Where You Are Is What You Do: On Inferring Offline Activities From Location Data

In this paper we investigate the ability of modern machine learning algorithms in inferring basic offline activities,~e.g., shopping and dining, from location data. Using anonymized data of thousands of users of a prominent location-based social network, we empirically demonstrate that not only state-of-the-art machine learning excels at the task at hand~(F1 score>0.9) but also tabular models are among the best performers. The findings we report here not only fill an existing gap in the literature, but also highlight the potential risks of such capabilities given the ubiquity of location data and the high accessibility of tabular machine learning models.Comment: Accepted to IEEE ICDM Workshops 202

系外惑星の研究のための新しい三次元軌道決定ツールの開発と応用

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 郷田 直輝, 東京大学准教授 田中 培生, 東京大学教授 小久保 英一郎, 東京大学准教授 藤井 通子, 東京工業大学准教授 佐藤 文衛University of Tokyo(東京大学

Data Reduction Pipeline for the CHARIS Integral-Field Spectrograph I: Detector Readout Calibration and Data Cube Extraction

We present the data reduction pipeline for CHARIS, a high-contrast integral-field spectrograph for the Subaru Telescope. The pipeline constructs a ramp from the raw reads using the measured nonlinear pixel response, and reconstructs the data cube using one of three extraction algorithms: aperture photometry, optimal extraction, or $\chi^2$ fitting. We measure and apply both a detector flatfield and a lenslet flatfield and reconstruct the wavelength- and position-dependent lenslet point-spread function (PSF) from images taken with a tunable laser. We use these measured PSFs to implement a $\chi^2$-based extraction of the data cube, with typical residuals of ~5% due to imperfect models of the undersampled lenslet PSFs. The full two-dimensional residual of the $\chi^2$ extraction allows us to model and remove correlated read noise, dramatically improving CHARIS' performance. The $\chi^2$ extraction produces a data cube that has been deconvolved with the line-spread function, and never performs any interpolations of either the data or the individual lenslet spectra. The extracted data cube also includes uncertainties for each spatial and spectral measurement. CHARIS' software is parallelized, written in Python and Cython, and freely available on github with a separate documentation page. Astrometric and spectrophotometric calibrations of the data cubes and PSF subtraction will be treated in a forthcoming paper.Comment: 18 pages, 15 figures, 3 tables, replaced with JATIS accepted version (emulateapj formatted here). Software at https://github.com/PrincetonUniversity/charis-dep and documentation at http://princetonuniversity.github.io/charis-de

CHARIS Science: Performance Simulations for the Subaru Telescope's Third-Generation of Exoplanet Imaging Instrumentation

We describe the expected scientific capabilities of CHARIS, a high-contrast integral-field spectrograph (IFS) currently under construction for the Subaru telescope. CHARIS is part of a new generation of instruments, enabled by extreme adaptive optics (AO) systems (including SCExAO at Subaru), that promise greatly improved contrasts at small angular separation thanks to their ability to use spectral information to distinguish planets from quasistatic speckles in the stellar point-spread function (PSF). CHARIS is similar in concept to GPI and SPHERE, on Gemini South and the Very Large Telescope, respectively, but will be unique in its ability to simultaneously cover the entire near-infrared $J$, $H$, and $K$ bands with a low-resolution mode. This extraordinarily broad wavelength coverage will enable spectral differential imaging down to angular separations of a few $\lambda/D$, corresponding to $\sim$0.\!\!''1. SCExAO will also offer contrast approaching $10^{-5}$ at similar separations, $\sim$0.\!\!''1--0.\!\!''2. The discovery yield of a CHARIS survey will depend on the exoplanet distribution function at around 10 AU. If the distribution of planets discovered by radial velocity surveys extends unchanged to $\sim$20 AU, observations of $\sim$200 mostly young, nearby stars targeted by existing high-contrast instruments might find $\sim$1--3 planets. Carefully optimizing the target sample could improve this yield by a factor of a few, while an upturn in frequency at a few AU could also increase the number of detections. CHARIS, with a higher spectral resolution mode of $R \sim 75$, will also be among the best instruments to characterize planets and brown dwarfs like HR 8799 cde and $\kappa$ And b.Comment: 13 pages, 7 figures, proceedings from SPIE Montrea

A Substellar Companion to Pleiades HII 3441

We find a new substellar companion to the Pleiades member star, Pleiades HII 3441, using the Subaru telescope with adaptive optics. The discovery is made as part of the high-contrast imaging survey to search for planetary-mass and substellar companions in the Pleiades and young moving groups. The companion has a projected separation of 0".49 +/- 0".02 (66 +/- 2 AU) and a mass of 68 +/- 5 M_J based on three observations in the J-, H-, and K_S-band. The spectral type is estimated to be M7 (~2700 K), and thus no methane absorption is detected in the H band. Our Pleiades observations result in the detection of two substellar companions including one previously reported among 20 observed Pleiades stars, and indicate that the fraction of substellar companions in the Pleiades is about 10.0 +26.1/-8.8 %. This is consistent with multiplicity studies of both the Pleiades stars and other open clusters.Comment: Main text (14 pages, 4 figures, 4 tables), and Supplementary data (8 pages, 3 tables). Accepted for Publications of Astronomical Society of Japa