Current-Induced One-Dimensional Diffusion of Co Adatoms on Graphene Nanoribbons.

One-dimensional diffusion of Co adatoms on graphene nanoribbons has been induced and investigated by means of scanning tunnelling microscopy (STM). To this end, the nanoribbons and the Co adatoms have been imaged before and after injecting current pulses into the nanoribbons, with the STM tip in direct contact with the ribbon. We observe current-induced motion of the Co atoms along the nanoribbons, which is approximately described by a distribution expected for a thermally activated one-dimensional random walk. This indicates that the nanoribbons reach temperatures far beyond 100 K, which is well above the temperature of the underlying Au substrate. This model system can be developed further for the study of electromigration at the single-atom level.

Using big data to map the relationship between time perspectives and economic outputs.

Recent studies have shown that population-level time perspectives can be approximated using "big data" on search engine queries, and that these indices, in turn, predict the per-capita Gross Domestic Product of countries. Although these findings seem to support Baumard's suggestion that affluence makes people more future-oriented, they also reveal a more complex relationship between time perspectives and economic outputs.

Quantifying the semantics of search behavior before stock market moves.

Technology is becoming deeply interwoven into the fabric of society. The Internet has become a central source of information for many people when making day-to-day decisions. Here, we present a method to mine the vast data Internet users create when searching for information online, to identify topics of interest before stock market moves. In an analysis of historic data from 2004 until 2012, we draw on records from the search engine Google and online encyclopedia Wikipedia as well as judgments from the service Amazon Mechanical Turk. We find evidence of links between Internet searches relating to politics or business and subsequent stock market moves. In particular, we find that an increase in search volume for these topics tends to precede stock market falls. We suggest that extensions of these analyses could offer insight into large-scale information flow before a range of real-world events.

Ballistic Transport in Graphene Antidot Lattices.

The bulk carrier mobility in graphene was shown to be enhanced in graphene-boron nitride heterostructures. However, nanopatterning graphene can add extra damage and drastically degrade the intrinsic properties by edge disorder. Here we show that graphene embedded into a heterostructure with hexagonal boron nitride (hBN) on both sides is protected during a nanopatterning step. In this way, we can prepare graphene-based antidot lattices where the high mobility is preserved. We report magnetotransport experiments in those antidot lattices with lattice periods down to 50 nm. We observe pronounced commensurability features stemming from ballistic orbits around one or several antidots. Due to the short lattice period in our samples, we can also explore the boundary between the classical and the quantum transport regime, as the Fermi wavelength of the electrons approaches the smallest length scale of the artificial potential.

Linking agent-based models and stochastic models of financial markets.

It is well-known that financial asset returns exhibit fat-tailed distributions and long-term memory. These empirical features are the main objectives of modeling efforts using (i) stochastic processes to quantitatively reproduce these features and (ii) agent-based simulations to understand the underlying microscopic interactions. After reviewing selected empirical and theoretical evidence documenting the behavior of traders, we construct an agent-based model to quantitatively demonstrate that "fat" tails in return distributions arise when traders share similar technical trading strategies and decisions. Extending our behavioral model to a stochastic model, we derive and explain a set of quantitative scaling relations of long-term memory from the empirical behavior of individual market participants. Our analysis provides a behavioral interpretation of the long-term memory of absolute and squared price returns: They are directly linked to the way investors evaluate their investments by applying technical strategies at different investment horizons, and this quantitative relationship is in agreement with empirical findings. Our approach provides a possible behavioral explanation for stochastic models for financial systems in general and provides a method to parameterize such models from market data rather than from statistical fitting.

Switching processes in financial markets.

For an intriguing variety of switching processes in nature, the underlying complex system abruptly changes from one state to another in a highly discontinuous fashion. Financial market fluctuations are characterized by many abrupt switchings creating upward trends and downward trends, on time scales ranging from macroscopic trends persisting for hundreds of days to microscopic trends persisting for a few minutes. The question arises whether these ubiquitous switching processes have quantifiable features independent of the time horizon studied. We find striking scale-free behavior of the transaction volume after each switching. Our findings can be interpreted as being consistent with time-dependent collective behavior of financial market participants. We test the possible universality of our result by performing a parallel analysis of fluctuations in time intervals between transactions. We suggest that the well known catastrophic bubbles that occur on large time scales--such as the most recent financial crisis--may not be outliers but single dramatic representatives caused by the formation of increasing and decreasing trends on time scales varying over nine orders of magnitude from very large down to very small.

Using big data to predict collective behavior in the real world.

