Web-Scale Training for Face Identification

Scaling machine learning methods to very large datasets has attracted considerable attention in recent years, thanks to easy access to ubiquitous sensing and data from the web. We study face recognition and show that three distinct properties have surprising effects on the transferability of deep convolutional networks (CNN): (1) The bottleneck of the network serves as an important transfer learning regularizer, and (2) in contrast to the common wisdom, performance saturation may exist in CNN's (as the number of training samples grows); we propose a solution for alleviating this by replacing the naive random subsampling of the training set with a bootstrapping process. Moreover, (3) we find a link between the representation norm and the ability to discriminate in a target domain, which sheds lights on how such networks represent faces. Based on these discoveries, we are able to improve face recognition accuracy on the widely used LFW benchmark, both in the verification (1:1) and identification (1:N) protocols, and directly compare, for the first time, with the state of the art Commercially-Off-The-Shelf system and show a sizable leap in performance

Space Math: Lunar Math

This booklet includes 17 problems relating to the Moon and its exploration. Images from NASA are analyzed to determine image scales and the physical sizes of various crates and features. The probability of meteor impacts near a lunar colony are calculated, and the horizon distance is determined using simple geometry. Also covered are: determining the mass of the Moon, a simple model for the lunar interior, heat flow rates, extracting oxygen from lunar rock, and lunar transits and eclipses. (8.5 x11, 28 pages, 11 color images, PDF file) Educational levels: Intermediate elementary, Middle school, High school

Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy

Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes of entire surveys a decade ago can now be acquired in a single night and real-time analysis is often desired. Thus, modern astronomy requires big data know-how, in particular it demands highly efficient machine learning and image analysis algorithms. But scalability is not the only challenge: Astronomy applications touch several current machine learning research questions, such as learning from biased data and dealing with label and measurement noise. We argue that this makes astronomy a great domain for computer science research, as it pushes the boundaries of data analysis. In the following, we will present this exciting application area for data scientists. We will focus on exemplary results, discuss main challenges, and highlight some recent methodological advancements in machine learning and image analysis triggered by astronomical applications

Personal Volunteer Computing

We propose personal volunteer computing, a novel paradigm to encourage technical solutions that leverage personal devices, such as smartphones and laptops, for personal applications that require significant computations, such as animation rendering and image processing. The paradigm requires no investment in additional hardware, relying instead on devices that are already owned by users and their community, and favours simple tools that can be implemented part-time by a single developer. We show that samples of personal devices of today are competitive with a top-of-the-line laptop from two years ago. We also propose new directions to extend the paradigm

The Square Kilometer Array: cosmology, pulsars and other physics with the SKA

SKA is a new technology radio-telescope array, about two orders of magnitude more sensitive and rapid in sky surveys than present instruments. It will probe the dark age of the universe, just afer recombination, and during the epoch of reionisation (z=6-15); it will be the unique instrument to map the atomic gas in high redshift galaxies, and determine the amount and distribution of dark matter in the early universe. Not only it will detect and measure the redshifts of billions of galaxies up to z=2, but also it will discover and monitor around 20 000 pulsars in our Milky Way. The timing of pulsars will trace the stretching of space, able to detect gravitational waves. Binary pulsars will help to test gravity in strong fields, and probe general relativity. These exciting perspectives will become real beyond 2020.Comment: 16 pages, 5 figures, Proceedings of INFIERI-2014, Summer School on "Intelligent Signal Processing for Frontier Research and Industry", JINS

The LSST Data Mining Research Agenda

We describe features of the LSST science database that are amenable to scientific data mining, object classification, outlier identification, anomaly detection, image quality assurance, and survey science validation. The data mining research agenda includes: scalability (at petabytes scales) of existing machine learning and data mining algorithms; development of grid-enabled parallel data mining algorithms; designing a robust system for brokering classifications from the LSST event pipeline (which may produce 10,000 or more event alerts per night); multi-resolution methods for exploration of petascale databases; indexing of multi-attribute multi-dimensional astronomical databases (beyond spatial indexing) for rapid querying of petabyte databases; and more.Comment: 5 pages, Presented at the "Classification and Discovery in Large Astronomical Surveys" meeting, Ringberg Castle, 14-17 October, 200

Arrangement Infringement Possibility Approach: Some Economic Features of Large-Scale Events

The definition of arrangement infringement has been given. Several characteristics of hurricanes as large-scale events and objectives for the first stages of insurance data analysis have been sketched out. Scale hypotheses, insurance and investment problems have been formulated.risk, insurance, investment, choice, hurricanes