Luo said the Jayhawk SFS could help to safeguard a world beyond just the computers and networks people have traditionally associated with the term "cyberspace."

"The future of information technology is the future of technology itself," he said. "All types of technology now are closely related to IT. For example, 20 years ago a car was a car and the territory of mechanical engineers. But because cars are getting smarter, they're vulnerable to cyberattack. So there's a transition from mechanical territory to electrical engineering territory that's transforming our lives. Now, we have smart refrigerators and smart TVs. In a few years, everything we do will be connected to the Internet, and everything connected to the Internet is subject to cyberattacks."

Cybersecurity software developed at Pacific Northwest National Laboratory learns about a company to better protect it. Called CHAMPION, the software can reason like an analyst to determine if network activity is suspicious. It then issues an alert in near-real-time. PNNL Shawn Hampton (left), Champion Technology Company's Ryan Hohimer (right) and their teams received an FLC Award for developing this technology.

PNNL wins Federal Laboratory Consortium award for bringing government technology to the marketplace

Software that helps cybersecurity analysts prevent hacks is one of the latest innovations Pacific Northwest National Laboratory has successfully commercialized with the help of business partners.

Due to the unique paths the development teams took to get the technology from Department of Energy lab to the private sector, the Federal Laboratory Consortium has honored the two teams made up of lab and commercial business staff with 2016 Excellence in Technology Transfer awards. The consortium is a nationwide network that encourages federal laboratories to transfer laboratory-developed, taxpayer-funded technologies to commercial markets.

PNNL has earned a total of 83 such awards since the program began in 1984 — far more than any other national laboratory. The 2016 awards will be presented April 27 in Chicago, Illinois, at the consortium's annual meeting.

Software "CHAMPIONs" cybersecurity experts

If you're a hacker aimed at stealing credit card information from a retail company and you want to evade detection, you hide in massive amounts of network data. Analysts have the know-how to sort through this digital mess to find hackers, but they often identify attacks too late. Analytical software developed at PNNL and licensed to Champion Technology Company Inc. can help find these and other threats in near-real-time. That's because the software, called Columnar Hierarchical Auto-associative Memory Processing in Ontological Networks — or CHAMPION, has the knowledge to sort through data like an analyst, but on a much greater scale.

Scientists designed CHAMPION to use human analysts and historical data to learn about the company it's protecting. Starting with advanced Semantic Web technologies, which translate human knowledge into something that's machine readable, CHAMPION then uses descriptive logic to reason whether activity is suspicious. For example, if a retail company's HVAC data back-up account tries to access the point-of-sale system, CHAMPION could use historical data to conclude that this is unusual. Once identified, the software alerts an analyst of the suspicious activity — in time to potentially thwart an attack.

Sorting through data can consume up to 40 percent of an analyst's day. By streamlining these tasks, CHAMPION can save money and free analysts to focus on higher-priority tasks. And cybersecurity isn't CHAMPION's only trick. Change its diet of knowledge and the software can learn to analyze financial services or health care data.

This technology transfer involved a unique collaboration between PNNL and Early X, a non-profit education foundation spun out from Pepperdine University's Graziadio School of Business and Management. In this effort, a group of MBA students and diverse business executives identified 70 market opportunities for CHAMPION. This groundwork led to the start of Champion Technology Company Inc.

The team receiving an FLC Award for CHAMPION includes: PNNL's Shawn Hampton and Kannan Krishnaswami; Champion Technology Company's Ryan Hohimer; and former PNNL staff John McEntire, Frank Greitzer and Matthew Love.

For more information on technology transfer programs at PNNL, visit their .

Automatic bug-repair system fixes 10 times as many errors as its predecessors.

MIT researchers have developed a machine-learning system that can comb through repairs to open-source computer programs and learn their general properties, in order to produce new repairs for a different set of programs.

The researchers tested their system on a set of programming errors, culled from real open-source applications, that had been compiled to evaluate automatic bug-repair systems. Where those earlier systems were able to repair one or two of the bugs, the MIT system repaired between 15 and 18, depending on whether it settled on the first solution it found or was allowed to run longer.

While an automatic bug-repair tool would be useful in its own right, professor of electrical engineering and computer science Martin Rinard, whose group developed the new system, believes that the work could have broader ramifications.

“One of the most intriguing aspects of this research is that we’ve found that there are indeed universal properties of correct code that you can learn from one set of applications and apply to another set of applications,” Rinard says. “If you can recognize correct code, that has enormous implications across all software engineering. This is just the first application of what we hope will be a brand-new, fabulous technique.”

Fan Long, a graduate student in electrical engineering and computer science at MIT, presented a paper describing the new system at the Symposium on Principles of Programming Languages last week. He and Rinard, his advisor, are co-authors.

Users of open-source programs catalogue bugs they encounter on project websites, and contributors to the projects post code corrections, or “patches,” to the same sites. So Long was able to write a computer script that automatically extracted both the uncorrected code and patches for 777 errors in eight common open-source applications stored in the online repository GitHub.

Feature performance

As with all machine-learning systems, the crucial aspect of Long and Rinard’s design was the selection of a “feature set” that the system would analyze. The researchers concentrated on values stored in memory — either variables, which can be modified during a program’s execution, or constants, which can’t. They identified 30 prime characteristics of a given value: It might be involved in an operation, such as addition or multiplication, or a comparison, such as greater than or equal to; it might be local, meaning it occurs only within a single block of code, or global, meaning that it’s accessible to the program as a whole; it might be the variable that represents the final result of a calculation; and so on.

