ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

ion Refinement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Pei-Hsin Ho

Automated Technology for Verification and Analysis

A counterexample to the satisfaction of a linear property $$\psi $$
 in a system $${\mathcal {S}}$$
 is an infinite computation of $${\mathcal {S}}$$
 that violates $$\psi $$
 . When $$\psi $$
 is a safety property, a counterexample to its satisfaction need not be infinite. Rather, it is a bad-prefix for $$\psi $$
 : a finite word all whose extensions violate $$\psi $$
 . The existence of finite counterexamples is very helpful in practice. Liveness properties do not have bad-prefixes and thus do not have finite counterexamples. We extend the notion of finite counterexamples to non-safety properties. We study counterable languages—ones that have at least one bad-prefix. Thus, a language is counterable iff it is not liveness. Three natural problems arise: (1) given a language, decide whether it is counterable, (2) study the length of minimal bad-prefixes for counterable languages, and (3) develop algorithms for detecting bad-prefixes for counterable languages. We solve the problems for languages given by means of LTL formulas or nondeterministic Buchi automata. In particular, our EXPSPACE-completeness proof for the problem of deciding whether a given LTL formula is counterable, and hence also for deciding liveness, settles a long-standing open problem. In addition, we make finite counterexamples more relevant and helpful by introducing two variants of the traditional definition of bad-prefixes. The first adds a probabilistic component to the definition. There, a prefix is bad if almost all its extensions violate the property. The second makes it relative to the system. There, a prefix is bad if all its extensions in the system violate the property. We also study the combination of the probabilistic and relative variants. Our framework suggests new variants also for safety and liveness languages. We solve the above three problems for the different variants. Interestingly, the probabilistic variant not only increases the chances to return finite counterexamples, but also makes the solution of the three problems exponentially easier.

/pdf/on-relative-and-probabilistic-finite-counterability-3likedjvvb.pdf

On relative and probabilistic finite counterability

Abstract Objective Clinical and surgical decisions for glioblastoma patients depend on a tumor imaging-based evaluation. Artificial Intelligence (AI) can be applied to magnetic resonance imaging (MRI) assessment to support clinical practice, surgery planning and prognostic predictions. In a real-world context, the current obstacles for AI are low-quality imaging and postoperative reliability. The aim of this study is to train an automatic algorithm for glioblastoma segmentation on a clinical MRI dataset and to obtain reliable results both pre- and post-operatively. Methods The dataset used for this study comprises 237 (71 preoperative and 166 postoperative) MRIs from 71 patients affected by a histologically confirmed Grade IV Glioma. The implemented U-Net architecture was trained by transfer learning to perform the segmentation task on postoperative MRIs. The training was carried out first on BraTS2021 dataset for preoperative segmentation. Performance is evaluated using DICE score (DS) and Hausdorff 95% (H95). Results In preoperative scenario, overall DS is 91.09 (± 0.60) and H95 is 8.35 (± 1.12), considering tumor core, enhancing tumor and whole tumor (ET and edema). In postoperative context, overall DS is 72.31 (± 2.88) and H95 is 23.43 (± 7.24), considering resection cavity (RC), gross tumor volume (GTV) and whole tumor (WT). Remarkably, the RC segmentation obtained a mean DS of 63.52 (± 8.90) in postoperative MRIs. Conclusions The performances achieved by the algorithm are consistent with previous literature for both pre-operative and post-operative glioblastoma’s MRI evaluation. Through the proposed algorithm, it is possible to reduce the impact of low-quality images and missing sequences. 

Deep learning-based algorithm for postoperative glioblastoma MRI segmentation: a promising new tool for tumor burden assessment

Variable automata with arithmetic enable the specification of reactive systems with variables over an infinite domain of numeric values and whose operation involves arithmetic manipulation of these values [9]. We study the synthesis problem for such specifications. While the problem is in general undecidable, we define a fragment, namely semantically deterministic variable automata with arithmetic, for which the problem is decidable. Essentially, an automaton is semantically deterministic if the restrictions on the possible assignments to the variables that are accumulated along its runs resolve its nondeterministic choices. We show that semantically deterministic automata can specify many interesting behaviors – many more than deterministic ones, and that the synthesis problem for them can be reduced to a solution of a two-player game. For automata with simple guards, the game has a finite state space, and the synthesis problem can be solved in time polynomial in the automaton and exponential in the number of its variables.

