204,557 research outputs found
Shape: A 3D Modeling Tool for Astrophysics
We present a flexible interactive 3D morpho-kinematical modeling application
for astrophysics. Compared to other systems, our application reduces the
restrictions on the physical assumptions, data type and amount that is required
for a reconstruction of an object's morphology. It is one of the first publicly
available tools to apply interactive graphics to astrophysical modeling. The
tool allows astrophysicists to provide a-priori knowledge about the object by
interactively defining 3D structural elements. By direct comparison of model
prediction with observational data, model parameters can then be automatically
optimized to fit the observation. The tool has already been successfully used
in a number of astrophysical research projects.Comment: 13 pages, 11 figures, accepted for publication in the "IEEE
Transactions on Visualization and Computer Graphics
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Automatic synthesis of analog layout : a survey
A review of recent research in the automatic synthesis of physical geometry for analog integrated circuits is presented. On introduction, an explanation of the difficulties involved in analog layout as opposed to digital layout is covered. Review of the literature then follows. Emphasis is placed on the exposition of general methods for addressing problems specific to analog layout, with the details of specific systems only being given when they surve to illustrate these methods well. The conclusion discusses problems remaining and offers a prediction as to how technology will evolve to solve them. It is argued that although progress has been and will continue to be made in the automation of analog IC layout, due to fundamental differences in the nature of analog IC design as opposed to digital design, it should not be expected that the level of automation of the former will reach that of the latter any time soon
Automatic Throughput and Critical Path Analysis of x86 and ARM Assembly Kernels
Useful models of loop kernel runtimes on out-of-order architectures require
an analysis of the in-core performance behavior of instructions and their
dependencies. While an instruction throughput prediction sets a lower bound to
the kernel runtime, the critical path defines an upper bound. Such predictions
are an essential part of analytic (i.e., white-box) performance models like the
Roofline and Execution-Cache-Memory (ECM) models. They enable a better
understanding of the performance-relevant interactions between hardware
architecture and loop code. The Open Source Architecture Code Analyzer (OSACA)
is a static analysis tool for predicting the execution time of sequential
loops. It previously supported only x86 (Intel and AMD) architectures and
simple, optimistic full-throughput execution. We have heavily extended OSACA to
support ARM instructions and critical path prediction including the detection
of loop-carried dependencies, which turns it into a versatile
cross-architecture modeling tool. We show runtime predictions for code on Intel
Cascade Lake, AMD Zen, and Marvell ThunderX2 micro-architectures based on
machine models from available documentation and semi-automatic benchmarking.
The predictions are compared with actual measurements.Comment: 6 pages, 3 figure
Analytical modelling in Dynamo
BIM is applied as modern database for civil
engineering. Its recent development allows to preserve
both structure geometrical and analytical information. The
analytical model described in the paper is derived directly
from BIM model of a structure automatically but in most
cases it requires manual improvements before being sent
to FEM software. Dynamo visual programming language
was used to handle the analytical data. Authors developed
a program which corrects faulty analytical model obtained
from BIM geometry, thus providing better automation for
preparing FEM model. Program logic is explained and test
cases shown
Efficient construction of linear models in materials modeling and applications to force constant expansions
Linear models, such as force constant (FC) and cluster expansions, play a key
role in physics and materials science. While they can in principle be
parametrized using regression and feature selection approaches, the convergence
behavior of these techniques, in particular with respect to thermodynamic
properties is not well understood. Here, we therefore analyze the efficacy and
efficiency of several state-of-the-art regression and feature selection
methods, in particular in the context of FC extraction and the prediction of
different thermodynamic properties. Generic feature selection algorithms such
as recursive feature elimination with ordinary least-squares (OLS), automatic
relevance determination regression, and the adaptive least absolute shrinkage
and selection operator can yield physically sound models for systems with a
modest number of degrees of freedom. For large unit cells with low symmetry
and/or high-order expansions they come, however, with a non-negligible
computational cost that can be more than two orders of magnitude higher than
that of OLS. In such cases, OLS with cutoff selection provides a viable route
as demonstrated here for both second-order FCs in large low-symmetry unit cells
and high-order FCs in low-symmetry systems. While regression techniques are
thus very powerful, they require well-tuned protocols. Here, the present work
establishes guidelines for the design of protocols that are readily usable,
e.g., in high-throughput and materials discovery schemes. Since the underlying
algorithms are not specific to FC construction, the general conclusions drawn
here also have a bearing on the construction of other linear models in physics
and materials science.Comment: 15 pages, 12 figure
Enabling Self-aware Smart Buildings by Augmented Reality
Conventional HVAC control systems are usually incognizant of the physical
structures and materials of buildings. These systems merely follow pre-set HVAC
control logic based on abstract building thermal response models, which are
rough approximations to true physical models, ignoring dynamic spatial
variations in built environments. To enable more accurate and responsive HVAC
control, this paper introduces the notion of "self-aware" smart buildings, such
that buildings are able to explicitly construct physical models of themselves
(e.g., incorporating building structures and materials, and thermal flow
dynamics). The question is how to enable self-aware buildings that
automatically acquire dynamic knowledge of themselves. This paper presents a
novel approach using "augmented reality". The extensive user-environment
interactions in augmented reality not only can provide intuitive user
interfaces for building systems, but also can capture the physical structures
and possibly materials of buildings accurately to enable real-time building
simulation and control. This paper presents a building system prototype
incorporating augmented reality, and discusses its applications.Comment: This paper appears in ACM International Conference on Future Energy
Systems (e-Energy), 201
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