Computer Science

Nothing unusual about 'the long peace' since WWII

Anonymous — Mon, 26 Feb 2018 07:00:00 +0000

Nothing unusual about 'the long peace' since WWII Anonymous (not verified) Mon, 02/26/2018 - 00:00

Jenna Marshall

Since the end of World War II, few violent conflicts have erupted between major powers. Scholars have come to call this 73-year period “the long peace.” But is this stretch of relative calm truly unusual in modern human history – and evidence that peace-keeping efforts are working? Or is it a cyclical peace, destined to be broken, with few lessons for preventing interstate conflict?

A new analysis by Aaron Clauset, an assistant professor of computer science at the University of Colorado at �鶹ӰԺ and the BioFrontiers Institute and an external faculty member of the Santa Fe Institute, aims to answer that long-standing question using novel statistical techniques to tease out how the “long peace” stacks up against historical trends of calm and conflict.

“There’s been a debate among people who think about conflict and war, policymakers and researchers, whether or not the pattern since World War II represents a trend,” Clauset says. Resolving this debate “is important because it shapes how we think about peace,” he adds.

Determining whether we are truly in the midst of a prolonged period of peace can help us understand what is an effective deterrent to war and what is not. If this really is a “long peace,” we can then examine why — and identify the mechanisms that have contributed to that peace. But if it’s not an anomaly, we may need to use caution in ascribing the current calm to particular policies or actions.

Using data on interstate conflicts worldwide between 1823 and 2003, Clauset looked for trends in the magnitude of those conflicts and the years between them, and then used that information to create models to determine the plausibility of a trend toward peace since World War II. “The tools, the framework and the models we built in this paper haven’t been used before, and they allow us to distinguish trends from fluctuations,” Clauset says.

What he found is that this prolonged period of peace is not so unusual. “The results of the study are that at least statistically speaking, the efforts to create peace have not changed the frequency of war,” Clauset says. “These periods of peace are relatively common. It doesn’t appear that the rules that generate war have changed.”

Between 1823 and 1939, there were 19 large wars, and a major conflict occurred about every 6.2 years. Then came an especially violent period: Between 1914 and 1939, which encompasses the onsets of the first and second world wars, 10 large wars erupted — about one every 2.7 years. In contrast, during the long peace of the 1940–2003 post-war period, there were only 5 large wars — about one every 12.8 years. So essentially, the long peace “has simply balanced the books,” counterbalancing the “great violence” of the early to mid 20th century, Clauset writes.

That’s not to say, however, that the current calm is insignificant. “This fact does not detract from the importance of the long peace, or the proposed mechanisms that explain it,” such as the spread of democracy and international diplomacy, he writes in the paper. “However, the models indicate that the post-war pattern of peace would need to endure at least another 100–140 years to become a statistically significant trend.”

Clauset compares this to flipping a coin over a period of time. “If I’m seeing a low number of heads out into the future, how long will the coins need to flip before the pattern really looks different? At what point does this pattern start to look unusual?”

Clauset says he is hopeful that the study will encourage a rethink of “the long peace.”

“I hope it will encourage caution,” Clauset says. “It’s a worthwhile exercise to check our assumptions about whether there’s a real trend or not.”

Part of the reason we tend to overstate the significance of the lack of major interstate conflict since World War II may be because of a human tendency to overestimate our ability to understand complexity, he notes in the paper. “Human agency certainly plays a critical role in shaping shorter-term dynamics and specific events in the history of interstate wars,” he writes. “But, the distributed and changing nature of the international system evidently moderates the impact that individuals or coalitions can have on longer-term and larger-scale system dynamics.

The research was supported by the One Earth Future Foundation, whose mission is to catalyze systems that eliminate the root causes of war.

Read the paper, “Trends and Fluctuations in the Severity of Interstate Wars," in Science Advances (February 21, 2018)

Read the article, "Are we in the middle of a long peace—or on the brink of a major war?" in Science (February 21, 2018)

Read the article, "War may be closer than we think," in Pacific Standard (February 23, 2018)

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Scant Evidence of Power Laws Found in Real-World Networks

Anonymous — Thu, 15 Feb 2018 07:00:00 +0000

Scant Evidence of Power Laws Found in Real-World Networks Anonymous (not verified) Thu, 02/15/2018 - 00:00

Erica Klarreich

A paper posted online last month has reignited a debate about one of the oldest, most startling claims in the modern era of network science: the proposition that most complex networks in the real world — from the World Wide Web to interacting proteins in a cell — are “scale-free.” Roughly speaking, that means that a few of their nodes should have many more connections than others, following a mathematical formula called a power law, so that there’s no one scale that characterizes the network.

