Tracing the German Centennial Flood in the Stream of Tweets: First Lessons Learned

Social microblogging services such as Twitter result in massive streams of georeferenced messages and geolocated status updates. This real-time source of information is invaluable for many application areas, in particular for disaster detection and response scenarios. Consequently, a considerable number of works has dealt with issues of their acquisition, analysis and visualization. Most of these works not only assume an appropriate percentage of georeferenced messages that allows for detecting relevant events for a specific region and time frame, but also that these geolocations are reasonably correct in representing places and times of the underlying spatio-temporal situation. In this paper, we review these two key assumption based on the results of applying a visual analytics approach to a dataset of georeferenced Tweets from Germany over eight months witnessing several large-scale flooding situations throughout the country. Our results con rm the potential of Twitter as a distributed 'social sensor' but at the same time highlight some caveats in interpreting immediate results. To overcome these limits we explore incorporating evidence from other data sources including further social media and mobile phone network metrics to detect, confirm and refine events with respect to location and time. We summarize the lessons learned from our initial analysis by proposing recommendations and outline possible future work directions

Chandra Orion Ultradeep Project: Observations and Source Lists

We present a description of the data reduction methods and the derived catalog of more than 1600 X-ray point sources from the exceptionally deep January 2003 Chandra X-ray Observatory observation of the Orion Nebula Cluster and embedded populations around OMC-1. The observation was obtained with Chandra's Advanced CCD Imaging Spectrometer (ACIS) and has been nicknamed the Chandra Orion Ultradeep Project (COUP). With an 838 ks exposure made over a continuous period of 13.2 days, the COUP observation provides the most uniform and comprehensive dataset on the X-ray emission of normal stars ever obtained in the history of X-ray astronomy.Comment: 52 pages, 11 figures, 12 tables. Accepted for publication in ApJS, special issue dedicated to Chandra Orion Ultradeep Project. A version with high quality figures can be found at http://www.astro.psu.edu/users/gkosta/COUP_Methodology.pd

Enhancing the Jaquez k Nearest Neighbor Test for Space-Time Interaction

The Jacquez k nearest neighbor test, originally developed to improve upon shortcomings of existing tests for space-time interaction, has been shown to be a robust and powerful method of detecting interaction. Despite its flexibility and power however, the test has three main shortcomings: (1) it discards important information regarding the spatial and temporal scale at which detected interac- tion takes place; (2) the results of the test have not been visualized; (3) recent research demonstrates the test to be susceptible to population shift bias. This study presents enhancements to the Jacquez k nearest neighbors test with the goal of addressing each of these three shortcomings and improving the utility of the test. Data on Burkitt’s lymphoma cases in Uganda between 1961-1975 are employed to illustrate the modifications and enhance the visual output of the test. Output from the enhanced test is compared to that provided by alternative tests of space-time interaction. Results show the enhancements presented in this study transform the Jacquez test into a complete, descriptive, and informative metric that can be used as a stand alone measure of global space-time interaction.space-time interaction, Jacquez k nearest neighbor, visualization, space-time cube, population shift bias

Impact of schizophrenia on anterior and posterior hippocampus during memory for complex scenes.

ObjectivesHippocampal dysfunction has been proposed as a mechanism for memory deficits in schizophrenia. Available evidence suggests that the anterior and posterior hippocampus could be differentially affected. Accordingly, we used fMRI to test the hypothesis that activity in posterior hippocampus is disproportionately reduced in schizophrenia, particularly during spatial memory retrieval.Methods26 healthy participants and 24 patients with schizophrenia from the UC Davis Early Psychosis Program were studied while fMRI was acquired on a 3 Tesla Siemens scanner. During encoding, participants were oriented to critical items through questions about item features (e.g., "Does the lamp have a square shade?") or spatial location (e.g., "Is the lamp on the table next to the couch?"). At test, participants determined whether scenes were changed or unchanged. fMRI analyses contrasted activation in a priori regions of interest (ROI) in anterior and posterior hippocampus during correct recognition of item changes and spatial changes.ResultsAs predicted, patients with schizophrenia exhibited reduced activation in the posterior hippocampus during detection of spatial changes but not during detection of item changes. Unexpectedly, patients exhibited increased activation of anterior hippocampus during detection of item changes. Whole brain analyses revealed reduced fronto-parietal and striatal activation in patients for spatial but not for item change trials.ConclusionsResults suggest a gradient of hippocampal dysfunction in which posterior hippocampus - which is necessary for processing fine-grained spatial relationships - is underactive, and anterior hippocampus - which may process context more globally - is overactive