On Synthesis of Specifications with Arithmetic

The computational bottleneck in model-checking applications is the blow-up involved in the translation of systems to their mathematical model. This blow up is especially painful in systems with variables over an infinite domain, and in composite systems described by means of their underlying components. We introduce and study linear temporal logic with arithmetic (LTLA, for short), where formulas include variables that take values in Z, and in which linear arithmetic over these values is supported. We develop an automata-theoretic approach for reasoning about LTLA formulas and use it in order to solve, in PSPACE, the satisfiability problem for the existential fragment of LTLA and the model-checking problem for its universal fragment. We show that these results are tight, as a single universallyquantified variable makes the satisfiability problem for LTLA undecidable. In addition to reasoning about systems with variables over Z, we suggest applications of LTLA in reasoning about hierarchical systems, which consist of subsystems that can call each other in a hierarchical manner. We use the values in Z in order to describe the nesting depth of components in the system. A naive model-checking algorithm for hierarchical systems flattens them, which involves an exponential blow up. We suggest a model-checking algorithm that avoids the flattening and avoids a blow up in the number of components.

/pdf/ltl-with-arithmetic-and-its-applications-in-reasoning-about-lxxgzx3jma.pdf

LTL with Arithmetic and its Applications in Reasoning about Hierarchical Systems

Of special interest in formal verification are safety specifications, which assert that the system stays within some allowed region, in which nothing “bad” happens. Equivalently, a computation violates a safety specification if it has a “bad prefix”—a prefix all whose extensions violate the specification. The theoretical properties of safety specifications as well as their practical advantages with respect to general specifications have been widely studied. Safety is binary: a specification is either safety or not safety. We introduce a quantitative measure for safety. Intuitively, the safety level of a language L measures the fraction of words not in L that have a bad prefix. In particular, a safety language has safety level 1 and a liveness language has safety level 0. Thus, our study spans the spectrum between traditional safety and liveness. The formal definition of safety level is based on probability and measures the probability of a random word not in L to have a bad prefix. We study the problem of finding the safety level of languages given by means of deterministic and nondeterministic automata as well as LTL formulas, and the problem of deciding their membership in specific classes along the spectrum (safety, almost-safety, fraction-safety, etc.). We also study properties of the different classes and the structure of deterministic automata for them.

/pdf/spanning-the-spectrum-from-safety-to-liveness-gzuskw2khg.pdf

Spanning the spectrum from safety to liveness

Of special interest in formal verification are safety specifications, which assert that the system stays within some allowed region, in which nothing “bad” happens. Equivalently, a computation violates a safety specification if it has a “bad prefix” – a prefix all whose extensions violate the specification. The theoretical properties of safety specifications as well as their practical advantages with respect to general specifications have been widely studied. Safety is binary: a specification is either safety or not safety. We introduce a quantitative measure for safety. Intuitively, the safety level of a language L measures the fraction of words not in L that have a bad prefix. In particular, a safety language has safety level 1 and a liveness language has safety level 0. Thus, our study spans the spectrum between traditional safety and liveness. The formal definition of safety level is based on probability and measures the probability of a random word not in L to have a bad prefix. We study the problem of finding the safety level of languages given by means of deterministic and nondeterministic automata as well as LTL formulas, and the problem of deciding their membership in specific classes along the spectrum (safety, almost-safety, fraction-safety, etc.). We also study properties of the different classes and the structure of deterministic automata for them.

Spanning the Spectrum from Safety to Liveness

Segmentation of post-operative glioblastoma multiforme (GBM) is essential for the planning of Tumor Treating Fields (TTFields) treatment and other clinical applications. Recent methods developed for pre-operative GBM segmentation perform poorly on post-operative GBM MRI scans. In this paper we present a method for the segmentation of GBM in post-operative patients. Our method incorporates an ensemble of segmentation networks and the Kullback–Leibler divergence agreement score in the objective function to estimate the prediction label uncertainty and cope with noisy labels and inter-observer variability. Moreover, our method integrates the surgery type and computes non-tumorous tissue delineation to automatically segment the tumor. We trained and validated our method on a dataset of 340 enhanced T1 MRI scans of patients that were treated with TTFields (270 scans for train and 70 scans for test). For validation, we developed a tool that uses the uncertainty map along with the segmentation result. Our tool allows visualization and fast editing of the tissues to improve the results dependent on user preference. Three physicians reviewed and graded our segmentation and editing tool on 12 different MRI scans. The validation set average (SD) Dice scores were 0.81 (0.11), 0.71 (0.24), 0.64 (0.25), and 0.68 (0.19) for whole-tumor, resection, necrotic-core, and enhancing-tissue, respectively. The physicians rated 72% of the segmented GBMs acceptable for treatment planning or better. Another 22% can be edited manually in a reasonable time to achieve a clinically acceptable result. According to these results, the proposed method for GBM segmentation can be integrated into TTFields treatment planning software in order to shorten the planning process. To conclude, we have extended a state-of-the-art pre-operative GBM segmentation method with surgery-type, anatomical information, and uncertainty visualization to facilitate a clinically viable segmentation of post-operative GBM for TTFields treatment planning.

/pdf/post-operative-glioblastoma-multiforme-segmentation-with-ip8plish.pdf

Post-operative glioblastoma multiforme segmentation with uncertainty estimation

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