Purely random networks do not obey power laws, so when the early proponents of the scale-free paradigm started seeing power laws in real-world networks in the late 1990s, they viewed them as evidence of a universal organizing principle underlying the formation of these diverse networks. The architecture of scale-freeness, researchers argued, could provide insight into fundamental questions such as how likely a virus is to cause an epidemic, or how easily hackers can disable a network.

Over the past two decades, an avalanche of papers has asserted the scale-freeness of hundreds of real-world networks. In 2002, Albert-László Barabási — a physicist-turned-network scientist who pioneered the scale-free networks paradigm — wrote a book for a general audience, Linked, in which he asserted that power laws are ubiquitous in complex networks.

Real-world networks exhibit a rich structural diversity that will likely require new ideas and mechanisms to explain.
Anna Broido and Aaron Clauset

“Amazingly simple and far-reaching natural laws govern the structure and evolution of all the complex networks that surround us,” wrote Barabási (who is now at Northeastern University in Boston) in Linked. He later added: “Uncovering and explaining these laws has been a fascinating roller coaster ride during which we have learned more about our complex, interconnected world than was known in the last hundred years.”

But over the years, other researchers have questioned both the pervasiveness of scale-freeness and the extent to which the paradigm illuminates the structure of specific networks. Now, the new paper reports that few real-world networks show convincing evidence of scale-freeness.

In a statistical analysis of nearly 1,000 networks drawn from biology, the social sciences, technology and other domains, researchers found that only about 4 percent of the networks (such as certain metabolic networks in cells) passed the paper’s strongest tests. And for 67 percent of the networks, including Facebook friendship networks, food webs and water distribution networks, the statistical tests rejected a power law as a plausible description of the network’s structure.

“These results undermine the universality of scale-free networks and reveal that real-world networks exhibit a rich structural diversity that will likely require new ideas and mechanisms to explain,” wrote the study’s authors, Anna Broido and Aaron Clauset of the University of Colorado, �鶹ӰԺ.

Network scientists agree, by and large, that the paper’s analysis is statistically sound. But when it comes to interpreting its findings, the paper seems to be functioning like a Rorschach test, in which both proponents and critics of the scale-free paradigm see what they already believed to be true. Much of the discussion has played out in vigorous Twitter debates.

Supporters of the scale-free viewpoint, many of whom came to network science by way of physics, argue that scale-freeness is intended as an idealized model, not something that precisely captures the behavior of real-world networks. Many of the most important properties of scale-free networks, they say, also hold for a broader class called “heavy-tailed networks” to which many real-world networks may belong (these are networks that have significantly more highly connected hubs than a random network has, but don’t necessarily obey a strict power law).

Critics object that terms like “scale-free” and “heavy-tailed” are bandied about in the network science literature in such vague and inconsistent ways as to make the subject’s central claims unfalsifiable.

The new paper “was an attempt to take a data-driven approach to sort of clean up this question,” Clauset said.

Network science is a young discipline — most of its papers date to the last 20 years — and the contentiousness surrounding the paper and the very vocabulary of scale-freeness stems from the field’s immaturity, said Mason Porter, a mathematician and network scientist at the University of California, Los Angeles. Network science, he said, is “still kind of in the Wild West.”

A Universal Law?

Many networks, from perfectly ordered lattices to purely random networks, do have a characteristic scale. In a two-dimensional square lattice, for instance, every node is connected to exactly four other nodes (so mathematicians say the node’s “degree” is four). In a random network, in which each pair of nodes has some constant probability of being connected to each other, different nodes can have different degrees, but these degrees nevertheless cluster fairly close to the average. The distribution of degrees is shaped roughly like a bell curve, and nodes with a disproportionately large number of links essentially never occur, just as the distribution of people’s heights is clustered in the 5- to 6-foot range and no one is a million (or even 10) feet tall.

But when a team led by Barabási examined a sample of the World Wide Web in 1998, it saw something very different: some web pages, such as the Google and Yahoo home pages, were linked to vastly more often than others. When the researchers plotted a histogram of the nodes’ degrees, it appeared to follow the shape of a power law, meaning that the probability that a given node had degree k was proportional to 1/kraised to a power. (In the case of incoming links in the World Wide Web, this power was approximately 2, the team reported.)

In a power law distribution, there is no characteristic scale (thus the name “scale-free”). A power law has no peak — it simply decreases for higher degrees, but relatively slowly, and if you zoom in on different sections of its graph, they look self-similar. As a result, while most nodes still have low degree, hubs with an enormous number of links do appear in small quantities, at every scale.

