In the last 15 years, the collective motion of large numbers of self-propelled objects has become an increasingly active area of research. The examples of such collective motion abound: flocks of birds, schools of fish, swarms of insects, herds of animals etc. Swarming of living creatures is believed to be critical for the population survival under harsh conditions.
Abstract excerpt: In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records.
Our findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy.
Abstract excerpt: In many types of network, the relationship between structure and function is of great significance. We are particularly interested in community structures, which arise in a wide variety of domains. We apply a simple oscillator model to networks with community structures and show that waves of regular oscillation are caused by synchronised clusters of nodes. Moreover, we show that such global oscillations may arise as a direct result of network topology.
From Abstract: We argue that network modularity reveals critical meso-scales that are probably common in populations, providing a powerful means of identifying fundamental scales for biology and for conservation strategies aimed at recovering imperilled species.
Abstract: We investigate the behaviour of the recently proposed Quantum PageRank algorithm, in large complex networks. We find that the algorithm is able to univocally reveal the underlying topology of the network and to identify and order the most relevant nodes. Furthermore, it is capable to clearly highlight the structure of secondary hubs and to resolve the degeneracy in importance of the low lying part of the list of rankings. The quantum algorithm displays an increased stability with respect to a variation of the damping parameter, present in the Google algorithm, and a more clearly pronounced power-law behaviour in the distribution of importance, as compared to the classical algorithm. We test the performance and confirm the listed features by applying it to real world examples from the WWW. Finally, we raise and partially address whether the increased sensitivity of the quantum algorithm persists under coordinated attacks in scale-free and random networks.
Research data comes in various forms and levels of significance. Finding the best way to share all of the results of a research project can be difficult, but new ways are constantly emerging. Scientists disseminate their work by writing and publishing scientific papers, but this finished product can conceal a wealth of effort and information. Behind the text and figures is the data itself, which has been recorded, analysed, interpreted and, eventually, summarized in graphs and images. The process is necessary, but it can mean that accessing the underlying data is not straightforward.
Abstract: The lack of predictability of citation-based measures frequently used to gauge impact, from impact factors to short-term citations, raises a fundamental question: Is there long-term predictability in citation patterns? Here, we derive a mechanistic model for the citation dynamics of individual papers, allowing us to collapse the citation histories of papers from different journals and disciplines into a single curve, indicating that all papers tend to follow the same universal temporal pattern. The observed patterns not only help us uncover basic mechanisms that govern scientific impact but also offer reliable measures of influence that may have potential policy implications.
An immediate and chronic concern for many of us is how the housing market influences the whole economy: surprisingly ants also have issues over the value of new homes, researchers from the University of Bristol have found.
Certain facts in mathematics feel as though they contain a kind of compressed power—they look innocuous and mild-mannered when you first meet them, but they're dazzling when you see them in action. One of the most compelling examples of such a fact is the Pigeonhole Principle.
Many complex networks show signs of modular structure, uncovered by community detection. Although many methods succeed in revealing various partitions, it remains difficult to detect at what scale some partition is significant.
In a new study, UCLA researchers analyzed the gene-expression profiles of more than 2,000 patients and were able to identify cancer-specific gene signatures for breast, lung, prostate and ovarian cancers. The study applied an innovative approach to gene-array analysis known as "surprisal analysis," which uses the principles of thermodynamics—the study of the relationship between different forms of energy—to understand cellular processes in cancer.
Abstract excerpt: We conduct a detailed investigation of correlations between real-time expressions of individuals made across the United States and a wide range of emotional, geographic, demographic, and health characteristics. Our results show how social media may potentially be used to estimate real-time levels and changes in population-scale measures such as obesity rates.
For the previous century, the accepted view was that once captured and stored in neural circuits in the brain, a memory could be retrieved but could not be rewritten. In that view, every time an experience is relived, it is the same, over and over.
Now, however, researchers understand that the process of recalling a memory actually changes it. “Each time you retrieve a memory it undergoes this storage process,”. That means the memory is in an unstable state, rewritten and remodeled every time it is retrieved.
From Abstract: Hallmarks of criticality, such as power-laws and scale invariance, have been empirically found in cortical-network dynamics and it has been conjectured that operating at criticality entails functional advantages, such as optimal computational capabilities, memory and large dynamical ranges. As critical behaviour requires a high degree of fine tuning to emerge, some type of self-tuning mechanism needs to be invoked. Here we show that, taking into account the complex hierarchical-modular architecture of cortical networks, the singular critical point is replaced by an extended critical-like region that corresponds—in the jargon of statistical mechanics—to a Griffiths phase.
Not quite! The above figure is the landscape of ~40,000 student submissions to the same programming assignment on Coursera's Machine Learningcourse. Nodes represent submissions and edges are drawn between syntactically similar submissions. Colors correspond to performance on a battery of unit tests (with red submissions passing all unit tests). In particular, clusters of similarly colored nodes correspond to multiple similar implementations that behaved in the same way (under unit tests).
** (For those curious, this particular programming assignment asked students to implement gradient descent for linear regression in Octave).
“If you tweet about your life, a new algorithm can identify your most significant events and assemble them into an accurate life history, say the computer scientists who built it (Algorithm Writes People's Life Histories Using Twitter”
Despite a rainy and cold day in Bucharest, tens of thousands of Romanians took to the streets for the fifth Sunday consecutively to protest against a draft bill passed by the government approving a cyanide-based open-pit mining project.
The global youth-led Romanian initiative is currently the largest environmental protest movement in Europe and the world and continues to be a model of non-violent resistance.
In a study published last week at PLOS ONE, scientists at the University of Pennsylvania examined the language used in 75,000 Facebook profiles. They found differences across ages, genders, and certain personality traits. This allowed the group, led by computer and information scientist H. Andrew Schwartz, to make predictions about the profile of each user.
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