Long and Rinard wrote a computer program that evaluated all the possible relationships between these characteristics in successive lines of code. More than 3,500 such relationships constitute their feature set. Their machine-learning algorithm then tried to determine what combination of features most consistently predicted the success of a patch.

“All the features we’re trying to look at are relationships between the patch you insert and the code you are trying to patch,” Long says. “Typically, there will be good connections in the correct patches, corresponding to useful or productive program logic. And there will be bad patterns that mean disconnections in program logic or redundant program logic that are less likely to be successful.”

Ranking candidates

In earlier work, Long had developed an algorithm that attempts to repair program bugs by systematically modifying program code. The modified code is then subjected to a suite of tests designed to elicit the buggy behavior. This approach may find a modification that passes the tests, but it could take a prohibitively long time. Moreover, the modified code may still contain errors that the tests don’t trigger.

Long and Rinard’s machine-learning system works in conjunction with this earlier algorithm, ranking proposed modifications according to the probability that they are correct before subjecting them to time-consuming tests.

The researchers tested their system, which they call Prophet, on a set of 69 program errors that had cropped up in eight popular open-source programs. Of those, 19 are amenable to the type of modifications that Long’s algorithm uses; the other 50 have more complicated problems that involve logical inconsistencies across larger swaths of code.

When Long and Rinard configured their system to settle for the first solution that passed the bug-eliciting tests, it was able to correctly repair 15 of the 19 errors; when they allowed it to run for 12 hours per problem, it repaired 18.

Of course, that still leaves the other 50 errors in the test set untouched. In ongoing work, Long is working on a machine-learning system that will look at more coarse-grained manipulation of program values across larger stretches of code, in the hope of producing a bug-repair system that can handle more complex errors.

“A revolutionary aspect of Prophet is how it leverages past successful patches to learn new ones,” says Eran Yahav, an associate professor of computer science at the Technion in Israel. “It relies on the insight that despite differences between software projects, fixes — patches — applied to projects often have commonalities that can be learned from. Using machine learning to learn from ‘big code’ holds the promise to revolutionize many programming tasks — code completion, reverse-engineering, et cetera.”

New transparent metamaterials under development could make possible computer chips and interconnecting circuits that use light instead of electrons to process and transmit data, representing a potential leap in performance.

Although optical fibers are now used to transmit large amounts of data over great distances, the technology cannot easily be miniaturized because the wavelength of light is too large to fit within the miniscule dimensions of microcircuits.

"The role of optical fibers is to guide light from point A to point B, in fact, across continents," said Zubin Jacob, an assistant professor of electrical and computer engineering at Purdue University. "The biggest advantage of doing this compared to copper cables is that it has a very high bandwidth, so large amounts of data can pass through these optical cables as opposed to copper wires. However, on our computers and consumer electronics we still use copper wires between different parts of the chip. The reason is that you can't confine light to the same size as a nanoscale copper wire."

Transparent metamaterials, nanostructured artificial media with transparent building blocks, allow unprecedented control of light and may represent a solution. Researchers are making progress in developing metamaterials that shrink the wavelength of light, pointing toward a strategy to use light instead of electrons to process and transmit data in computer chips.

"If you have very high bandwidth communication on the chip as well as interconnecting circuits between chips, you can go to faster clock speeds, so faster data processing," Jacob said. Such an advance could make it possible to shrink the bulkiness of a supercomputer cluster to the size of a standard desktop machine.

Unlike some of the metamaterials under development, which rely on the use of noble metals such as gold and silver, the new metamaterials are made entirely of dielectric materials, or insulators and non-metals. This approach could allow researchers to overcome a major limitation encountered thus far in the development of technologies based on metamaterials: using metals results in the loss of too much light to be practical for many applications.

 A review article about all-dielectric metamaterials appeared online this month in the journal Nature Nanotechnology, highlighting the rapid development in this new field of research. The article was authored by doctoral student Saman Jahani and Jacob.

"A key factor is that we don't use metals at all in this metamaterial, because if you use metals a lot of the light goes into heat and is lost," Jacob said. "We want to bring everything to the silicon platform because this is the best material to integrate electronic and photonic devices on the same chip."

A critical detail is the material's "anisotropic velocity" – meaning light is transmitted much faster in one direction through the material than in another. Conventional materials transmit light at almost the same speed no matter which direction it is traveling through the material.

"The tricky part of this work is that we require the material to be highly anisotropic," he said. "So in one direction light travels almost as fast as it would in a vacuum, and in the other direction it travels as it would in silicon, which is around four times slower."

The innovation could make it possible to modify a phenomenon called "total internal reflection," the principle currently used to guide light in fiber optics. The researchers are working to engineer total internal reflection in optical fibers surrounded by the new silicon-based metamaterial.

"Our contribution has been basically the fact that we have been able to adapt this total internal reflection phenomenon down to the nanoscale, which was conventionally thought impossible," Jacob said.

Because the material is transparent it is suitable for transmitting light, which is a critical issue for practical device applications. The approach could reduce heating in circuits, meaning less power would be required to operate devices. Such an innovation could in the long run bring miniaturized data processing units.

"Another fascinating application for these transparent metamaterials is in enhancing light-matter coupling for single quantum light emitters," Jacob said. "The size of light waves inside a fiber are too large to effectively interact with tiny atoms and molecules. The transparent metamaterial cladding can compress the light waves to sub-wavelength values thus allowing light to effectively interact with quantum objects. This can pave the way for light sources at the single photon level."

The research is being performed jointly at Purdue's Birck Nanotechnology Center in the university's Discovery Park and at the University of Alberta.

The researchers have obtained a U.S. patent on the design. The research was funded by the National Science and Engineering Research Council of Canada and Helmholtz Alberta Initiative.

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