A new model developed by Ģ����ý researchers could offer a new explanation as to how cracks on icy moons, such as Pluto’s Charon, formed.

Until now, it was thought that the cracks were the result of geodynamical processes, such as plate tectonics, but the models run by Alice Quillen and her collaborators suggest that a close encounter with another body might have been the cause.

Astronomers have long known that the craters visible on moons were caused by the impact of other bodies, billions of years ago. But for every crash and graze, there would have been many more close encounters. By devising and running a new computer model, Quillen, a professor of physics and astronomy at Rochester, has now shown that the tidal pull exerted by another, similar object could be strong enough to crack the surface of such icy moons. Quillen also thinks that “it might even offer a possible explanation for the crack on Mars, but that’s much harder to model.”

Icy moons exhibit what is know as brittle elastic behavior, which Quillen says most resembles “silly putty.”

“If you take silly putty and throw it on the floor it bounces – that’s the elastic part,” said Quillen. “But if you pull on it rapidly and hard enough, it breaks apart.”

To simulate the behavior, Quillen modeled the icy moons as if their interior was made up of many bodies connected by springs (an N-body problem with springs). While N-body problems are often used to understand the effect of gravity on planets and stars, N-body problems had never been used to model the inside of an astronomical body, in this case the moons. Other models for icy moons used what are known as “rubble pile models.”

“I was inspired by computer graphics code in how to model the icy moons,” said Quillen. “The inside of the moons is similar to how blood splatter is modeled in games and the outer, icy crust is similar to modeling clothes and how they move. But I, of course, had to ensure the code matched the underlying physics!”

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Simulations done by Alice Quillen showing how different bodies would react to close tidal encounters, including when modeling only the cores of the icy moons and how cracks occur when the top, icy layer is added.

To ensure her model took into account the right properties for the materials that make up the moons, she worked with earth sciences Professor Cynthia Ebinger.

“I jumped at the opportunity to consider a novel alternative to plate tectonics, the governing theory to explain earthquakes, volcanoes and moving plates on Earth,” said Ebinger. “My role was to provide some checks and balances to Alice’s modeling and the choice of model parameters.”

In the paper, to be published by the journal Icarus, Quillen states that “strong tidal encounters” may be responsible for the cracks on icy moons such as Charon, Saturn’s Dione and Tethys, and Uranus’ Ariel.

The key factor in determining if a crack is going to occur is the strain rate, the rate of pull from another body that would have caused the moons to deform at a rate that the top, icy layer could not sustain – leading to cracks.

Quillen’s and Ebinger’s co-authors on the paper are David Giannella and John G. Shaw, also at the University of Rochester.

In a companion paper, published in Monthly Notices of the Royal Astronomical Society, Quillen has shown that her models are consistent with the rate at which moons spin up or down when orbiting another object.”

The work was in part supported by NASA grant NNX13AI27G.

Using the same mathematical framework as the Rochester Cloak, researchers at the URochester have been able to use flat screen displays to extend the range of angles that can be hidden from view. Their method lays out how cloaks of arbitrary shapes, that work from multiple viewpoints, may be practically realized in the near future using commercially available digital devices.

The Rochester researchers have shown a proof-of-concept demonstration for such a setup, which is still much lower resolution than the nearly perfect imaging achieved by the Rochester Cloak lenses. But with increasingly higher resolution displays becoming available, the “digital integral cloak” they describe in their new will continue to improve.

While the Rochester Cloak offered a simple way of cloaking, it was limited by the cloaking working only over small angles, and cloaking large objects would require large, expensive lenses.

By breaking up the information into distinct pieces, it becomes possible to use currently available digital cameras and digital displays. The Rochester researchers use a camera to scan a background and then encode the information in such a way that every pixel on a screen offers a unique view of a given point on the background for a given position of a viewer. By doing this for many views and using lenticular lenses – a sheet of plastic with an array of thin, parallel semicylindrical lenses – they can recreate multiple images of the background, each corresponding to a viewer at a different position. So if the viewer moves from side to side, every part of the background moves accordingly as if the screen was not there, “cloaking” anything in the space between the screen and the background.

In the current system, it takes PhD student Joseph Choi and his advisor Professor of Physics John Howell several minutes to scan, process and update the image on the screen, i.e. to update the background. But Choi explains they are hoping soon to be able to do this in real-time, even if at lower resolution.