Recent studies provide convincing evidence that data on online information gathering, alongside massive real-world datasets, can give new insights into real-world collective decision making and can even anticipate future actions. We argue that Bentley et al.'s timely account should consider the full breadth, and, above all, the predictive power of big data.

Faster indicators of chikungunya incidence using Google searches.

Chikungunya, a mosquito-borne disease, is a growing threat in Brazil, where over 640,000 cases have been reported since 2017. However, there are often long delays between diagnoses of chikungunya cases and their entry in the national monitoring system, leaving policymakers without the up-to-date case count statistics they need. In contrast, weekly data on Google searches for chikungunya is available with no delay. Here, we analyse whether Google search data can help improve rapid estimates of chikungunya case counts in Rio de Janeiro, Brazil. We build on a Bayesian approach suitable for data that is subject to long and varied delays, and find that including Google search data reduces both model error and uncertainty. These improvements are largest during epidemics, which are particularly important periods for policymakers. Including Google search data in chikungunya surveillance systems may therefore help policymakers respond to future epidemics more quickly.

Using aircraft location data to estimate current economic activity.

Aviation is a key sector of the economy, contributing at least 3% to gross domestic product (GDP) in the UK and the US. Currently, airline performance statistics are published with a three month delay. However, aircraft now broadcast their location in real-time using the Automated Dependent Surveillance Broadcast system (ADS-B). In this paper, we analyse a global dataset of flights since July 2016. We first show that it is possible to accurately estimate airline flight volumes using ADS-B data, which is available immediately. Next, we demonstrate that real-time knowledge of flight volumes can be a leading indicator for aviation's direct contribution to GDP in both the UK and the US. Using ADS-B data could therefore help move us towards real-time estimates of GDP, which would equip policymakers with the information to respond to shocks more quickly.

Happiness is Greater in More Scenic Locations.

Does spending time in beautiful settings boost people's happiness? The answer to this question has long remained elusive due to a paucity of large-scale data on environmental aesthetics and individual happiness. Here, we draw on two novel datasets: first, individual happiness data from the smartphone app, Mappiness, and second, crowdsourced ratings of the "scenicness" of photographs taken across England from the online game Scenic-Or-Not. We find that individuals are happier in more scenic locations, even when we account for a range of factors such as the activity the individual was engaged in at the time, weather conditions and the income of local inhabitants. Crucially, this relationship holds not only in natural environments, but in built-up areas too, even after controlling for the presence of green space. Our results provide evidence that the aesthetics of the environments that policymakers choose to build or demolish may have consequences for our everyday wellbeing.

Statistical analysis of financial returns for a multiagent order book model of asset trading.

We recently introduced a realistic order book model [T. Preis, Europhys. Lett. 75, 510 (2006)] which is able to generate the stylized facts of financial markets. We analyze this model in detail, explain the consequences of the use of different groups of traders, and focus on the foundation of a nontrivial Hurst exponent based on the introduction of a market trend. Our order book model supports the theoretical argument that a nontrivial Hurst exponent implies not necessarily long-term correlations. A coupling of the order placement depth to the market trend can produce fat tails, which can be described by a truncated Lévy distribution.

Using deep learning to quantify the beauty of outdoor places.

Beautiful outdoor locations are protected by governments and have recently been shown to be associated with better health. But what makes an outdoor space beautiful? Does a beautiful outdoor location differ from an outdoor location that is simply natural? Here, we explore whether ratings of over 200 000 images of Great Britain from the online game Scenic-Or-Not, combined with hundreds of image features extracted using the Places Convolutional Neural Network, might help us understand what beautiful outdoor spaces are composed of. We discover that, as well as natural features such as 'Coast', 'Mountain' and 'Canal Natural', man-made structures such as 'Tower', 'Castle' and 'Viaduct' lead to places being considered more scenic. Importantly, while scenes containing 'Trees' tend to rate highly, places containing more bland natural green features such as 'Grass' and 'Athletic Fields' are considered less scenic. We also find that a neural network can be trained to automatically identify scenic places, and that this network highlights both natural and built locations. Our findings demonstrate how online data combined with neural networks can provide a deeper understanding of what environments we might find beautiful and offer quantitative insights for policymakers charged with design and protection of our built and natural environments.

Quantifying the link between art and property prices in urban neighbourhoods.