The Deep Lens Survey Transient Search I : Short Timescale and Astrometric Variability

We report on the methodology and first results from the Deep Lens Survey transient search. We utilize image subtraction on survey data to yield all sources of optical variability down to 24th magnitude. Images are analyzed immediately after acquisition, at the telescope and in near-real time, to allow for followup in the case of time-critical events. All classes of transients are posted to the web upon detection. Our observing strategy allows sensitivity to variability over several decades in timescale. The DLS is the first survey to classify and report all types of photometric and astrometric variability detected, including solar system objects, variable stars, supernovae, and short timescale phenomena. Three unusual optical transient events were detected, flaring on thousand-second timescales. All three events were seen in the B passband, suggesting blue color indices for the phenomena. One event (OT 20020115) is determined to be from a flaring Galactic dwarf star of spectral type dM4. From the remaining two events, we find an overall rate of \eta = 1.4 events deg-2 day-1 on thousand-second timescales, with a 95% confidence limit of \eta < 4.3. One of these events (OT 20010326) originated from a compact precursor in the field of galaxy cluster Abell 1836, and its nature is uncertain. For the second (OT 20030305) we find strong evidence for an extended extragalactic host. A dearth of such events in the R passband yields an upper 95% confidence limit on short timescale astronomical variability between 19.5 < R < 23.4 of \eta_R < 5.2. We report also on our ensemble of astrometrically variable objects, as well as an example of photometric variability with an undetected precursor.Comment: 24 pages, 12 figures, 3 tables. Accepted for publication in ApJ. Variability data available at http://dls.bell-labs.com/transients.htm

Evaluating the utility of multispectral information in delineating the areal extent of precipitation

Data from geosynchronous Earth-orbiting (GEO) satellites equipped with visible (VIS) and infrared (IR) scanners are commonly used in rain retrieval algorithms. These algorithms benefit from the high spatial and temporal resolution of GEO observations, either in stand-alone mode or in combination with higher-quality but less frequent microwave observations from low Earth-orbiting (LEO) satellites. In this paper, a neural network-based framework is presented to evaluate the utility of multispectral information in improving rain/no-rain (R/NR) detection. The algorithm uses the powerful classification features of the self-organizing feature map (SOFM), along with probability matching techniques to map single- or multispectral input space into R/NR maps. The framework was tested and validated using the 31 possible combinations of the five Geostationary Operational Environmental Satellite 12 (GOES-12) channels. An algorithm training and validation study was conducted over the conterminous United States during June-August 2006. The results indicate that during daytime, the visible channel (0.65 μm) can yield significant improvements in R/NR detection capabilities, especially when combined with any of the other four GOES-12 channels. Similarly, for nighttime detection the combination of two IR channels - particularly channels 3 (6.5 μm) and 4 (10.7 μm)-resulted in significant performance gain over any single IR channel. In both cases, however, using more than two channels resulted only in marginal improvements over two-channel combinations. Detailed examination of event-based images indicate that the proposed algorithm is capable of extracting information useful to screen no-rain pixels associated with cold, thin clouds and identifying rain areas under warm but rainy clouds. Both cases have been problematic areas for IR-only algorithms. © 2009 American Meteorological Society

Fast, scalable, Bayesian spike identification for multi-electrode arrays

We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes this goal, and is fast, because it exploits the spatial locality of each unit and the basic biophysics of extracellular signal propagation. Human intervention is minimized and streamlined via a graphical interface. We illustrate our method on data from a mammalian retina preparation and document its performance on simulated data consisting of spikes added to experimentally measured background noise. The algorithm is highly accurate