Their mathematical framework and their proof-of-concept setup also demonstrates how any object of a fixed size can be cloaked, even when in motion – so long as the shape of the object remains fixed and does not deform. To do this one side of the object would be covered in an array of sensors – effectively cameras – and the other side in pixels with tiny lenses over them. Choi’s and Howell’s approach could then be used to identify which sensors need to feed into which pixels so as to show the background as if an object wasn’t there. A similar trick has been used in advertising, but for one viewing angle only. However, by using the Rochester group’s setup, a car, for example, could be made invisible to viewers from multiple positions, not just to a person at a predetermined position.

The Rochester Digital Cloak is patent pending. For business inquiries please read this .

Work by an internationally acclaimed Rochester professor may offer an alternative to the way in which researchers have approached some photonics applications.

Photonics applications rely greatly on what physicists call nonlinear optics – the different way in which materials behave depending on the intensity of light that passes through them. The greater the nonlinearity, the more promising the material for real-life applications. Now a team led by Robert W. Boyd, professor of optics and physics at the URochester and the Canada Excellence Research Chair in Quantum Nonlinear Optics at the University of Ottawa, has demonstrated that the transparent, electrical conductor indium tin oxide can result in up to 100 times greater nonlinearity than other known materials.

“This result is a game-changer for photonics applications,” said Boyd. “It rests on the core of what I’ve worked on for over 30 years at Rochester. I find it very rewarding that even after all this time there are still fundamental questions to be answered in the field of nonlinear optics.”

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Photonics uses light to transmit information. Photonics also uses light to perform logical operations, just as electronics does with electrons. A key aspect to being able to exert control over light is to be able to control a specific property – the refractive index – of the material that is transmitting the light.

Tweaking the refractive index of a material, which leads to light traveling faster or slower, is the key way in which photonics applications control light. When the refractive index is different for different light intensities, the material is described as being optically nonlinear.

When a pulse of light is sent through the material, the refractive index changes according to that intensity. The refractive index of the material is changed for only a few femtoseconds – a few millionths of a billionth of a second. For some potential applications, it is possible to send a second pulse through the material before it has time to recover for the first pulse. This second pulse then “sees” the material as having the refractive index as modified by the first pulse.

In general though, it is the quickness with which the material recovers as well as the range of values that the refractive index can take – how strongly nonlinear the material is – that makes this system particularly attractive to photonics applications.

Boyd, his PhD student at Ottawa, M. Zahirul Alam, and then-research associate Israel De Leon were able to improve on the previous record for optical nonlinearities by a factor of 100. This improvement took place because these researchers exploited the unusual optical properties of a material that occurs under certain conditions, what is known as the epsilon-near-zero region.

“It was surprising that showing such a strong optical nonlinearity in a known metal was this easy,” said Boyd. “This material has been around for many years, but until now the community had overlooked the potential that the ‘epsilon-near-zero’ region of materials offered.”

“The optical nonlinear response that we have observed introduces a new paradigm in nonlinear optics,” said De Leon, now a professor at Tecnologico de Monterrey, Mexico. “The common knowledge had always been that nonlinear effects are tiny compared with the linear ones; but in our work we have measured a nonlinear response that is 170% larger than the linear response.”

The result opens the door for more careful study of this region of materials, with a view to finding a material that can offer just the right properties for certain photonics applications.

The ‘epsilon-near-zero’ region for this material is linked to light of a specific frequency, roughly a wavelength of 1.2 micrometers. This wavelength is of interest because it is in between that of visible light and light of wavelength 1.5 micrometers. This wavelength is of particular interest to optical communications, which uses devices such as fiber optics to transmit information in light.

It is possible that changes in the chemical composition of the material could lead to a change in the frequency at which epsilon near zero occurs, thus bringing this frequency closer to that used by optical communications.

Researchers have developed a new conceptual framework for understanding how stars similar to our Sun evolve. Their framework helps explain how the rotation of stars, their emission of x-rays, and the intensity of their stellar winds vary with time. According to first author Eric Blackman, professor of physics and astronomy at the URochester, the work could also “ultimately help to determine the age of stars more precisely than is currently possible.”

