Young technology firms’ sluggish growth rate is a troubling sign for the US economy, according to a newly-released white paper from the Kauffman Foundation, a nonprofit organization that studies entrepreneurship and provides grants to award educational achievement and entrepreneurial success. The high-tech sector has traditionally sparked economic growth in recent decades. However, the Kauffman report finds the number of technology firms five years old and younger—which typically drive job creation—has fallen from a high of 113,000 in 2001 to about 80,000 now, as it was in the mid-1990s. One factor that may have skewed the number is the acquisition of young firms by established technology companies. The report also finds that technology firms’ job reallocation rate—which basically subtracts the rate at which jobs are lost from the rate at which they are created—has fallen to the lowest rate since the late 1970s “Because young high-tech firms are so disproportionately important for innovation and job creation, a slowdown in this sector calls for a new approach to fostering a stronger entrepreneurial economy,” said Dane Stangler, the Kauffman Foundation’s vice president of research and policy. (Reuters)(Ewing Marion Kauffman Foundation)
Wednesday, March 5, 2014
Saturday, October 20, 2012
Popularity versus similarity: A balance that predicts network growth
That's much less likely, unless you are a fan of Italian baroque music or free solo climbing.
In that case, you would have heard of Bachar just as likely as Washington. The latter was popular, while the former was not as popular but had interests similar to yours.
A new paper published this week in the science journal Nature by the Cooperative Association for Internet Data Analysis (CAIDA), based at the San Diego Supercomputer Center (SDSC) at the University of California, San Diego, explores the concept of popularity versus similarity, and if one more than the other fuels the growth of a variety of networks, whether it is the Internet, a social network of trust between people, or a biological network.
The researchers, in a study called "Popularity Versus Similarity in Growing Networks", show for the first time how networks evolve optimizing a unique trade-off between popularity and similarity. They found that while popularity attracts new connections, similarity is just as attractive.
"Popular nodes in a network, or those that are more connected than others, tend to attract more new connections in growing networks," said Dmitri Krioukov, co-author of the Nature paper and a research scientist with SDSC's CAIDA group, which studies the practical and theoretical aspects of the Internet and other large networks. "But similarity between nodes is just as important because it is instrumental in determining precisely how these networks grow. Accounting for these similarities can help us better predict the creation of new links in evolving networks."
In the paper, Krioukov and his colleagues, which include network analysis experts from academic institutions in Cyprus and Spain, describe a new model that significantly increases the accuracy of network evolution prediction by considering the trade-offs between popularity and similarity. Their model describes large-scale evolution of three kinds of networks: technological (the Internet), social (a network of trust relationships between people), and biological (a metabolic network of the Escherichia coli, typically harmlessly found in the human gastrointestinal tract, though some strains can cause diarrheal diseases.)
The researchers write that the model's ability to predict links in networks may find applications ranging from predicting protein interactions or terrorist connections to improving recommender and collaborative filtering systems, such as Netflix or Amazon product recommendations.
"On a more general note, if we know the laws describing the dynamics of a complex system, then we not only can predict its behavior, but we may also find ways to better control it," added Krioukov.
In establishing connections in networks, nodes optimize certain trade-offs between the two dimensions of popularity and similarity, according to the researchers. "These two dimensions can be combined or mapped into a single space, and this mapping allows us to predict the probability of connections in networks with a remarkable accuracy," said Krioukov. "Not only can we capture all the structural properties of three very different networks, but also their large-scale growth dynamics. In short, these networks evolve almost exactly as our model predicts."
Many factors contribute to the probability of connections between nodes in real networks. In the Internet, for example, this probability depends on geographic, economic, political, technological, and many other factors, many of which are un-measurable or even unknown.
"The beauty of the new model is that it accounts for all of these factors, and projects them, properly weighted, into a single metric, while allowing us to predict the probability of new links with a high degree of precision," according to Krioukov.
The other researchers who worked on this project are Fragkiskos Papadopoulos, Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology in Cyprus; Maksim Kitsak, CAIDA/SDSC/UC San Diego; M. Ángeles Serrano and Marián Boguñá, Departament de Fisica Fonamental, Univsitat de Barcelona, in Spain.
This research was supported by a variety of grants, including National Science Foundation (NSF) grants CNS-0964236, CNS-1039646, and CNS-0722070; Department of Homeland Security (DHS) grant N66001-08-C-2029; Defense Advanced Research Projects Agency (DARPA) grant HR0011-12-1-0012; and support from Cisco Systems.
International support was provided by a Marie Curie International Reintegration Grant within the 7th European Community Framework Programme; Office of the Ministry of Economy and Competitiveness, Spain (MICINN) projects FIS2010-21781-C02-02 and BFU2010-21847-C02-02; Generalitat de Catalunya grant 2009SGR838; the Ramón y Cajal program of the Spanish Ministry of Science; and the Catalan Institution for Research and Advanced Studies (ICREA) Academia prize 2010, funded by the Generalitat de Catalunya, Spain.
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The above story is reprinted from materials provided by University of California, San Diego. The original article was written by Jan Zverina.