The scale-free paradigm in networks emerged at a historical moment when power laws had taken on an outsize role in statistical physics. In the 1960s and 1970s they had played a key part in universal laws that underlie phase transitions in a wide range of physical systems, a finding that earned Kenneth Wilson the 1982 Nobel Prize in physics. Soon after, power laws formed the core of two other paradigms that swept across the statistical physics world: fractals, and a theory about organization in nature called self-organized criticality.

By the time Barabási was turning his attention to networks in the mid-1990s, statistical physicists were primed to see power laws everywhere, said Steven Strogatz, a mathematician at Cornell University (and a member of Quanta’s advisory board). In physics, he said, there’s a “power law religion.”

There was a bandwagon effect in which people were doing stuff rather indiscriminately.
Mason Porter

Barabási’s team published its findings in Nature in 1999; a month later, Barabási and his then-graduate student Réka Albert (now a network scientist at Pennsylvania State University) wrote in Science, in a paper that has since been cited more than 30,000 times, that power laws describe the structure not just of the World Wide Web but also of many other networks, including the collaboration network of movie actors, the electrical power grid of the Western United States, and the citation network of scientific papers. Most complex networks, Barabási asserted a few years later in Linked, obey a power law, whose exponent is usually between 2 and 3.

A simple mechanism called “preferential attachment,” Albert and Barabási argued, explains why these power laws appear: When a new node joins a network, it is more likely to connect to a conspicuous, high-degree node than an obscure, low-degree node. In other words, the rich get richer and the hubs get hubbier.

Scale-free networks, Barabási’s team wrote in the July 27, 2000, issue of Nature, have some key properties that distinguish them from other networks: They are simultaneously robust against failure of most of the nodes and vulnerable to targeted attacks against the hubs. The cover of Nature trumpeted this last property as the “Achilles’ heel of the internet” (a characterization that has since been roundly disputedby internet experts).

Barabási’s work electrified many mathematicians, physicists and other scientists, and was instrumental in launching the modern field of network science. It unleashed a torrent of papers asserting that one real-world network after another was scale-free — a sort of preferential attachment in which Barabási’s early papers became the hubs. “There was a bandwagon effect in which people were doing stuff rather indiscriminately,” Porter said. The excitement spilled over into the popular press, with talk of universal laws of nature and cover stories in Science, New Scientist and other magazines.

From the beginning, though, the scale-free paradigm also attracted pushback. Critics pointed out that preferential attachment is far from the only mechanism that can give rise to power laws, and that networks with the same power law can have very different topologies. Some network scientists and domain experts cast doubt on the scale-freeness of specific networks such as power grids, metabolic networks and the physical internet.

Others objected to a lack of statistical rigor. When a power law is graphed on a “log-log plot” (in which the x– and y-axes have logarithmic scales) it becomes a straight line. So to decide whether a network was scale-free, many early researchers simply eyeballed a log-log plot of the network’s degrees. “We would even squint at the computer screen from an angle to get a better idea if a curve was straight or not,” recalled the network scientist Petter Holmeof Tokyo Institute of Technology in a blog post.

“There must be a thousand papers,” Clauset said, “in which people plot the degree distribution, put a line through it and say it’s scale-free without really doing the careful statistical work.”

In response to these criticisms, over the years some of the physicists studying scale-freeness shifted their focus to the broader class of heavy-tailed networks. Even so, a steady stream of papers continued to assert scale-freeness for a growing array of networks.

And the discussion was muddied by a lack of consistency, from one paper to another, about what “scale-free” actually meant. Was a scale-free network one that obeys a power law with an exponent between 2 and 3, or one in which this power law arises out of preferential attachment? Or was it just a network that obeys some power law, or follows a power law on some scales, or something even more impressionistic?

“The lack of precision of language is a constant frustration,” Porter said.

Clauset, who is active in outreach efforts, has found that many of the students he interacts with still think that the ubiquity of power laws is settled science. “I was struck by how much confusion there was in the upcoming generation of scientists about scale-free networks,” he said.

The evidence against scale-freeness was scattered across the literature, with most papers examining just a few networks at a time. Clauset was well-positioned to do something much more ambitious: His research group has spent the past few years curating a giant online compendium, the Colorado Index of Complex Networks (ICON), comprising more than 4,000 networks drawn from economics, biology, transportation and other domains.

“We wanted to treat the hypothesis as falsifiable, and then assess the evidence across all domains,” he said.