In a paper published today in Monthly Notices of the Royal Astronomical Society, the researchers describe how they have corroborated known, observable data for the activity of Sun-like stars with fundamental astrophysics theory. By looking at the physics behind the speeding up or slowing down of a star’s rotation, its x-ray activity, and magnetic field generation, Blackman says the research is a “first attempt to build a comprehensive model for the activity evolution of these stars.”

Using our Sun as the calibration point, the model most accurately describes the likely behavior of the Sun in the past, and how it would be expected to behave in the future. But Blackman adds that there are many stars of similar mass and radius, and so the model is a good starting point for predictions for these stars.

“Our model shows that stars younger than our Sun can vary quite significantly in the intensity of their x-ray emission and mass loss,” said Blackman. “But there is a convergence in the activity of the stars after a certain age, so you could say that our Sun is very typical for stars of its mass, radius, and its age. They get more predictable as they age.”

“We’re not yet at the point where we can accurately predict a star’s precise age, because there are simplifying assumptions that go into the model,” said Blackman. “But in principle, by extending the work to relax some of these assumptions we could predict the age of  for a wide range of stars based on their x-ray luminosity.”

At the moment, empirically determining the age of stars is most easily accomplished if a star is among a cluster of stars, from whose mutual properties astronomers can estimate the age. Blackman explains that its age can then be estimated “to an accuracy not better than a factor of 25% of its actual age, which is typically billions of years.” The problem is worse for “field stars,” alone in space such that the cluster method of dating cannot be used. For these stars, astronomers have turned to “gyrochronology” and “activity” aging – empirically aging the stars based the fact that older stars of known age rotate more slowly and have lower x-ray luminosities than younger stars.

“Over the past few decades astronomers have been able to empirically measure these trends in rotation and magnetic activity for stars like the Sun, but Eric and his collaborators are trying to devise a comprehensive theoretical interpretation,” said Eric Mamajek, professor of physics and astronomy at the URochester and one of the astronomers leading the development of empirical methods for determining a star’s age. “Ultimately this should lead to improved constraints on the evolution of rotation and activity in Sun-like stars, and better constraints on how the magnetic properties of our Sun have changed over the course of its main sequence life.”

And this is where the model developed by Blackman and his coauthor James E. Owen is important: it provides a physics explanation for how stellar rotation, activity, magnetic field, and mass loss all mutually evolve with age.

“Only by tackling the entire problem of how stellar rotation, x-ray activity, magnetic field and mass-loss mutually affect each other could we build a complete picture,” said Owen, a NASA Hubble fellow at the Institute for Advanced Study, Princeton. “We find these processes to be strongly intertwined and the majority of previous approaches had only considered the evolution of one or two processes together, not the complete problem.”

Blackman carried out part of this work while he was on sabbatical as a IBM-Einstein Fellow/Simons Fellow at the Institute for Advanced Study, Princeton. The authors would also like to acknowledge NSF and NASA for their grant support.

The paper, entitled “Minimalist coupled evolution model for stellar x-ray activity, rotation, mass loss, and magnetic field,” was published on March 23, 2016 in Monthly Notices of the Royal Astronomical Society, by Oxford University Press. A copy of the paper is available at .

In a , PhD student Nabil Hossain reports that he and his collaborators have taught computers to analyze tweets about drinking in an effort to predict where Twitter users are when they report drinking.

Hossain is a student in the computer science group led by Henry Kautz, the Robin and Tim Wentworth Director of the Goergen Institute for Data Science. He posted the paper on the arXiv.org repository after it was accepted for the International AAAI Conference on Web and Social Media to be held in Germany in May. An article in MIT Technology Review, which has helped Hossain’s work find a viral life of its own, says the work is based on “two breakthroughs.”

“The first is a way to train a machine-learning algorithm to spot tweets that relate to alcohol and those sent by people drinking alcohol at the time,” according to the article. “The second is a way to find a Twitter user’s home location with much greater accuracy than has ever been possible and therefore to determine whether they are drinking at home or not.”

For the first step, the researchers chose to use alcohol consumption to demonstrate the effectiveness of their model, which can not only distinguish between people who are discussing an activity versus those who are discussing performing the activity themselves, but can also determine whether they are performing it at-the-moment as opposed to some time in the past or the future. This model could also be applied to other behaviors, not just drinking.

