13,290 research outputs found
Proving Differential Privacy with Shadow Execution
Recent work on formal verification of differential privacy shows a trend
toward usability and expressiveness -- generating a correctness proof of
sophisticated algorithm while minimizing the annotation burden on programmers.
Sometimes, combining those two requires substantial changes to program logics:
one recent paper is able to verify Report Noisy Max automatically, but it
involves a complex verification system using customized program logics and
verifiers.
In this paper, we propose a new proof technique, called shadow execution, and
embed it into a language called ShadowDP. ShadowDP uses shadow execution to
generate proofs of differential privacy with very few programmer annotations
and without relying on customized logics and verifiers. In addition to
verifying Report Noisy Max, we show that it can verify a new variant of Sparse
Vector that reports the gap between some noisy query answers and the noisy
threshold. Moreover, ShadowDP reduces the complexity of verification: for all
of the algorithms we have evaluated, type checking and verification in total
takes at most 3 seconds, while prior work takes minutes on the same algorithms.Comment: 23 pages, 12 figures, PLDI'1
ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans
We introduce ScanComplete, a novel data-driven approach for taking an
incomplete 3D scan of a scene as input and predicting a complete 3D model along
with per-voxel semantic labels. The key contribution of our method is its
ability to handle large scenes with varying spatial extent, managing the cubic
growth in data size as scene size increases. To this end, we devise a
fully-convolutional generative 3D CNN model whose filter kernels are invariant
to the overall scene size. The model can be trained on scene subvolumes but
deployed on arbitrarily large scenes at test time. In addition, we propose a
coarse-to-fine inference strategy in order to produce high-resolution output
while also leveraging large input context sizes. In an extensive series of
experiments, we carefully evaluate different model design choices, considering
both deterministic and probabilistic models for completion and semantic
inference. Our results show that we outperform other methods not only in the
size of the environments handled and processing efficiency, but also with
regard to completion quality and semantic segmentation performance by a
significant margin.Comment: Video: https://youtu.be/5s5s8iH0NF
Degree of Sequentiality of Weighted Automata
Weighted automata (WA) are an important formalism to describe quantitative properties. Obtaining equivalent deterministic machines is a longstanding research problem. In this paper we consider WA with a set semantics, meaning that the semantics is given by the set of weights of accepting runs. We focus on multi-sequential WA that are defined as finite unions of sequential WA. The problem we address is to minimize the size of this union. We call this minimum the degree of sequentiality of (the relation realized by) the WA.
For a given positive integer k, we provide multiple characterizations of relations realized by a union of k sequential WA over an infinitary finitely generated group: a Lipschitz-like machine independent property, a pattern on the automaton (a new twinning property) and a subclass of cost register automata. When possible, we effectively translate a WA into an equivalent union of k sequential WA. We also provide a decision procedure for our twinning property for commutative computable groups thus allowing to compute the degree of sequentiality. Last, we show that these results also hold for word transducers and that the associated decision problem is PSPACE
-complete
Parallel BioScape: A Stochastic and Parallel Language for Mobile and Spatial Interactions
BioScape is a concurrent language motivated by the biological landscapes
found at the interface of biology and biomaterials. It has been motivated by
the need to model antibacterial surfaces, biofilm formation, and the effect of
DNAse in treating and preventing biofilm infections. As its predecessor, SPiM,
BioScape has a sequential semantics based on Gillespie's algorithm, and its
implementation does not scale beyond 1000 agents. However, in order to model
larger and more realistic systems, a semantics that may take advantage of the
new multi-core and GPU architectures is needed. This motivates the introduction
of parallel semantics, which is the contribution of this paper: Parallel
BioScape, an extension with fully parallel semantics.Comment: In Proceedings MeCBIC 2012, arXiv:1211.347
ROSA Analyser: An automatized approach to analyse processes of ROSA
In this work we present the first version of ROSA Analyser, a tool designed
to get closer to a fully automatic process of analysing the behaviour of a
system specified as a process of the Markovian Process Algebra ROSA. In this
first development stage, ROSA Analyser is able to generate the Labelled
Transition System, according to ROSA Operational Semantics.
ROSA Analyser performance starts with the Syntactic Analysis so generating a
layered structure, suitable to then, apply the Operational Semantics Transition
rules in the easier way. ROSA Analyser is able to recognize some states
identities deeper than the Syntactic ones. This is the very first step in the
way to reduce the size of the LTS and then to avoid the state explosion
problem, so making this task more tractable.
For the sake of better illustrating the usefulness of ROSA Analyser, a case
study is also provided within this work.Comment: In Proceedings WS-FMDS 2012, arXiv:1207.1841. Formal model's too
Correct and Efficient Antichain Algorithms for Refinement Checking
The notion of refinement plays an important role in software engineering. It
is the basis of a stepwise development methodology in which the correctness of
a system can be established by proving, or computing, that a system refines its
specification. Wang et al. describe algorithms based on antichains for
efficiently deciding trace refinement, stable failures refinement and
failures-divergences refinement. We identify several issues pertaining to the
soundness and performance in these algorithms and propose new, correct,
antichain-based algorithms. Using a number of experiments we show that our
algorithms outperform the original ones in terms of running time and memory
usage. Furthermore, we show that additional run time improvements can be
obtained by applying divergence-preserving branching bisimulation minimisation
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