Sweeping Up the Dirt and Dust

To test the scale-free paradigm, Clauset and Broido, his graduate student, subjected nearly a thousand of the ICON networks to a series of increasingly strict statistical tests, designed to measure which (if any) of the definitions of scale-freeness could plausibly explain the network’s degree distribution. They also compared the power law to several other candidates, including an exponential distribution (which has a relatively thin tail) and a “log-normal” distribution (which has a heavier tail than an exponential distribution, but a lighter tail than a power law).

There is no general theory of networks.
Alessandro Vespignani

Broido and Clauset found that for about two-thirds of the networks, no power law fit well enough to plausibly explain the degree distribution. (That doesn’t mean the remaining one-third necessarily obey a power law — just that a power law was not ruled out.) And each of the other candidate distributions outperformed the power law on many networks, with the log normal beating the power law on 45 percent of the networks and essentially tying with it on another 43 percent.

Only about 4 percent of the networks satisfied Broido and Clauset’s strongest test, which requires, roughly speaking, that the power law should survive their goodness-of-fit test, have an exponent between 2 and 3, and beat the other four distributions.

For Barabási, these findings do not undermine the idea that scale-freeness underlies many or most complex networks. After all, he said, in real-world networks, a mechanism like preferential attachment won’t be the only thing going on — other processes will often nudge the network away from pure scale-freeness, making the network fail Broido and Clauset’s tests. Network scientists have already figured out how to correct for these other processes in dozens of networks, Barabási said.

“In the real world, there is dirt and dust, and this dirt and dust will be on your data,” said Alessandro Vespignani of Northeastern, another physicist-turned-network scientist. “You will never see the perfect power law.”

As an analogy, Barabási noted, a rock and a feather fall at very different speeds even though the law of gravitation says they should fall at the same speed. If you didn’t know about the effect of air resistance, he said, “you would conclude that gravitation is wrong.”

Clauset doesn’t find this analogy convincing. “I think it’s pretty common for physicists who are trained in statistical mechanics … to use these kinds of analogies for why their model shouldn’t be held to a very high standard.”

If you were to observe 1,000 falling objects instead of just a rock and a feather, Clauset said, a clear picture would emerge of how both gravity and air resistance work. But his and Broido’s analysis of nearly 1,000 networks has yielded no similar clarity. “It is reasonable to believe a fundamental phenomenon would require less customized detective work” than Barabási is calling for, Clauset wrote on Twitter.

“The tacit and common assumption that all networks are scale-free and it’s up to us to figure out how to see them that way — that sounds like a nonfalsifiable hypothesis,” he said.

If some of the networks rejected by the tests do involve a scale-free mechanism overlaid by other forces, then those forces must be quite strong, Clauset and Strogatz said. “Contrary to what we see in the case of gravity … where the dominant effects really are dominant and the smaller effects really are small perturbations, it looks like what’s going on with networks is that there isn’t a single dominant effect,” Strogatz said.

For Vespignani, the debate illustrates a gulf between the mindsets of physicists and statisticians, both of whom have valuable perspectives. Physicists are trying to be “the artists of approximation,” he said. “What we want to find is some organizing principle.”

The scale-free paradigm, Vespignani said, provides valuable intuition for how the broader class of heavy-tailed networks should behave. Many traits of scale-free networks, including their combination of robustness and vulnerability, are shared by heavy-tailed networks, he said, and so the important question is not whether a network is precisely scale-free but whether it has a heavy tail. “I thought the community was agreeing on that,” he said.

But Duncan Watts, a network scientist at Microsoft Research in New York City, objected on Twitter that this point of view “is really shifting the goal posts.” As with “scale-freeness,” he said, the term “heavy-tailed” is used in several different ways in the literature, and the two terms are sometimes conflated, making it hard to assess the various claims and evidence. The version of “heavy-tailed” that is close enough to “scale-free” for many properties to transfer over is not an especially broad class of networks, he said.

Scale-freeness “actually did mean something very clear once, and almost certainly that definition does not apply to very many things,” Watts said. But instead of network scientists going back and retracting the early claims, he said, “the claim just sort of slowly morphs to conform to all the evidence, while still maintaining its brand label surprise factor. That’s bad for science.”

Porter likes to joke that if people want to discuss something contentious, they should set aside U.S. politics and talk about power laws. But, he said, there’s a good reason these discussions are so fraught. “We have these arguments because the problems are hard and interesting.”

Clauset sees his work with Broido not as an attack but as a call to action to network scientists, to examine a more diverse set of possible mechanisms and degree distributions than they have been doing. “Perhaps we should consider new ideas, as opposed to trying to force old ideas to fit,” he said.