Until now, predicting a social media user’s home location was done by establishing the place from which the user most frequently tweets, or the most common last location of the day from which the user posts. In the new work, the researchers applied machine-learning techniques to identify in-the-moment user behavior. That allowed them to accurately predict users’ home locations within 100 meters.

Combining these tools, they were able to discover patterns of alcohol use in urban and suburban settings. Their goal is that “such methods can help us better understand the occurrence, frequency, and settings of alcohol consumption, a health-risk behavior, and can lead to actionable information in prevention and public health.”

A team of University and Adobe researchers is outperforming other approaches to creating computer-generated image captions in an international competition. The key to their winning approach? Thinking about words – what they mean and how they fit in a sentence structure – just as much as thinking about the image itself.

The Rochester/Adobe model mixes the two approaches that are often used in image captioning: the “top-down” approach, which starts from the “gist” of the image and then converts it into words, and the “bottom-up” approach, which first assigns words to different aspects of the image and then combines them together to form a sentence.

The Rochester/Adobe model is currently beating Google, Microsoft, Baidu/UCLA, Stanford University, University of California Berkeley, University of Toronto/Montreal, and others to top the leaderboard in an image captioning competition run by Microsoft, called the Microsoft COCO Image Captioning Challenge. While the winner of the year-long competition is still to be determined, the Rochester “Attention” system – or ATT on the leaderboard – has been leading the field since last November.

Other groups have also tried to combine these two methods by having a feedback mechanism that allows a system to improve on what just one of the approaches would be able to do. However, several systems that tried to blend these two approaches focused on “visual attention,” which tries to take into account which parts of an image are visually more important to describe the image better.

The Rochester/Adobe system focuses on what the researchers describe as “semantic attention.” In a paper accepted by the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), entitled  computer science professor Jiebo Luo and his colleagues define semantic attention as “the ability to provide a detailed, coherent description of semantically important objects that are needed exactly when they are needed.”

“To describe an image you need to decide what to pay more attention to,” said Luo. “It is not only about what is in the center of the image or a bigger object, it’s also about coming up with a way of deciding on the importance of specific words.”

For example, take an image that shows a table and seated people. The table might be at the center of the image but a better caption might be “a group of people sitting around a table” instead of “a table with people seated.” Both are correct, but the former one also tries to take into account what might be of interest to readers and viewers.

Computer image captioning brings together two key areas in artificial intelligence: computer vision and natural language processing. For the computer vision side, researchers train their systems on a massive dataset of images, so they learn to identify objects in images. Language models can then be used to put these words together. For the algorithm that Luo and his team used in their system, they also trained their system on many texts. The objective was not only to understand sentence structure but also the meanings of individual words, what words often get used together with these words, and what words might be semantically more important.

The related paper can be found online at . The Rochester/Adobe team is formed by Luo; doctoral student Quanzeng You; and their Adobe collaborators, Hailin Jin, Zhaowen Wang, and Chen Fang. They will present this work as a “Spotlight” to the computer vision community at the 2016 CVPR to be held in Las Vegas in late June 2016.

A closely related paper on video captioning by Luo, graduate student Yuncheng Li, and their Yahoo Research colleagues Yale Song, Liangliang Cao, Joel Tetreault, andLarry  Goldberg.  will also be featured as a “Spotlight” presentation at CVPR.

David Cameron, a visiting scientist in Eric Mamajek’s research group in the Department of Physics and Astronomy, recently discovered a new short-period comet named Comet P/2015 PD229 (ISON-Cameron).

Cameron discovered the comet last August while analyzing images taken in May 2015 by Mamajek with the Dark Energy Camera (DECam) on the Blanco 4-m telescope at the Cerro Tololo Inter-American Observatory in Chile.

“We were looking not for comets, but objects in the outer solar system called Kuiper Belt objects, which orbit the Sun far beyond the orbit of Neptune,” said Cameron. “The former planet Pluto is the most well known Kuiper Belt object. In our images, these objects appear as little dots of light that, over time, seem to slowly jump from point to point. While searching our images one day, I was surprised to find a small streak of light with a tail, and like the Kuiper Belt objects it also jumped from point to point over time. The object wasn’t described in any database, so that was it: an unexpected, new comet discovery.”

Mamajek ‘s group has been using DECam to discover both faint young failed stars (“brown dwarfs”) in nearby star clusters (a project led by PhD student Fred Moolekamp) and new outer solar system objects (led by Cameron).

