Data set management

The data sets currently supported by the Pilot Climate Data System (PCDS) are listed, many of which are Level II and Level III NImbus-7 data sets. Those data sets planned for future access through the PCDS were also listed, and their current installation status was stated. The tasks involved in supporting data sets within the PCDS were identified and described. After a data set is approved for implementation into the system and communication with the data producers is established, the information for the detailed catalog entry is gathered. This information then is reviewed with the scientists involved before producing a catalog summary. Once this is done, the catalog information can be provided to users, even before the data set is installed. The next several tasks involve software development and can prove to be the most time-consuming aspect in the data set support. These tasks can be simplified if the data producers provide complete and accurate documentation of their product. Software for reading and interpreting the data sets is developed and the data sets, or portions therefore, that will be made available for use within the PCDS are inventoried. Users can access this information via the INVENTORY Subsystem of the PCDS

Automatic computation of data-set definitions

Mathematical method for the construction of a computer program data set description from a computer program contains detailed declarative information. Cartesian products and disjoint-union operators are used to yield a series of recursive group equations

DIY Human Action Data Set Generation

The recent successes in applying deep learning techniques to solve standard computer vision problems has aspired researchers to propose new computer vision problems in different domains. As previously established in the field, training data itself plays a significant role in the machine learning process, especially deep learning approaches which are data hungry. In order to solve each new problem and get a decent performance, a large amount of data needs to be captured which may in many cases pose logistical difficulties. Therefore, the ability to generate de novo data or expand an existing data set, however small, in order to satisfy data requirement of current networks may be invaluable. Herein, we introduce a novel way to partition an action video clip into action, subject and context. Each part is manipulated separately and reassembled with our proposed video generation technique. Furthermore, our novel human skeleton trajectory generation along with our proposed video generation technique, enables us to generate unlimited action recognition training data. These techniques enables us to generate video action clips from an small set without costly and time-consuming data acquisition. Lastly, we prove through extensive set of experiments on two small human action recognition data sets, that this new data generation technique can improve the performance of current action recognition neural nets

Seabird and Plastic Ingestion Data Set

Plastic debris is a pervasive and critical environmental challenge that is being described as a world-wide crisis for marine life. Seabirds are sensitive to pollutants and are of critical conservation concern. Because seabirds are excellent bioindicators of marine ecosystem health, information about their plastic ingestion can serve as an indicator of plastic exposure across multiple marine trophic levels. Our study describes the prevalence of plastic ingestion for four seabird species: Great Black-backed Gulls (Larus marinus), Herring Gulls (L. argentatus), Common Terns (Sterna hirundo), and Roseate Terns (S. dougallii) nesting in the Gulf of Maine. Samples were collected opportunistically, including pellets, regurgitant, discarded fish, and deceased seabirds. Plastics were primarily found in pellet samples, and common types included fragments and sheets. Herring Gulls displayed significantly higher plastic exposure than the other three species across all metrics analyzed (p-valu

Boston University Common Data Set 2014-2015

This is the archive of common data sets of Boston University from 2014-2015, including general information, enrollment and persistence, first-time, first-year (freshman) admission, transfer admission, academic offerings and policies, student life, annual expenses, financial aid, instructional faculty, and degrees conferred

IPC: A Benchmark Data Set for Learning with Graph-Structured Data

Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods. We release a new data set, compiled from International Planning Competitions (IPC), for benchmarking graph classification, regression, and related tasks. Apart from the graph construction (based on AI planning problems) that is interesting in its own right, the data set possesses distinctly different characteristics from popularly used benchmarks. The data set, named IPC, consists of two self-contained versions, grounded and lifted, both including graphs of large and skewedly distributed sizes, posing substantial challenges for the computation of graph models such as graph kernels and graph neural networks. The graphs in this data set are directed and the lifted version is acyclic, offering the opportunity of benchmarking specialized models for directed (acyclic) structures. Moreover, the graph generator and the labeling are computer programmed; thus, the data set may be extended easily if a larger scale is desired. The data set is accessible from \url{https://github.com/IBM/IPC-graph-data}.Comment: ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data. The data set is accessible from https://github.com/IBM/IPC-graph-dat

BICEP2 II: Experiment and Three-Year Data Set

We report on the design and performance of the BICEP2 instrument and on its three-year data set. BICEP2 was designed to measure the polarization of the cosmic microwave background (CMB) on angular scales of 1 to 5 degrees ($\ell$=40-200), near the expected peak of the B-mode polarization signature of primordial gravitational waves from cosmic inflation. Measuring B-modes requires dramatic improvements in sensitivity combined with exquisite control of systematics. The BICEP2 telescope observed from the South Pole with a 26~cm aperture and cold, on-axis, refractive optics. BICEP2 also adopted a new detector design in which beam-defining slot antenna arrays couple to transition-edge sensor (TES) bolometers, all fabricated on a common substrate. The antenna-coupled TES detectors supported scalable fabrication and multiplexed readout that allowed BICEP2 to achieve a high detector count of 500 bolometers at 150 GHz, giving unprecedented sensitivity to B-modes at degree angular scales. After optimization of detector and readout parameters, BICEP2 achieved an instrument noise-equivalent temperature of 15.8 $\mu$K sqrt(s). The full data set reached Stokes Q and U map depths of 87.2 nK in square-degree pixels (5.2 $\mu$K arcmin) over an effective area of 384 square degrees within a 1000 square degree field. These are the deepest CMB polarization maps at degree angular scales to date. The power spectrum analysis presented in a companion paper has resulted in a significant detection of B-mode polarization at degree scales.Comment: 30 pages, 24 figure