Is there an association between art and changes in the economic conditions of urban neighbourhoods? While the popular media and policymakers commonly believe this to be the case, quantitative evidence remains lacking. Here, we use metadata of geotagged photographs uploaded to the popular image-sharing platform Flickr to quantify the presence of art in London neighbourhoods. We estimate the presence of art in neighbourhoods by determining the proportion of Flickr photographs which have the word 'art' attached. We compare this with the relative gain in residential property prices for each Inner London neighbourhood. We find that neighbourhoods which have a higher proportion of 'art' photographs also have greater relative gains in property prices. Our findings demonstrate how online data can be used to quantify aspects of the visual environment at scale and reveal new connections between the visual environment and crucial socio-economic measurements.

Quantifying the Search Behaviour of Different Demographics Using Google Correlate.

Vast records of our everyday interests and concerns are being generated by our frequent interactions with the Internet. Here, we investigate how the searches of Google users vary across U.S. states with different birth rates and infant mortality rates. We find that users in states with higher birth rates search for more information about pregnancy, while those in states with lower birth rates search for more information about cats. Similarly, we find that users in states with higher infant mortality rates search for more information about credit, loans and diseases. Our results provide evidence that Internet search data could offer new insight into the concerns of different demographics.

Tracking Protests Using Geotagged Flickr Photographs.

Recent years have witnessed waves of protests sweeping across countries and continents, in some cases resulting in political and governmental change. Much media attention has been focused on the increasing usage of social media to coordinate and provide instantly available reports on these protests. Here, we investigate whether it is possible to identify protest outbreaks through quantitative analysis of activity on the photo sharing site Flickr. We analyse 25 million photos uploaded to Flickr in 2013 across 244 countries and regions, and determine for each week in each country and region what proportion of the photographs are tagged with the word "protest" in 34 different languages. We find that higher proportions of "protest"-tagged photographs in a given country and region in a given week correspond to greater numbers of reports of protests in that country and region and week in the newspaper The Guardian. Our findings underline the potential value of photographs uploaded to the Internet as a source of global, cheap and rapidly available measurements of human behaviour in the real world.

Estimating suicide occurrence statistics using Google Trends.

Data on the number of people who have committed suicide tends to be reported with a substantial time lag of around two years. We examine whether online activity measured by Google searches can help us improve estimates of the number of suicide occurrences in England before official figures are released. Specifically, we analyse how data on the number of Google searches for the terms 'depression' and 'suicide' relate to the number of suicides between 2004 and 2013. We find that estimates drawing on Google data are significantly better than estimates using previous suicide data alone. We show that a greater number of searches for the term 'depression' is related to fewer suicides, whereas a greater number of searches for the term 'suicide' is related to more suicides. Data on suicide related search behaviour can be used to improve current estimates of the number of suicide occurrences. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1140/epjds/s13688-016-0094-0) contains supplementary material.

Searching Choices: Quantifying Decision-Making Processes Using Search Engine Data.

When making a decision, humans consider two types of information: information they have acquired through their prior experience of the world, and further information they gather to support the decision in question. Here, we present evidence that data from search engines such as Google can help us model both sources of information. We show that statistics from search engines on the frequency of content on the Internet can help us estimate the statistical structure of prior experience; and, specifically, we outline how such statistics can inform psychological theories concerning the valuation of human lives, or choices involving delayed outcomes. Turning to information gathering, we show that search query data might help measure human information gathering, and it may predict subsequent decisions. Such data enable us to compare information gathered across nations, where analyses suggest, for example, a greater focus on the future in countries with a higher per capita GDP. We conclude that search engine data constitute a valuable new resource for cognitive scientists, offering a fascinating new tool for understanding the human decision-making process.

Quantifying the Impact of Scenic Environments on Health.

Few people would deny an intuitive sense of increased wellbeing when spending time in beautiful locations. Here, we ask: can we quantify the relationship between environmental aesthetics and human health? We draw on data from Scenic-Or-Not, a website that crowdsources ratings of "scenicness" for geotagged photographs across Great Britain, in combination with data on citizen-reported health from the Census for England and Wales. We find that inhabitants of more scenic environments report better health, across urban, suburban and rural areas, even when taking core socioeconomic indicators of deprivation into account, such as income, employment and access to services. Our results provide evidence in line with the striking hypothesis that the aesthetics of the environment may have quantifiable consequences for our wellbeing.

The advantage of short paper titles.

Vast numbers of scientific articles are published each year, some of which attract considerable attention, and some of which go almost unnoticed. Here, we investigate whether any of this variance can be explained by a simple metric of one aspect of the paper's presentation: the length of its title. Our analysis provides evidence that journals which publish papers with shorter titles receive more citations per paper. These results are consistent with the intriguing hypothesis that papers with shorter titles may be easier to understand, and hence attract more citations.