“Discovering a comet is not particularly rare nowadays, but still exciting,” said Mamajek. “Ģ����ý 80 comet of all types were discovered last year. This is one of about 500 Jupiter family comet, which means that Jupiter strongly influences its orbit around the Sun.”

Though no comets have been found by Rochester astronomers in the past century, Rochester does have a history in comet hunting. “The prolific 19th-century comet hunter Lewis Swift discovered or co-discovered 13 comets, including the famous comet 109P/Swift-Tuttle – the parent body of the Perseid meteor shower,” said Mamajek.

Swift (1820-1913) was born in the village of Clarkson in Monroe County, New York, and in recognition of his astronomical discoveries received an honorary degree from the Ģ����ý in 1879.

Mamajek explained that the new comet appears to be about 13 miles across, “larger than most other Jupiter family comets.” It orbits the Sun every 19.2 years, mostly between the orbits of Jupiter and Saturn. The comet reached its closest point to the Sun in August 2015 just inside the orbit of Jupiter. Given their icy natures, low inclinations, and orbits crossing the large outer planets, it is thought that Jupiter family comets originate from the Kuiper Belt beyond Neptune. When their orbits bring them closer to the Sun, the resultant heating causes their volatile compounds, including water, to sublimate off and produce a distinctive tail.

In recent years, Rochester astronomers have also discovered the first ring system around an exoplanet, the first indirect evidence for the presence of an exomoon, contributed to new technologies for the hunting of asteroids, developed theories for the shape of the orbits of stars in our galaxies, and explained how stellar death can lead to twin celestial jets.

Read .

The prize, presented by the Breakthrough Prize Foundation, was awarded “for the fundamental discovery of neutrino oscillations, revealing a new frontier beyond, and possibly far beyond, the standard model of particle physics.”

The Breakthrough prizes were set up by Russian billionaire Yuri Milner together with well-known entrepreneurs from Silicon Valley, including Facebook’s Mark Zuckerberg and Google’s Sergei Brin. The committee for the physics prize includes Stephen Hawking, Nobel prize winner Saul Perlmutter, and other past winners of the Breakthrough Prize in Fundamental Physics.

The prize is valued at $3 million, and is shared with four other international experimental collaborations studying neutrino oscillation: the Daya Bay, KamLAND, SNO, and Super-Kamiokande scientific collaborations.

“These experiments point to the possibility that the tiny neutrino is the reason that the universe today has as much matter as it does,” said McFarland, professor of physics. “It may be that without the neutrino, the matter that we and the earth around us are made of would have been gobbled up by antimatter in the early universe. We may owe the neutrinos a big ‘thank you’.”

There are three distinct “flavors” (types) of neutrinos in nature that can be identified by how they interact with matter. These experiments study neutrinos sent over long distances (from the sun, from neutrinos created when cosmic rays interact in the atmosphere, and from neutrinos created by humans at nuclear reactors and particle accelerators) and observed that neutrinos created as one flavor type “oscillate” into neutrinos of other flavors. This phenomenon can only occur if neutrinos have mass; and therefore, by demonstrating neutrino oscillations these experiments showed neutrinos have mass. Two of the leaders of the five experiments honored by this prize also received the Nobel Prize in Physics this past October for their roles in the discovery.

“Some people refer to neutrinos as ghost particles because they almost never interact,” said McFarland. “That makes the experiments very challenging and time consuming. Going halfway around the world to Japan is not the most convenient commute from Rochester. But an award like this reminds me what a special opportunity it has been to work on this experiment and to be a part of this discovery.”

“It’s great fun to be part of Rochester’s long love affair with neutrinos,” said Manly, professor of physics. “We have a history. The project in which our group is involved, which is recognized by this prize, is partly based on a foundation laid by a URochester alumnus (Ph.D. 1955) Masatoshi Koshiba – for which he was awarded the 2002 Nobel Prize in physics.”

Researchers at the URochester became interested in the opportunity to be part of T2K experiment fourteen years ago, and the work is still going on. Apart from Manly and McFarland, the Breakthrough Prize also named two Rochester postdocs, Phil Rodrigues and Daniel Ruterbories, and a former graduate student, Melanie Day, in their laureate list. The Rochester team currently working on the experiment also includes graduate student Konosuke Iwamoto. Professor Arie Bodek, senior researcher Howard Budd and graduate student Hyup Woo Lee are past contributors who are still at Rochester.