Vespignani agrees that there is work to be done. “If you ask me, ‘Do you all agree what is the truth of the field?’ Well, there is no truth yet,” he said. “There is no general theory of networks.”

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Accelerating innovation: 7 research teams receive commercialization grants

Anonymous — Thu, 11 May 2017 06:00:00 +0000

Accelerating innovation: 7 research teams receive commercialization grants Anonymous (not verified) Thu, 05/11/2017 - 00:00

CUBT - CU �鶹ӰԺ Today

Seven CU �鶹ӰԺ research teams have been selected to receive grants for the development of commercially-promising technologies. A total of 21 applications were reviewed by a panel of external judges made up of entrepreneurs, investors, business executives and intellectual property attorneys from around the country.

Six of the seven awards are funded by the Advanced Industries Accelerator (AIA) program, administered by the Colorado Office of Economic Development and International Trade with the support of Colorado Advanced Industry trade associations and the state’s Economic Development Commission. AIA program funds are matched at a 25 percent rate by the CU �鶹ӰԺ Technology Transfer Office (TTO), with internal funding provided by Chancellor Philip DiStefano’s office, derived from TTO licensing revenues. The remaining award is fully funded by the university.

The AIA program is designed to identify technologies from research institutions and connect them to the private sector, where they can be developed into commercial products.

The seven awardees are:

Next-generation autonomous manipulation for collaborative robotics (Nikolaus Correll, Computer Science): The field of mobile robots is taking off. Robots are increasingly used in warehouses, factories, hotels and homes, where they enable telepresence, deliver goods, or clean, for example. Robotic Materials Inc. has licensed the key technology that will allow equipping robots with the sense of touch, dramatically increasing the value they can create for users. CU

Development of a next-generation, highly thermostable human papillomavirus vaccine (Robert Garcea, Molecular, Cellular and Developmental Biology; Ted Randolph, Chemical and Biological Engineering): The goal of this proposal is to combine new technologies to formulate vaccines—applied specifically here to new HPV vaccines—that are both potent and stable without refrigeration for extended periods of time. This will circumvent impediments to current vaccine delivery by eliminating the need for a “cold chain” (appropriate refrigeration) that is frequently absent in less developed areas of the world, where vaccine needs are great.

Enhancement of thermal atomic layer etching for anisotropic etching (Steven George, Chemistry): Anisotropic etching is critical for the fabrication of three-dimensional nanostructures, such as those used in advanced semiconductor devices. George’s team will build on its discovery of new thermal reactions for atomic layer etching (ALE)—the controlled removal of material at the atomic level. This new project will develop methods to obtain anisotropic etching, where material is removed with a preferred directionality.

Miniature laser for multiphoton microscopy (Juliet Gopinath, Electrical Engineering): The goal of this project is to develop a miniature diode laser system that can eventually be used for the study and treatment of neurological disease, photodynamic therapy and endoscopy. The technology will produce short pulse sources in the near-infrared range, enabling deep penetration in tissue.

Carbonate products analyzed by XRD crystal phase analysis (Jason Ren)

Direct energy and resource recovery from carbon dioxide and wastewater (Jason Ren, Civil Engineering): The Ren lab will work with HySummit—a company dedicated to producing carbon-negative water, hydrogen and high-value chemicals—to develop scalable systems to convert beverage wastewater and CO2 into valuable chemicals, such as carbonate and hydrogen.

Preclinical assessment of drug for colorectal cancer (Tin Tin Su, Molecular, Cellular, and Developmental Biology): Tin Tin Su’s team is studying a first-in-class drug candidate that works by depriving cancer cells of proteins they need to survive and grow. This grant will enable the team to obtain proof-of-concept data in preclinical models of colorectal cancer, where therapy options remain limited.

Continuous magnesium metal production in a novel reactor (Aaron Palumbo and Al Weimer, Chemical and Biological Engineering): Magnesium metal is the lightest structural metal used to make electronic devices ultra-portable and vehicles more fuel efficient. However, current production methods are extremely energy intensive. Palumbo and Weimer’s project will focus on the construction of a continuous, high-temperature extraction system that can reduce processing energy consumption by 60 percent, translating into a cost-reduction of at least 20 percent.

Five of the seven winners have already formed startup companies around their technologies: Robotic Materials (Correll); VitriVax (Garcea, Randolph); HySummit (Ren); SuviCa (Su); and Big Blue (Weimer, Palumbo).

