A Faithful Semantics for Generalised Symbolic Trajectory Evaluation

Generalised Symbolic Trajectory Evaluation (GSTE) is a high-capacity formal verification technique for hardware. GSTE uses abstraction, meaning that details of the circuit behaviour are removed from the circuit model. A semantics for GSTE can be used to predict and understand why certain circuit properties can or cannot be proven by GSTE. Several semantics have been described for GSTE. These semantics, however, are not faithful to the proving power of GSTE-algorithms, that is, the GSTE-algorithms are incomplete with respect to the semantics. The abstraction used in GSTE makes it hard to understand why a specific property can, or cannot, be proven by GSTE. The semantics mentioned above cannot help the user in doing so. The contribution of this paper is a faithful semantics for GSTE. That is, we give a simple formal theory that deems a property to be true if-and-only-if the property can be proven by a GSTE-model checker. We prove that the GSTE algorithm is sound and complete with respect to this semantics

The association between reduced knee joint proprioception and medial meniscal abnormalities using MRI in knee osteoarthritis: results from the Amsterdam osteoarthritis cohort.

BACKGROUND: Osteoarthritis (OA) of the knee is characterized by pain and activity limitations. In knee OA, proprioceptive accuracy is reduced and might be associated with pain and activity limitations. Although causes of reduced proprioceptive accuracy are divergent, medial meniscal abnormalities, which are highly prevalent in knee OA, have been suggested to play an important role. No study has focussed on the association between proprioceptive accuracy and meniscal abnormalities in knee OA. OBJECTIVE: To explore the association between reduced proprioceptive accuracy and medial meniscal abnormalities in a clinical sample of knee OA subjects. METHODS: Cross-sectional study in 105 subjects with knee OA. Knee proprioceptive accuracy was assessed by determining the joint motion detection threshold in the knee extension direction. The knee was imaged with a 3.0 T magnetic resonance (MR) scanner. Number of regions with medial meniscal abnormalities and the extent of abnormality in the anterior and posterior horn and body were scored according to the Boston-Leeds Osteoarthritis Knee Score (BLOKS) method. Multiple regression analyzes were used to examine whether reduced proprioceptive accuracy was associated with medial meniscal abnormalities in knee OA subjects. RESULTS: Mean proprioceptive accuracy was 2.9degree + 1.9degree. Magnetic resonance imaging (MRI)-detected medial meniscal abnormalities were found in the anterior horn (78%), body (80%) and posterior horn (90%). Reduced proprioceptive accuracy was associated with both the number of regions with meniscal abnormalities (P < 0.01) and the extent of abnormality (P = 0.02). These associations were not confounded by muscle strength, joint laxity, pain, age, gender, body mass index (BMI) and duration of knee complaints. CONCLUSION: This is the first study showing that reduced proprioceptive accuracy is associated with medial meniscal abnormalities in knee OA. The study highlights the importance of meniscal abnormalities in understanding reduced proprioceptive accuracy in persons with knee OA. Copyright 2013 Osteoarthritis Research Society International. All rights reserve

Role of the general practitioner during the active breast cancer treatment phase: an analysis of health care use

PURPOSE: Little is known about the actual involvement of the general practitioner (GP) during the active breast cancer treatment phase. Therefore, this study explored (disease-specific) primary health care use among women undergoing active treatment for breast cancer compared with women without breast cancer. METHODS: A total of 185 women with a first diagnosis of early-stage breast cancer between 1998 and 2007 were identified in the primary care database of the Registration Network Groningen and matched with a reference population of 548 women without breast cancer on birth year and GP. RESULTS: Since diagnosis, patients with breast cancer had twice as many face-to-face contacts compared with women from the reference population (median 6.0 vs 3.0/year, Mann-Whitney (M-W) test p < 0.001). The median number of drug prescriptions and referrals was also significantly higher among patients than among the reference population (11.0 vs 7.0/year, M-W test p < 0.001 and 1.0 vs 0.0/year, M-W test p < 0.001). More patients than women from the reference population had face-to-face contacts or were prescribed drugs for reasons related to breast cancer and its treatment, including gastrointestinal problems, psychological reasons and endocrine therapy. CONCLUSIONS: During the active breast cancer treatment phase, GPs are involved in the management of treatment-related side effects and psychological symptoms, as well as in the administration of endocrine therapy. Based on the findings of this study, interventions across the primary/secondary interface can be planned to improve quality of life and other outcomes in patients undergoing breast cancer treatment

Sat-based assistance in abstraction refinement for symbolic trajectory evaluation

Abstract. We present a SAT-based algorithm for assisting users of Symbolic Trajectory Evaluation (STE) in manual abstraction refinement. We demonstrate the usefulness of the algorithm on a larger case study (the verification of a CAM).

