246,062 research outputs found
Incremental and Modular Context-sensitive Analysis
Context-sensitive global analysis of large code bases can be expensive, which
can make its use impractical during software development. However, there are
many situations in which modifications are small and isolated within a few
components, and it is desirable to reuse as much as possible previous analysis
results. This has been achieved to date through incremental global analysis
fixpoint algorithms that achieve cost reductions at fine levels of granularity,
such as changes in program lines. However, these fine-grained techniques are
not directly applicable to modular programs, nor are they designed to take
advantage of modular structures. This paper describes, implements, and
evaluates an algorithm that performs efficient context-sensitive analysis
incrementally on modular partitions of programs. The experimental results show
that the proposed modular algorithm shows significant improvements, in both
time and memory consumption, when compared to existing non-modular, fine-grain
incremental analysis techniques. Furthermore, thanks to the proposed
inter-modular propagation of analysis information, our algorithm also
outperforms traditional modular analysis even when analyzing from scratch.Comment: 56 pages, 27 figures. To be published in Theory and Practice of Logic
Programming. v3 corresponds to the extended version of the ICLP2018 Technical
Communication. v4 is the revised version submitted to Theory and Practice of
Logic Programming. v5 (this one) is the final author version to be published
in TPL
Seedless clustering in all-sky searches for gravitational-wave transients
The problem of searching for unmodeled gravitational-wave bursts can be
thought of as a pattern recognition problem: how to find statistically
significant clusters in spectrograms of strain power when the precise signal
morphology is unknown. In a previous publication, we showed how "seedless
clustering" can be used to dramatically improve the sensitivity of searches for
long-lived gravitational-wave transients. In order to manage the computational
costs, this initial analysis focused on externally triggered searches where the
source location and emission time are both known to some degree of precision.
In this paper, we show how the principle of seedless clustering can be extended
to facilitate computationally-feasible, all-sky searches where the direction
and emission time of the source are entirely unknown. We further demonstrate
that it is possible to achieve a considerable reduction in computation time by
using graphical processor units (GPUs), thereby facilitating more sensitive
searches.Comment: 9 pages, 2 figure
Algorithmic Superactivation of Asymptotic Quantum Capacity of Zero-Capacity Quantum Channels
The superactivation of zero-capacity quantum channels makes it possible to
use two zero-capacity quantum channels with a positive joint capacity for their
output. Currently, we have no theoretical background to describe all possible
combinations of superactive zero-capacity channels; hence, there may be many
other possible combinations. In practice, to discover such superactive
zero-capacity channel-pairs, we must analyze an extremely large set of possible
quantum states, channel models, and channel probabilities. There is still no
extremely efficient algorithmic tool for this purpose. This paper shows an
efficient algorithmical method of finding such combinations. Our method can be
a very valuable tool for improving the results of fault-tolerant quantum
computation and possible communication techniques over very noisy quantum
channels.Comment: 35 pages, 17 figures, Journal-ref: Information Sciences (Elsevier,
2012), presented in part at Quantum Information Processing 2012 (QIP2012),
v2: minor changes, v3: published version; Information Sciences, Elsevier,
ISSN: 0020-0255; 201
Markerless visual servoing on unknown objects for humanoid robot platforms
To precisely reach for an object with a humanoid robot, it is of central
importance to have good knowledge of both end-effector, object pose and shape.
In this work we propose a framework for markerless visual servoing on unknown
objects, which is divided in four main parts: I) a least-squares minimization
problem is formulated to find the volume of the object graspable by the robot's
hand using its stereo vision; II) a recursive Bayesian filtering technique,
based on Sequential Monte Carlo (SMC) filtering, estimates the 6D pose
(position and orientation) of the robot's end-effector without the use of
markers; III) a nonlinear constrained optimization problem is formulated to
compute the desired graspable pose about the object; IV) an image-based visual
servo control commands the robot's end-effector toward the desired pose. We
demonstrate effectiveness and robustness of our approach with extensive
experiments on the iCub humanoid robot platform, achieving real-time
computation, smooth trajectories and sub-pixel precisions
Automation of the matrix element reweighting method
Matrix element reweighting is a powerful experimental technique widely
employed to maximize the amount of information that can be extracted from a
collider data set. We present a procedure that allows to automatically evaluate
the weights for any process of interest in the standard model and beyond. Given
the initial, intermediate and final state particles, and the transfer functions
for the final physics objects, such as leptons, jets, missing transverse
energy, our algorithm creates a phase-space mapping designed to efficiently
perform the integration of the squared matrix element and the transfer
functions. The implementation builds up on MadGraph, it is completely
automatized and publicly available. A few sample applications are presented
that show the capabilities of the code and illustrate the possibilities for new
studies that such an approach opens up.Comment: 41 pages, 21 figure
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Comparison of Current Gravity Estimation and Determination Models
This paper will discuss the history of gravity estimation and determination models while analyzing methods that are in development. Some fundamental methods for calculating the gravity field include spherical harmonics solutions, local weighted interpolation, and global point mascon modeling (PMC). Recently, high accuracy measurements have become more accessible, and the requirements for high order geopotential modeling have become more stringent. Interest in irregular bodies, accurate models of the hydrological system, and on-board processing has demanded a comprehensive model that can quickly and accurately compute the geopotential with low memory costs. This trade study of current geopotential modeling techniques will reveal that each modeling technique has a unique use case. It is notable that the spherical harmonics model is relatively accurate but poses a cumbersome inversion problem. PMC and interpolation models, on the other hand, are computationally efficient, but require more research to become robust models with high levels of accuracy. Considerations of the trade study will suggest further research for the point mascon model. The PMC model should be improved through mascon refinement, direct solutions that stem from geodetic measurements, and further validation of the gravity gradient. Finally, the potential for each model to be implemented with parallel computation will be shown to lead to large improvements in computing time while reducing the memory cost for each technique.Aerospace Engineering and Engineering Mechanic
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