What Will You Do for the Rest of the Day?

Understanding and predicting human mobility is vital to a large number of applications, ranging from recommendations to safety and urban service planning. In some travel applications, the ability to accurately predict the user's future trajectory is vital for delivering high quality of service. The accurate prediction of detailed trajectories would empower location-based service providers with the ability to deliver more precise recommendations to users. Existing work on human mobility prediction has mainly focused on the prediction of the next location (or the set of locations) visited by the user, rather than on the prediction of the continuous trajectory (sequences of further locations and the corresponding arrival and departure times). Furthermore, existing approaches often return predicted locations as regions with coarse granularity rather than geographical coordinates, which limits the practicality of the prediction. In this paper, we introduce a novel trajectory prediction problem: given historical data and a user's initial trajectory in the morning, can we predict the user's full trajectory later in the day (e.g. the afternoon trajectory)? The predicted continuous trajectory includes the sequence of future locations, the stay times, and the departure times. We first conduct a comprehensive analysis about the relationship between morning trajectories and the corresponding afternoon trajectories, and found there is a positive correlation between them. Our proposed method combines similarity metrics over the extracted temporal sequences of locations to estimate similar informative segments across user trajectories. Our evaluation shows results on both labeled and geographical trajectories with a prediction error reduced by 10-35% in comparison to the baselines. This improvement has the potential to enable precise location services, raising usefulness to users to unprecedented levels. We also present empirical evaluations with Markov model and Long Short Term Memory (LSTM), a state-of-the-art Recurrent Neural Network model. Our proposed method is shown to be more effective when smaller number of samples are used and is exponentially more efficient than LSTM.</jats:p

The Outer Disks of Early-Type Galaxies. I. Surface-Brightness Profiles of Barred Galaxies

We present a study of 66 barred, early-type (S0-Sb) disk galaxies, focused on the disk surface brightness profile outside the bar region and the nature of Freeman Type I and II profiles, their origins, and their possible relation to disk truncations. This paper discusses the data and their reduction, outlines our classification system, and presents $R$-band profiles and classifications for all galaxies in the sample. The profiles are derived from a variety of different sources, including the Sloan Digital Sky Survey (Data Release 5). For about half of the galaxies, we have profiles derived from more than one telescope; this allows us to check the stability and repeatability of our profile extraction and classification. The vast majority of the profiles are reliable down to levels of mu_R ~ 27 mag arcsec^-2; in exceptional cases, we can trace profiles down to mu_R > 28. We can typically follow disk profiles out to at least 1.5 times the traditional optical radius R_25; for some galaxies, we find light extending to ~ 3 R_25. We classify the profiles into three main groups: Type I (single-exponential), Type II (down-bending), and Type III (up-bending). The frequencies of these types are approximately 27%, 42%, and 24%, respectively, plus another 6% which are combinations of Types II and III. We further classify Type II profiles by where the break falls in relation to the bar length, and in terms of the postulated mechanisms for breaks at large radii ("classical trunction" of star formation versus the influence of the Outer Lindblad Resonance of the bar). We also classify the Type III profiles by the probable morphology of the outer light (disk or spheroid). Illustrations are given for all cases. (Abridged)Comment: 41 pages, 26 PDF figures. To appear in the Astronomical Journal. Version with full-resolution figures available at http://www.mpe.mpg.de/~erwin/research

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

The First Post-Kepler Brightness Dips of KIC 8462852

We present a photometric detection of the first brightness dips of the unique variable star KIC 8462852 since the end of the Kepler space mission in 2013 May. Our regular photometric surveillance started in October 2015, and a sequence of dipping began in 2017 May continuing on through the end of 2017, when the star was no longer visible from Earth. We distinguish four main 1-2.5% dips, named "Elsie," "Celeste," "Skara Brae," and "Angkor", which persist on timescales from several days to weeks. Our main results so far are: (i) there are no apparent changes of the stellar spectrum or polarization during the dips; (ii) the multiband photometry of the dips shows differential reddening favoring non-grey extinction. Therefore, our data are inconsistent with dip models that invoke optically thick material, but rather they are in-line with predictions for an occulter consisting primarily of ordinary dust, where much of the material must be optically thin with a size scale <<1um, and may also be consistent with models invoking variations intrinsic to the stellar photosphere. Notably, our data do not place constraints on the color of the longer-term "secular" dimming, which may be caused by independent processes, or probe different regimes of a single process

De novo and biallelic DEAF1 variants cause a phenotypic spectrum.

PURPOSE: To investigate the effect of different DEAF1 variants on the phenotype of patients with autosomal dominant and recessive inheritance patterns and on DEAF1 activity in vitro. METHODS: We assembled a cohort of 23 patients with de novo and biallelic DEAF1 variants, described the genotype-phenotype correlation, and investigated the differential effect of de novo and recessive variants on transcription assays using DEAF1 and Eif4g3 promoter luciferase constructs. RESULTS: The proportion of the most prevalent phenotypic features, including intellectual disability, speech delay, motor delay, autism, sleep disturbances, and a high pain threshold, were not significantly different in patients with biallelic and pathogenic de novo DEAF1 variants. However, microcephaly was exclusively observed in patients with recessive variants (p < 0.0001). CONCLUSION: We propose that different variants in the DEAF1 gene result in a phenotypic spectrum centered around neurodevelopmental delay. While a pathogenic de novo dominant variant would also incapacitate the product of the wild-type allele and result in a dominant-negative effect, a combination of two recessive variants would result in a partial loss of function. Because the clinical picture can be nonspecific, detailed phenotype information, segregation, and functional analysis are fundamental to determine the pathogenicity of novel variants and to improve the care of these patients