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The possibilities and limits of using data to predict scientific discoveries

Anonymous — Fri, 03 Feb 2017 18:51:49 +0000

The possibilities and limits of using data to predict scientific discoveries Anonymous (not verified) Fri, 02/03/2017 - 11:51

CUBT - CU �鶹ӰԺ Today

Amidst the vast and varied ecosystem of modern science, the emerging interdisciplinary field known as the “science of science” is exploring a difficult, but provocative, question: In the age of data science, are future discoveries now predictable?

In an article published this week in the journal Science, CU �鶹ӰԺ researcher Aaron Clauset and co-authors Daniel Larremore (Santa Fe Institute) and Roberta Sinatra (Central European University) examine the possibilities and limits of using massive data sets of scientific papers and information on scientific careers to study the social processes that underlie discoveries.

“There is more interest than ever in quantifying scientific behavior,” said Aaron Clauset, an assistant professor in CU �鶹ӰԺ’s Department of Computer Science and a faculty member in the BioFrontiers Institute. “The question is: Can we use the abundant data on the scientific process in order to make better predictions about scientific discoveries, which could improve funding decisions, peer review and hiring decisions?”

Historically, scientific discoveries have fallen on a spectrum between highly expected (such as the Higgs boson, which evidence pointed to years in advance) and entirely unexpected (such as penicillin, which arrived with minimal preceding research). Predicting such advances has value to scientists (when choosing a research field), funding agencies (who want to allocate dollars effectively), hiring committees (who want to hire successful faculty) and taxpayers (who fund a large percentage of research projects).

The recent proliferation of bibliographic databases such as Google Scholar, Web of Science, PubMed, ORCID and others has given researchers new tools by which to examine various aspects of the scientific community as a whole, such as the number of citations a given article receives or how many journal articles a given researcher publishes. But, do such metrics make some kinds of discoveries easier to predict?

Feedback loops

One problem with using such data to make predictions is the likelihood that the scientific community and the various incentives for scientists may currently be structured in a way that creates self-reinforcing feedback loops in which future opportunity depends on being lucky, undermining the potential for other less-heralded projects to advance science.

“We tend to reward and reinvest in people and subjects that have paid off in the past, but there’s no guarantee they will continue to do so. This can create a kind of purifying selection,” said Clauset, who is also an external faculty member at the Santa Fe Institute. “Ecology teaches us that the most robust systems in the face of uncertainty are diverse systems. We may be killing the golden goose of scientific discovery very slowly by focusing on minutiae at the expense of variety.”

Clauset’s data also questions the conventional academic narrative that scientists achieve an early productivity peak followed by a long and slow decline. In a related paper published in December 2016, he and his co-authors analyzed over 200,000 publications from 2,453 tenure-track faculty in all 205 PhD-granting computer science departments in the U.S. and Canada. They found the conventional pattern accurately described only one-third of faculty while the remaining two-thirds exhibited a wide variety of productivity patterns over the course of their careers.

Sleeping beauties

Another insight into the unpredictability of scientific advances comes from so-called “sleeping beauties.” While bibliographic data illuminate that some aspects of scientific impact are predictable, the broad existence of “sleeping beauty” papers, which lay dormant for years before a sudden uptick in relevance, implies that some aspects of discovery may be fundamentally unpredictable. A notable example is a now-famous 1935 Albert Einstein paper on quantum mechanics that was only modestly cited for several decades before fairly recently becoming one of the most important papers in quantum mechanics.

“This suggests that there’s another scale to consider, one in which we need to zoom out even farther to understand how these various scientific fields and subfields are interacting with one another,” said Clauset.

The article also states that while publication data is useful in some ways, citations are fundamentally lagging indicators, which only look backward at the past, and thus may have limited utility for predicting the future.

Looking forward, Clauset and his co-authors suggest that better predictions could be made using data sets on scientific preprints, workshop papers, conference presentations and rejected grant proposals. Such databases—should they ever become available—might provide additional trends and insights that are not being captured currently by better illustrating how the frontier of scientific discovery is moving.

Overall, the authors state, the limits of data in predicting future advances point to the importance of maintaining a wide-ranging scientific community.

“We would be wise to hedge our bets by building a diverse ecosystem of scientists and approaches to science rather than focus on predicting individual discoveries,” said Clauset.

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Five Questions about Network Science

Anonymous — Sun, 03 Jul 2016 06:00:00 +0000

Five Questions about Network Science Anonymous (not verified) Sun, 07/03/2016 - 00:00

BioFrontiers

Five Questions for Aaron Clauset

Aaron Clauset is an assistant professor of computer science at CU-�鶹ӰԺ and a faculty member of the BioFrontiers Institute. He recently accepted the 2016 Erdős-Rényi Prize in Network Science, which is an international prize awarded annually to a researcher under 40 who has made fundamental contributions to the advancement of network science. Network science is an interdisciplinary field dedicated to understanding the structure and function of networks in all domains, from social networks to biological networks to technological networks. Clauset answers five questions about how he uses network science in his research:

How did you become interested in network science?

