Deadlock-Detection in Component-Based Systems is NP-hard.

We present a polynomial-time reduction from 3-SAT to DLIS, where DLIS is the set of interaction systems for which a deadlock-state is reachable

Deciding Liveness in Component-Based Systems is NP-hard

Interaction systems are a formal model for component-based systems. Combining components via connectors to form more complex systems may give rise to deadlock situations. In a system that has been shown to be deadlock-free one can ask if a set of components is live. We present here a polynomial time reduction from 3-SAT to the question whether a set of components is live in a deadlock-free system

Interaction Systems and 1-safe Petri Nets

Interaction systems are a formal model for component-based systems, where components are combined via connectors to form more complex systems. We compare interaction systems (IS) to the wellstudied model of 1-safe Petri nets (1SN) by giving a translation map1: 1SN → IS and a translation map2: IS → 1SN, so that a 1-safe Petri net (an interaction system) and its according interaction system (1-safe Petri net) defined by the respective mapping are isomorphic up to some label relation R. So in some sense both models share the same expressiveness. Also, the encoding map1 is polynomial and can be used to reduce the problems of reachability, deadlock and liveness in 1SN to the problems of reachability, deadlock and liveness in IS, yielding PSPACE-hardness for these questions

Consistent ICP for the registration of sparse and inhomogeneous point clouds

In this paper, we derive a novel iterative closest point (ICP) technique that performs point cloud alignment in a robust and consistent way. Traditional ICP techniques minimize the point-to-point distances, which are successful when point clouds contain no noise or clutter and moreover are dense and more or less uniformly sampled. In the other case, it is better to employ point-to-plane or other metrics to locally approximate the surface of the objects. However, the point-to-plane metric does not yield a symmetric solution, i.e. the estimated transformation of point cloud p to point cloud q is not necessarily equal to the inverse transformation of point cloud q to point cloud p. In order to improve ICP, we will enforce such symmetry constraints as prior knowledge and make it also robust to noise and clutter. Experimental results show that our method is indeed much more consistent and accurate in presence of noise and clutter compared to existing ICP algorithms

On the Limits and Practice of Automatically Designing Self-Stabilization

A protocol is said to be self-stabilizing when the distributed system executing it is guaranteed to recover from any fault that does not cause permanent damage. Designing such protocols is hard since they must recover from all possible states, therefore we investigate how feasible it is to synthesize them automatically. We show that synthesizing stabilization on a fixed topology is NP-complete in the number of system states. When a solution is found, we further show that verifying its correctness on a general topology (with any number of processes) is undecidable, even for very simple unidirectional rings. Despite these negative results, we develop an algorithm to synthesize a self-stabilizing protocol given its desired topology, legitimate states, and behavior. By analogy to shadow puppetry, where a puppeteer may design a complex puppet to cast a desired shadow, a protocol may need to be designed in a complex way that does not even resemble its specification. Our shadow/puppet synthesis algorithm addresses this concern and, using a complete backtracking search, has automatically designed 4 new self-stabilizing protocols with minimal process space requirements: 2-state maximal matching on bidirectional rings, 5-state token passing on unidirectional rings, 3-state token passing on bidirectional chains, and 4-state orientation on daisy chains

A computational group theoretic symmetry reduction package for the SPIN model checker

Symmetry reduced model checking is hindered by two problems: how to identify state space symmetry when systems are not fully symmetric, and how to determine equivalence of states during search. We present TopSpin, a fully automatic symmetry reduction package for the Spin model checker. TopSpin uses the Gap computational algebra system to effectively detect state space symmetry from the associated Promela specification, and to choose an efficient symmetry reduction strategy by classifying automorphism groups as a disjoint/wreath product of subgroups. We present encouraging experimental results for a variety of Promela examples