131,503 research outputs found
Refactoring, reengineering and evolution: paths to Geant4 uncertainty quantification and performance improvement
Ongoing investigations for the improvement of Geant4 accuracy and
computational performance resulting by refactoring and reengineering parts of
the code are discussed. Issues in refactoring that are specific to the domain
of physics simulation are identified and their impact is elucidated.
Preliminary quantitative results are reported.Comment: To be published in the Proc. CHEP (Computing in High Energy Physics)
201
Ultrafast processing of pixel detector data with machine learning frameworks
Modern photon science performed at high repetition rate free-electron laser
(FEL) facilities and beyond relies on 2D pixel detectors operating at
increasing frequencies (towards 100 kHz at LCLS-II) and producing rapidly
increasing amounts of data (towards TB/s). This data must be rapidly stored for
offline analysis and summarized in real time. While at LCLS all raw data has
been stored, at LCLS-II this would lead to a prohibitive cost; instead,
enabling real time processing of pixel detector raw data allows reducing the
size and cost of online processing, offline processing and storage by orders of
magnitude while preserving full photon information, by taking advantage of the
compressibility of sparse data typical for LCLS-II applications. We
investigated if recent developments in machine learning are useful in data
processing for high speed pixel detectors and found that typical deep learning
models and autoencoder architectures failed to yield useful noise reduction
while preserving full photon information, presumably because of the very
different statistics and feature sets between computer vision and radiation
imaging. However, we redesigned in Tensorflow mathematically equivalent
versions of the state-of-the-art, "classical" algorithms used at LCLS. The
novel Tensorflow models resulted in elegant, compact and hardware agnostic
code, gaining 1 to 2 orders of magnitude faster processing on an inexpensive
consumer GPU, reducing by 3 orders of magnitude the projected cost of online
analysis at LCLS-II. Computer vision a decade ago was dominated by hand-crafted
filters; their structure inspired the deep learning revolution resulting in
modern deep convolutional networks; similarly, our novel Tensorflow filters
provide inspiration for designing future deep learning architectures for
ultrafast and efficient processing and classification of pixel detector images
at FEL facilities.Comment: 9 pages, 9 figure
Network emulation focusing on QoS-Oriented satellite communication
This chapter proposes network emulation basics and a complete case study of QoS-oriented Satellite Communication
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
Revisiting the Core Ontology and Problem in Requirements Engineering
In their seminal paper in the ACM Transactions on Software Engineering and
Methodology, Zave and Jackson established a core ontology for Requirements
Engineering (RE) and used it to formulate the "requirements problem", thereby
defining what it means to successfully complete RE. Given that stakeholders of
the system-to-be communicate the information needed to perform RE, we show that
Zave and Jackson's ontology is incomplete. It does not cover all types of basic
concerns that the stakeholders communicate. These include beliefs, desires,
intentions, and attitudes. In response, we propose a core ontology that covers
these concerns and is grounded in sound conceptual foundations resting on a
foundational ontology. The new core ontology for RE leads to a new formulation
of the requirements problem that extends Zave and Jackson's formulation. We
thereby establish new standards for what minimum information should be
represented in RE languages and new criteria for determining whether RE has
been successfully completed.Comment: Appears in the proceedings of the 16th IEEE International
Requirements Engineering Conference, 2008 (RE'08). Best paper awar
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