19,372 research outputs found
A Framework to Synergize Partial Order Reduction with State Interpolation
We address the problem of reasoning about interleavings in safety
verification of concurrent programs. In the literature, there are two prominent
techniques for pruning the search space. First, there are well-investigated
trace-based methods, collectively known as "Partial Order Reduction (POR)",
which operate by weakening the concept of a trace by abstracting the total
order of its transitions into a partial order. Second, there is state-based
interpolation where a collection of formulas can be generalized by taking into
account the property to be verified. Our main contribution is a framework that
synergistically combines POR with state interpolation so that the sum is more
than its parts
A Survey of Symbolic Execution Techniques
Many security and software testing applications require checking whether
certain properties of a program hold for any possible usage scenario. For
instance, a tool for identifying software vulnerabilities may need to rule out
the existence of any backdoor to bypass a program's authentication. One
approach would be to test the program using different, possibly random inputs.
As the backdoor may only be hit for very specific program workloads, automated
exploration of the space of possible inputs is of the essence. Symbolic
execution provides an elegant solution to the problem, by systematically
exploring many possible execution paths at the same time without necessarily
requiring concrete inputs. Rather than taking on fully specified input values,
the technique abstractly represents them as symbols, resorting to constraint
solvers to construct actual instances that would cause property violations.
Symbolic execution has been incubated in dozens of tools developed over the
last four decades, leading to major practical breakthroughs in a number of
prominent software reliability applications. The goal of this survey is to
provide an overview of the main ideas, challenges, and solutions developed in
the area, distilling them for a broad audience.
The present survey has been accepted for publication at ACM Computing
Surveys. If you are considering citing this survey, we would appreciate if you
could use the following BibTeX entry: http://goo.gl/Hf5FvcComment: This is the authors pre-print copy. If you are considering citing
this survey, we would appreciate if you could use the following BibTeX entry:
http://goo.gl/Hf5Fv
Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset
CRU TS (Climatic Research Unit gridded Time Series) is a widely used climate dataset on a 0.5 degrees latitude by 0.5 degrees longitude grid over all land domains of the world except Antarctica. It is derived by the interpolation of monthly climate anomalies from extensive networks of weather station observations. Here we describe the construction of a major new version, CRU TS v4. It is updated to span 1901-2018 by the inclusion of additional station observations, and it will be updated annually. The interpolation process has been changed to use angular-distance weighting (ADW), and the production of secondary variables has been revised to better suit this approach. This implementation of ADW provides improved traceability between each gridded value and the input observations, and allows more informative diagnostics that dataset users can utilise to assess how dataset quality might vary geographically
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks
Magnetic resonance image (MRI) in high spatial resolution provides detailed
anatomical information and is often necessary for accurate quantitative
analysis. However, high spatial resolution typically comes at the expense of
longer scan time, less spatial coverage, and lower signal to noise ratio (SNR).
Single Image Super-Resolution (SISR), a technique aimed to restore
high-resolution (HR) details from one single low-resolution (LR) input image,
has been improved dramatically by recent breakthroughs in deep learning. In
this paper, we introduce a new neural network architecture, 3D Densely
Connected Super-Resolution Networks (DCSRN) to restore HR features of
structural brain MR images. Through experiments on a dataset with 1,113
subjects, we demonstrate that our network outperforms bicubic interpolation as
well as other deep learning methods in restoring 4x resolution-reduced images.Comment: Accepted by ISBI'1
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