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Journal Reference:
Fragkiskos Papadopoulos, Maksim Kitsak, M. Ángeles Serrano, Marián Boguñá, Dmitri Krioukov. Popularity versus similarity in growing networks. Nature, 2012; DOI: 10.1038/nature11459Note: 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.
Saturday, August 18, 2012
Quantum computers could help search engines keep up with the Internet's growth
Behind the scenes, a lot of math goes into figuring out exactly what qualifies as most relevant web page for your search. Google, for example, uses a page ranking algorithm that is rumored to be the largest numerical calculation carried out anywhere in the world. With the web constantly expanding, researchers at USC have proposed -- and demonstrated the feasibility -- of using quantum computers to speed up that process.
"This work is about trying to speed up the way we search on the web," said Daniel Lidar, corresponding author of a paper on the research that appeared in the journal Physical Review Letters on June 4.
As the Internet continues to grow, the time and resources needed to run the calculation -- which is done daily -- grow with it, Lidar said.
Lidar, who holds appointments at the USC Viterbi School of Engineering and the USC Dornsife College of Letters, Arts and Sciences, worked with colleagues Paolo Zanardi of USC Dornsife and first author Silvano Garnerone, formerly a postdoctoral researcher at USC and now of the University of Waterloo, to see whether quantum computing could be used to run the Google algorithm faster.
As opposed to traditional computer bits, which can encode distinctly either a one or a zero, quantum computers use quantum bits or "qubits," which can encode a one and a zero at the same time. This property, called superposition, some day will allow quantum computers to perform certain calculations much faster than traditional computers.
Currently, there isn't a quantum computer in the world anywhere near large enough to run Google's page ranking algorithm for the entire web. To simulate how a quantum computer might perform, the researchers generated models of the web that simulated a few thousand web pages.
The simulation showed that a quantum computer could, in principle, return the ranking of the most important pages in the web faster than traditional computers, and that this quantum speedup would improve the more pages needed to be ranked. Further, the researchers showed that to simply determine whether the web's page rankings should be updated, a quantum computer would be able to spit out a yes-or-no answer exponentially faster than a traditional computer.
This research was funded by number of sources, including the National Science Foundation, the NASA Ames Research Center, the Lockheed Martin Corporation University Research Initiative program, and a Google faculty research award to Lidar.
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Story Source:
The above story is reprinted from materials provided by University of Southern California.
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Journal Reference:
Silvano Garnerone, Paolo Zanardi, Daniel Lidar. Adiabatic Quantum Algorithm for Search Engine Ranking. Physical Review Letters, 2012; 108 (23) DOI: 10.1103/PhysRevLett.108.230506Note: 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.
Sunday, July 15, 2012
Quantum computers could help search engines keep up with the Internet's growth
Behind the scenes, a lot of math goes into figuring out exactly what qualifies as most relevant web page for your search. Google, for example, uses a page ranking algorithm that is rumored to be the largest numerical calculation carried out anywhere in the world. With the web constantly expanding, researchers at USC have proposed -- and demonstrated the feasibility -- of using quantum computers to speed up that process.
"This work is about trying to speed up the way we search on the web," said Daniel Lidar, corresponding author of a paper on the research that appeared in the journal Physical Review Letters on June 4.
As the Internet continues to grow, the time and resources needed to run the calculation -- which is done daily -- grow with it, Lidar said.
Lidar, who holds appointments at the USC Viterbi School of Engineering and the USC Dornsife College of Letters, Arts and Sciences, worked with colleagues Paolo Zanardi of USC Dornsife and first author Silvano Garnerone, formerly a postdoctoral researcher at USC and now of the University of Waterloo, to see whether quantum computing could be used to run the Google algorithm faster.
As opposed to traditional computer bits, which can encode distinctly either a one or a zero, quantum computers use quantum bits or "qubits," which can encode a one and a zero at the same time. This property, called superposition, some day will allow quantum computers to perform certain calculations much faster than traditional computers.
Currently, there isn't a quantum computer in the world anywhere near large enough to run Google's page ranking algorithm for the entire web. To simulate how a quantum computer might perform, the researchers generated models of the web that simulated a few thousand web pages.
The simulation showed that a quantum computer could, in principle, return the ranking of the most important pages in the web faster than traditional computers, and that this quantum speedup would improve the more pages needed to be ranked. Further, the researchers showed that to simply determine whether the web's page rankings should be updated, a quantum computer would be able to spit out a yes-or-no answer exponentially faster than a traditional computer.
This research was funded by number of sources, including the National Science Foundation, the NASA Ames Research Center, the Lockheed Martin Corporation University Research Initiative program, and a Google faculty research award to Lidar.
Share this story on Facebook, Twitter, and Google:Other social bookmarking and sharing tools:
Story Source:
The above story is reprinted from materials provided by University of Southern California.
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Journal Reference:
Silvano Garnerone, Paolo Zanardi, Daniel Lidar. Adiabatic Quantum Algorithm for Search Engine Ranking. Physical Review Letters, 2012; 108 (23) DOI: 10.1103/PhysRevLett.108.230506Note: 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.