17,242 research outputs found
ROOT - A C++ Framework for Petabyte Data Storage, Statistical Analysis and Visualization
ROOT is an object-oriented C++ framework conceived in the high-energy physics
(HEP) community, designed for storing and analyzing petabytes of data in an
efficient way. Any instance of a C++ class can be stored into a ROOT file in a
machine-independent compressed binary format. In ROOT the TTree object
container is optimized for statistical data analysis over very large data sets
by using vertical data storage techniques. These containers can span a large
number of files on local disks, the web, or a number of different shared file
systems. In order to analyze this data, the user can chose out of a wide set of
mathematical and statistical functions, including linear algebra classes,
numerical algorithms such as integration and minimization, and various methods
for performing regression analysis (fitting). In particular, ROOT offers
packages for complex data modeling and fitting, as well as multivariate
classification based on machine learning techniques. A central piece in these
analysis tools are the histogram classes which provide binning of one- and
multi-dimensional data. Results can be saved in high-quality graphical formats
like Postscript and PDF or in bitmap formats like JPG or GIF. The result can
also be stored into ROOT macros that allow a full recreation and rework of the
graphics. Users typically create their analysis macros step by step, making use
of the interactive C++ interpreter CINT, while running over small data samples.
Once the development is finished, they can run these macros at full compiled
speed over large data sets, using on-the-fly compilation, or by creating a
stand-alone batch program. Finally, if processing farms are available, the user
can reduce the execution time of intrinsically parallel tasks - e.g. data
mining in HEP - by using PROOF, which will take care of optimally distributing
the work over the available resources in a transparent way
Feasibility study of an Integrated Program for Aerospace-vehicle Design (IPAD) system. Volume 2: Characterization of the IPAD system, phase 1, task 1
The aircraft design process is discussed along with the degree of participation of the various engineering disciplines considered in this feasibility study
A Computational Design Pipeline to Fabricate Sensing Network Physicalizations
Interaction is critical for data analysis and sensemaking. However, designing
interactive physicalizations is challenging as it requires cross-disciplinary
knowledge in visualization, fabrication, and electronics. Interactive
physicalizations are typically produced in an unstructured manner, resulting in
unique solutions for a specific dataset, problem, or interaction that cannot be
easily extended or adapted to new scenarios or future physicalizations. To
mitigate these challenges, we introduce a computational design pipeline to 3D
print network physicalizations with integrated sensing capabilities. Networks
are ubiquitous, yet their complex geometry also requires significant
engineering considerations to provide intuitive, effective interactions for
exploration. Using our pipeline, designers can readily produce network
physicalizations supporting selection-the most critical atomic operation for
interaction-by touch through capacitive sensing and computational inference.
Our computational design pipeline introduces a new design paradigm by
concurrently considering the form and interactivity of a physicalization into
one cohesive fabrication workflow. We evaluate our approach using (i)
computational evaluations, (ii) three usage scenarios focusing on general
visualization tasks, and (iii) expert interviews. The design paradigm
introduced by our pipeline can lower barriers to physicalization research,
creation, and adoption.Comment: 11 pages, 8 figures; to be published in Proceedings of IEEE VIS 202
Fast filtering and animation of large dynamic networks
Detecting and visualizing what are the most relevant changes in an evolving
network is an open challenge in several domains. We present a fast algorithm
that filters subsets of the strongest nodes and edges representing an evolving
weighted graph and visualize it by either creating a movie, or by streaming it
to an interactive network visualization tool. The algorithm is an approximation
of exponential sliding time-window that scales linearly with the number of
interactions. We compare the algorithm against rectangular and exponential
sliding time-window methods. Our network filtering algorithm: i) captures
persistent trends in the structure of dynamic weighted networks, ii) smoothens
transitions between the snapshots of dynamic network, and iii) uses limited
memory and processor time. The algorithm is publicly available as open-source
software.Comment: 6 figures, 2 table
物理/バーチャル空間の接続と分離を媒介する可動壁に関する研究
Tohoku University博士(情報科学)thesi
Speaker-following Video Subtitles
We propose a new method for improving the presentation of subtitles in video
(e.g. TV and movies). With conventional subtitles, the viewer has to constantly
look away from the main viewing area to read the subtitles at the bottom of the
screen, which disrupts the viewing experience and causes unnecessary eyestrain.
Our method places on-screen subtitles next to the respective speakers to allow
the viewer to follow the visual content while simultaneously reading the
subtitles. We use novel identification algorithms to detect the speakers based
on audio and visual information. Then the placement of the subtitles is
determined using global optimization. A comprehensive usability study indicated
that our subtitle placement method outperformed both conventional
fixed-position subtitling and another previous dynamic subtitling method in
terms of enhancing the overall viewing experience and reducing eyestrain
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