“Ten years after we started to work on the experiment that became T2K, two things happened,” said McFarland. “A devastating earthquake struck the east coast of Japan with the epicenter only a few hours drive from where we work, and T2K had just finished collecting the data for which the Breakthrough Prize was awarded. I still remember the projector shaking from the aftershocks of the quake as a graduate student showed the analysis of the data. And I remember the realization that this was what we had been working for over those ten years as being the biggest shock of the day.”

The award was presented last night at a ceremony at the NASA Ames Research Centre in California. The ceremony was broadcast live on the National Geographic Channel, and was hosted by comedian Seth Macfarlane. A one-hour version of the broadcast is scheduled for Fox on Nov. 29, at 7 p.m. ET.

The T2K experiment in Japan uses a proton accelerator to create an intense beam of muon neutrinos. The neutrinos are directed to the Super-Kamiokande detector in the Kamioka mine deep inside Mt Ikeno, 295 km away. T2K’s citation for the prize was given for the observation of electron neutrino appearance in the muon neutrino beam, which is the first observation of the appearance of a neutrino flavor. This discovery sets the stage for the study of differences in the neutrino oscillation process relative to their antiparticles (antineutrinos), called CP violation, which may elucidate how the universe came to be matter dominated. The T2K Collaboration has included over 500 members from 64 institutions in 12 countries.

In the past 10 years, CEIS has provided more than $4M in funding to magnify the impact of more than 200 corporate-sponsored research projects at the URochester, RIT, Cornell, Columbia, and other partner universities. This funding has generated $740 million in direct economic impact, and created or retained at least 440 jobs.

“We are grateful to Governor Cuomo and Empire State Development for continuing the CAT program and for selecting CEIS for renewal,” said Mark F. Bocko, director of CEIS and professor of electrical and computer engineering. “I want to especially thank Assembly Majority Leader Joe Morelle, Senator Joe Robach, and the other members of our state delegation for their continued support and leadership. The CAT program investment, which enables CEIS and the similar centers across the state to leverage the capabilities of universities to help grow industry and create jobs, has shown significant results and we are pleased to continue our work in the Finger Lakes region.”

“The New York State funding also has enabled CEIS to support and enhance the world class capabilities of our region’s companies and universities in optics, photonics, and imaging,” said Paul Ballentine, executive director of CEIS. “This work, among other things, helped lay the groundwork to secure the recent AIM Photonics award. This would not have been possible without the state’s investment in CEIS.”

CEIS is one of 15 CATs in New York, supported by NYSTAR, a Division of Empire State Development. It is designed to spur technology-based research and economic development in New York; promote national and international research collaboration and innovation; and better leverage the state’s research expertise and funding with investments from the federal government, foundations, businesses, venture capital firms, and other entities.

In the past 10 years, has provided more than $4M in funding to magnify the impact of more than 200 corporate-sponsored research projects at the URochester, RIT, Cornell, Columbia, and other partner universities. This funding has generated $740 million in direct economic impact, and created or retained at least 440 jobs.

“We are grateful to Governor Cuomo and Empire State Development for continuing the CAT program and for selecting CEIS for renewal,” said Mark F. Bocko, director of CEIS and professor of electrical and computer engineering. “I want to especially thank Assembly Majority Leader Joe Morelle, Senator Joe Robach, and the other members of our state delegation for their continued support and leadership. The CAT program investment, which enables CEIS and the similar centers across the state to leverage the capabilities of universities to help grow industry and create jobs, has shown significant results and we are pleased to continue our work in the Finger Lakes region.”

“The New York State funding also has enabled CEIS to support and enhance the world class capabilities of our region’s companies and universities in optics, photonics, and imaging,” said Paul Ballentine, executive director of CEIS. “This work, among other things, helped lay the groundwork to secure the recent AIM Photonics award. This would not have been possible without the state’s investment in CEIS.”

CEIS is one of 15 CATs in New York, supported by NYSTAR, a Division of Empire State Development. It is designed to spur technology-based research and economic development in New York; promote national and international research collaboration and innovation; and better leverage the state’s research expertise and funding with investments from the federal government, foundations, businesses, venture capital firms, and other entities.