I began studying networks during my doctoral work at the University of New Mexico. Although interest in networks stretches back to at least the early part of the 20^th century in sociology, network science as a modern interdisciplinary field was born only recently, as a result of the computer revolution. These technologies now allow scientists to record and study immense volumes of data on interactions in nearly every scientific field, from the way people interact with each other either online or offline, to how genes regulate each other or how food webs are structured, to how the structure of a network shapes how information flows across it. A long-running theme in my research, which started during my doctoral work, is the development of advanced algorithms that can identify subtle organizational patterns in networks, and use these patterns to make predictions.

What do you hope to do with your discoveries in network science?

Increasingly, networks are a powerful tool for making discoveries about the structure and function of complex systems in scientific domains, for example, in the social or biological sciences. Part of my research focuses on using network techniques to answer specific scientific questions. But I’m also hope to identify the basic organizing principles of networks that span different scientific domains. Insights from studying networks in one domain, such as human social networks, can often help us to better understand some aspects of networks in other domains, such as molecular interaction networks, or to develop more powerful tools for analyzing them.

Studying network science is allowing you to look at a broad range of subjects: faculty hiring, sports scoring, social networks like Facebook, and malaria virulence genetics. What have you discovered by researching the networks of so many things?

It’s definitely true that my research on networks has taken me across a pretty wide variety of topics. But they are all interesting! What I’ve found is that there are surprising and evocative commonalities across many different systems, if one looks carefully. It’s exciting to discover these, and to think about how their appearance across different systems may point to more simple processes that underlay the observable complexity in the world.

What are the ingredients of a good network scientist?

It’s difficult to be a good network scientist today without being a good statistician and a good computer scientist. Network data is inherently more messy and difficult to understand than traditional types of data. So, making progress, either in developing new algorithms for analyzing the data or in applying network techniques to test scientific hypotheses, requires some technical skill. But, I also think it helps to have a good imagination and a healthy sense of skepticism. Networks are highly non-intuitive objects. Imagination can help you identify new ideas about how they behave, and skepticism will keep you keep from falling in love with your theories.

Do you tend to apply network science to other areas of your life? And how has it enriched your life?

Networks have shaped how I think about many things. For instance, I think about the spread of information differently now. Personal privacy is basically a network effect, and that places pretty strong limits on what any individual can do to maintain it. Knowing this has probably led me to be a bit more circumspect about posting information online. But more broadly, networks are everywhere in life, and I think taking a “network perspective” can help shed new light and new understanding on a many things, both mundane and not.

You can find Aaron Clauset on Twitter @aaronclauset.

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IQ Bio Blog: Workshop on Genomics

Anonymous — Wed, 04 Apr 2012 06:00:00 +0000

IQ Bio Blog: Workshop on Genomics Anonymous (not verified) Wed, 04/04/2012 - 00:00

Daniel McDonald

This past January, I had an amazing opportunity to be an instructor at the Workshop on Genomics, and the associated advanced topic sessions, in the Czech Republic. The workshop was hosted in Cesky Krumlov, a UNESCO World Heritage site considered the best preserved medieval town in Europe. The workshop is a two week, intensive crash course on the latest and greatest tools used in genomics, with courses and lectures taught by the developers and researchers sitting at the forefront of genomics research.

For instance, a few of the sessions covered during this past workshop included lectures and tutorials on genome assembly with ALLPATHS-LG and velvet, transcriptome assembly with Trinity and variant calling with Stacks (and that's only scratching the surface). The advanced topic sessions covered R/Bioconductor and Python/BioPython. Students even got familiarity with Amazon's EC2 and cloud computing during the two weeks.

Beyond learning about tools, the workshop offered a fantastic chance to interact with peers within and across disciplines including numerous opportunities for students and invited faculty alike to interact and discuss science over meals and Budvar (the original Budweiser). Furthermore, the diversity of the workshop was immense: over 70 students ranging from graduate level to established PIs, with countless nationalities and languages represented. One of the greatest difficulties as an instructor was becoming familiar with the sheer variety of keyboards!

