Spurious trend switching phenomena in financial markets

The observation of power laws in the time to extrema of volatility, volume and intertrade times, from milliseconds to years, are shown to result straightforwardly from the selection of biased statistical subsets of realizations in otherwise featureless processes such as random walks. The bias stems from the selection of price peaks that imposes a condition on the statistics of price change and of trade volumes that skew their distributions. For the intertrade times, the extrema and power laws results from the format of transaction data

Quantifying trading behavior in financial markets using Google Trends

Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as “early warning signs” of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior

Solar system constraints on Rindler acceleration

We discuss the classical tests of general relativity in the presence of Rindler acceleration. Among these tests the perihelion shifts give the tightest constraints and indicate that the Pioneer anomaly cannot be caused by a universal solar system Rindler acceleration. We address potential caveats for massive test-objects. Our tightest bound on Rindler acceleration that comes with no caveats is derived from radar echo delay and yields |a|<3nm/s^2.Comment: 7 pages, v2: minor changes, added references, v3: corrected typos, extended Table 1, corrected bound on measurement of gravitational redshif

Scanning Raman spectroscopy of graphene antidot lattices: Evidence for systematic p-type doping

We have investigated antidot lattices, which were prepared on exfoliated graphene single layers via electron-beam lithography and ion etching, by means of scanning Raman spectroscopy. The peak positions, peak widths and intensities of the characteristic phonon modes of the carbon lattice have been studied systematically in a series of samples. In the patterned samples, we found a systematic stiffening of the G band mode, accompanied by a line narrowing, while the 2D mode energies are found to be linearly correlated with the G mode energies. We interpret this as evidence for p-type doping of the nanostructured graphene

Sensor Networks for Monitoring and Control of Water Distribution Systems

Water distribution systems present a significant challenge for structural monitoring. They comprise a complex network of pipelines buried underground that are relatively inaccessible. Maintaining the integrity of these networks is vital for providing clean drinking water to the general public. There is a need for in-situ, on-line monitoring of water distribution systems in order to facilitate efficient management and operation. In particular, it is important to detect and localize pipe failures soon after they occur, and pre-emptively identify ‘hotspots’, or areas of the distribution network that are more likely to be susceptible to structural failure. These capabilities are vital for reducing the time taken to identify and repair failures and hence, mitigating impacts on water supply. WaterWiSe is a platform that manages and analyses data from a network of intelligent wireless sensor nodes, continuously monitoring hydraulic, acoustic and water quality parameters. WaterWiSe supports many applications including dynamic prediction of water demand and hydraulic state, online detection of events such as pipe bursts, and data mining for identification of longer-term trends. This paper describes the WaterWiSe@SG project in Singapore, focusing on the use of WaterWiSe as a tool for monitoring, detecting and predicting abnormal events that may be indicative of structural pipe failures, such as bursts or leaks.Singapore-MIT Alliance for Research and Technology. Center for Environmental Sensing and Modelin

Crowdsourcing Dialect Characterization through Twitter

We perform a large-scale analysis of language diatopic variation using geotagged microblogging datasets. By collecting all Twitter messages written in Spanish over more than two years, we build a corpus from which a carefully selected list of concepts allows us to characterize Spanish varieties on a global scale. A cluster analysis proves the existence of well defined macroregions sharing common lexical properties. Remarkably enough, we find that Spanish language is split into two superdialects, namely, an urban speech used across major American and Spanish citites and a diverse form that encompasses rural areas and small towns. The latter can be further clustered into smaller varieties with a stronger regional character.Comment: 10 pages, 5 figure

Estimating tourism statistics with Wikipedia page views

Decision makers depend on socio-economic indicators to shape the world we inhabit. Reports of these indicators are often delayed due to the effort involved in gathering and aggregating the underlying data. Our increasing interactions with large scale technological systems are generating vast datasets on global human behaviour which are immediately accessible. Here we analyse whether data on how often people view Wikipedia articles might help us to improve estimates of the current number of tourists leaving the UK. Our analyses suggest that in the absence of sufficient history, Wikipedia page views provide an advantage. We conclude that when using adaptive models, Wikipedia usage opens up the possibility to improve estimates of tourism demand