9,654 research outputs found
Applying Formal Methods to Networking: Theory, Techniques and Applications
Despite its great importance, modern network infrastructure is remarkable for
the lack of rigor in its engineering. The Internet which began as a research
experiment was never designed to handle the users and applications it hosts
today. The lack of formalization of the Internet architecture meant limited
abstractions and modularity, especially for the control and management planes,
thus requiring for every new need a new protocol built from scratch. This led
to an unwieldy ossified Internet architecture resistant to any attempts at
formal verification, and an Internet culture where expediency and pragmatism
are favored over formal correctness. Fortunately, recent work in the space of
clean slate Internet design---especially, the software defined networking (SDN)
paradigm---offers the Internet community another chance to develop the right
kind of architecture and abstractions. This has also led to a great resurgence
in interest of applying formal methods to specification, verification, and
synthesis of networking protocols and applications. In this paper, we present a
self-contained tutorial of the formidable amount of work that has been done in
formal methods, and present a survey of its applications to networking.Comment: 30 pages, submitted to IEEE Communications Surveys and Tutorial
On functional module detection in metabolic networks
Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models
Parameterized Model-Checking for Timed-Systems with Conjunctive Guards (Extended Version)
In this work we extend the Emerson and Kahlon's cutoff theorems for process
skeletons with conjunctive guards to Parameterized Networks of Timed Automata,
i.e. systems obtained by an \emph{apriori} unknown number of Timed Automata
instantiated from a finite set of Timed Automata templates.
In this way we aim at giving a tool to universally verify software systems
where an unknown number of software components (i.e. processes) interact with
continuous time temporal constraints. It is often the case, indeed, that
distributed algorithms show an heterogeneous nature, combining dynamic aspects
with real-time aspects. In the paper we will also show how to model check a
protocol that uses special variables storing identifiers of the participating
processes (i.e. PIDs) in Timed Automata with conjunctive guards. This is
non-trivial, since solutions to the parameterized verification problem often
relies on the processes to be symmetric, i.e. indistinguishable. On the other
side, many popular distributed algorithms make use of PIDs and thus cannot
directly apply those solutions
Petri nets for systems and synthetic biology
We give a description of a Petri net-based framework for
modelling and analysing biochemical pathways, which uni¯es the qualita-
tive, stochastic and continuous paradigms. Each perspective adds its con-
tribution to the understanding of the system, thus the three approaches
do not compete, but complement each other. We illustrate our approach
by applying it to an extended model of the three stage cascade, which
forms the core of the ERK signal transduction pathway. Consequently
our focus is on transient behaviour analysis. We demonstrate how quali-
tative descriptions are abstractions over stochastic or continuous descrip-
tions, and show that the stochastic and continuous models approximate
each other. Although our framework is based on Petri nets, it can be
applied more widely to other formalisms which are used to model and
analyse biochemical networks
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
Verification and Synthesis of Symmetric Uni-Rings for Leads-To Properties
This paper investigates the verification and synthesis of parameterized
protocols that satisfy leadsto properties on symmetric
unidirectional rings (a.k.a. uni-rings) of deterministic and constant-space
processes under no fairness and interleaving semantics, where and are
global state predicates. First, we show that verifying for
parameterized protocols on symmetric uni-rings is undecidable, even for
deterministic and constant-space processes, and conjunctive state predicates.
Then, we show that surprisingly synthesizing symmetric uni-ring protocols that
satisfy is actually decidable. We identify necessary and
sufficient conditions for the decidability of synthesis based on which we
devise a sound and complete polynomial-time algorithm that takes the predicates
and , and automatically generates a parameterized protocol that
satisfies for unbounded (but finite) ring sizes. Moreover, we
present some decidability results for cases where leadsto is required from
multiple distinct predicates to different predicates. To demonstrate
the practicality of our synthesis method, we synthesize some parameterized
protocols, including agreement and parity protocols
Prototyping Formal System Models with Active Objects
We propose active object languages as a development tool for formal system
models of distributed systems. Additionally to a formalization based on a term
rewriting system, we use established Software Engineering concepts, including
software product lines and object orientation that come with extensive tool
support. We illustrate our modeling approach by prototyping a weak memory
model. The resulting executable model is modular and has clear interfaces
between communicating participants through object-oriented modeling.
Relaxations of the basic memory model are expressed as self-contained variants
of a software product line. As a modeling language we use the formal active
object language ABS which comes with an extensive tool set. This permits rapid
formalization of core ideas, early validity checks in terms of formal invariant
proofs, and debugging support by executing test runs. Hence, our approach
supports the prototyping of formal system models with early feedback.Comment: In Proceedings ICE 2018, arXiv:1810.0205
TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions
Although deep learning approaches have had tremendous success in image, video
and audio processing, computer vision, and speech recognition, their
applications to three-dimensional (3D) biomolecular structural data sets have
been hindered by the entangled geometric complexity and biological complexity.
We introduce topology, i.e., element specific persistent homology (ESPH), to
untangle geometric complexity and biological complexity. ESPH represents 3D
complex geometry by one-dimensional (1D) topological invariants and retains
crucial biological information via a multichannel image representation. It is
able to reveal hidden structure-function relationships in biomolecules. We
further integrate ESPH and convolutional neural networks to construct a
multichannel topological neural network (TopologyNet) for the predictions of
protein-ligand binding affinities and protein stability changes upon mutation.
To overcome the limitations to deep learning arising from small and noisy
training sets, we present a multitask topological convolutional neural network
(MT-TCNN). We demonstrate that the present TopologyNet architectures outperform
other state-of-the-art methods in the predictions of protein-ligand binding
affinities, globular protein mutation impacts, and membrane protein mutation
impacts.Comment: 20 pages, 8 figures, 5 table
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