As an instructor for the workshop, my role was geared towards the setup and testing of tutorials and providing support during the tutorials. The majority of the students taking part did not have a computational background. Initially, many of the questions surrounded a Linux shell environment, but the students were quick to pick up on the common, and powerful, command-line-oriented tasks. During the course of the workshop, a few students began inquiring about Shell programming. We okayed it with the organizer to spawn a few impromptu three-hour practical Python programming sessions that were very well received by the students. For many, it was their first time programming which led well into the advanced topic sessions.

Genomics in the 21st century is becoming increasingly data-driven with the development of next-generation sequencing technologies. Learning how to make sense from sequence within this tsunami of data requires the development of computational skills, knowledge of the tools available and resource requirements. As a current IQ Biology student, I'm enrolled in courses teaching the theory behind many of these tools. The workshop ,in turn, offers the practical hands-on knowledge of how to use and maximize the effectiveness of the techniques; an awesome combination.

If all works out, the workshop will be coming to Biofrontiers the summer of 2013. Hope to see you there!

Daniel McDonald (below in the orange jacket) is currently completing his first year of graduate studies in the IQ Biology Interdisciplinary Quantitative Biology program.

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Chasing the elegant solution

Anonymous — Tue, 22 Nov 2011 07:00:00 +0000

Chasing the elegant solution Anonymous (not verified) Tue, 11/22/2011 - 00:00

BioFrontiers

Chasing the elegant solution

Stereotypes tell us that computer scientists are all about hardware, software and servers. They are all about sifting through crowded lines of code in the dim basement of the engineering school. If this is what you believe about computer scientists, Aaron Clauset is about to burst that misconception. An assistant professor in computer science and a faculty member of the Biofrontiers Institute, he is more interested in using computational tools to understand how complex biological and social systems work.

After graduating with a bachelor’s degree in physics, Clauset was impressed by the computer’s ability to simulate the real world and make predictions that could be tested in the laboratory. In addition, computers could model things that couldn’t be done on live subjects, or over impossibly long periods of time. He went on to get his Ph.D. in computer science and now develops computational tools for modeling phenomena in biological, technological and social systems.

“I saw an opportunity to use the computer as a virtual laboratory,” he says. “Although I came out of the natural sciences, I was fascinated by the complexity of messy systems like biological evolution and human behavior.”

Clauset’s timing couldn’t have been better. The complexity of biology came into focus in 2001 when the Human Genome Project was completed. More than 1,000 scientists around the world sifted through three billion bits of data in human cells to map the ordering of all human genes. This breakthrough was just the beginning of our understanding of how genomes actually build life.

"In many areas, we're practically swimming in data and it can be difficult to turn this mountain of information into actual scientific understanding," says Clauset. "The traditional approach is to drill down and isolate things from each other. But, this leaves out the interactions between those pieces that make a complex system work. So, I try to 'drill up' to get a wider view of how the pieces fit together. This often requires developing completely new mathematical and computational techniques to figure out what's important and what's not."

Clauset looks at data on a macro level, seeking patterns. For example, biologists have extensively studied how species change size as they evolve. The sizes of fish species around the world are relatively small when averaged statistically across all fish species. But the largest members of the fish family, say a whale shark, are large to an extreme—sometimes thousands or millions of times bigger than anything in their taxonomic family.

Clauset developed a deceptively simple computational model that could predict this seemingly quantum leap of evolution. First, he put a strict limit on how small a species could become and still survive, and then slowly increased the extinction rate with the size of the species. Otherwise, Darwin’s rules held: a species inherits its size from its parent, but with a small amount of variability. Surprisingly, he ignored some of the more traditional rules of evolution and ecology, filtering out species interactions and the dynamics of growing populations.

Then he let evolution unfold in his computer, over millions of years, to show that a species tendency to grow larger is offset by its tendency to become extinct more quickly. In other words, living large as a species is risky and tends to earn you a shorter time on Earth. By recreating evolution in his computer he was able to identify patterns in species size over time.

To check his work, Clauset used fossil data from extinct mammal species going back 90 million years. He was able to show that his “virtual evolution” calculations accurately reproduced both the diversity of 4,000 living species and the fossil record patterns over the past 60 million years. Clauset’s solution was an elegant one: it stripped away enough complexity to keep the computational model very compact, but was able to accurately predict something as large as global diversity of mammal sizes.

His work on this elegant solution crossed into several academic areas: biology, paleontology, mathematics and physics, in addition to computer science. A multi-discipline approach suits Clauset’s need to roam across academic subjects, and his ability to grasp the larger picture.

“You have to be interested in the synthesis of ideas to truly be interdisciplinary,” says Clauset. “To build elegant solutions around how parts interact to create a working complex system almost always requires combining ideas from multiple disciplines.”

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