Semantics, Decision Procedures, and Abstraction Refinement for Symbolic Trajectory Evaluation

The rapid growth in hardware complexity has led to a need for formal verification of hardware designs to prevent bugs from entering the final silicon. Model-checking is a verification method in which a model of a system is checked against a property, describing the desired behaviour of the system over time. Today, all major hardware companies use model-checkers in order to reduce the number of bugs in their designs.Symbolic Trajectory Evaluation (STE) is a model-checking technique for hardware. STE uses abstraction, meaning that details of the circuit behaviour are removed from the circuit model. This improves the capacity limits of the method, but has as down-side that certain properties cannot be proved if the wrong abstraction is chosen. STE is limited to properties ranging over a finite numberof time-steps. Generalised Symbolic Trajectory Evaluation (GSTE) is an extension of STE that can deal with properties ranging over unbounded time.This thesis describes several important contributions to research on STE and GSTE.First of all, the thesis describes a SAT-based method for abstraction refinement in STE. A main drawback of STE is that the user needs to spend time on finding the right abstraction. Often, a great deal of time is spent on such manual abstraction refinement.To address this problem, we have invented a method for assisting STE users with manual abstraction refinement. As a case study, we have demonstrated the usefulness of the algorithm by showing how to refine and verify an STE specification of a Content-Addressable Memory (CAM).Furthermore, the thesis describes faithful semantics for STE and GSTE.The reason for developing these semantics is that we have discovered thatthe existing semantics for STE and GSTE do not correspond to the proving power of the corresponding model-checking algorithms.We believe that the semantics are an important contribution for at least two reasons. First of all, a faithful semantics makes STE and GSTE more accessible to novice users: a faithful semantics enables users to understand the abstraction used in STE and GSTE, without having to understand the details of the model-checking algorithm.Secondly, a faithful semantics can be used as basis for research on new model-checking algorithms and other tools for STE and GSTE.To illustrate this, building upon our faithful semantics for STE, we have developed the third contribution of this thesis: a new SAT-based model-checking algorithm for STE. In the thesis, we demonstrate on a series of benchmarks that our new algorithm outperforms other SAT-based model-checking algorithms for STE

Belief Updates in Multiple Agent Systems

We give a model for a multi-agent system which describes how the knowledge and beliefs of agents should change when they receive new information. The formal tool we use for this description is a combination of modal and dynamic logic. Two core notions in our model of belief update are the expansion of the knowledge and beliefs of an agent, and the processing of new information by an agent. An expansion has been defined as the change in the knowledge and beliefs of an agent when it decides to believe an incoming formula while holding on to its current propositional beliefs. To prevent our agents from forming inconsistent beliefs they do not expand with every piece of information they receive. Instead of that, our agents remember their original beliefs (the beliefs they had before receiving any information) and every piece of information they receive

Semantics, Decision Procedures, and Abstraction Refinement for Symbolic Trajectory Evaluation

The rapid growth in hardware complexity has led to a need for formal verification of hardware designs to prevent bugs from entering the final silicon. Model-checking is a verification method in which a model of a system is checked against a property, describing the desired behaviour of the system over time. Today, all major hardware companies use model-checkers in order to reduce the number of bugs in their designs.Symbolic Trajectory Evaluation (STE) is a model-checking technique for hardware. STE uses abstraction, meaning that details of the circuit behaviour are removed from the circuit model. This improves the capacity limits of the method, but has as down-side that certain properties cannot be proved if the wrong abstraction is chosen. STE is limited to properties ranging over a finite numberof time-steps. Generalised Symbolic Trajectory Evaluation (GSTE) is an extension of STE that can deal with properties ranging over unbounded time.This thesis describes several important contributions to research on STE and GSTE.First of all, the thesis describes a SAT-based method for abstraction refinement in STE. A main drawback of STE is that the user needs to spend time on finding the right abstraction. Often, a great deal of time is spent on such manual abstraction refinement.To address this problem, we have invented a method for assisting STE users with manual abstraction refinement. As a case study, we have demonstrated the usefulness of the algorithm by showing how to refine and verify an STE specification of a Content-Addressable Memory (CAM).Furthermore, the thesis describes faithful semantics for STE and GSTE.The reason for developing these semantics is that we have discovered thatthe existing semantics for STE and GSTE do not correspond to the proving power of the corresponding model-checking algorithms.We believe that the semantics are an important contribution for at least two reasons. First of all, a faithful semantics makes STE and GSTE more accessible to novice users: a faithful semantics enables users to understand the abstraction used in STE and GSTE, without having to understand the details of the model-checking algorithm.Secondly, a faithful semantics can be used as basis for research on new model-checking algorithms and other tools for STE and GSTE.To illustrate this, building upon our faithful semantics for STE, we have developed the third contribution of this thesis: a new SAT-based model-checking algorithm for STE. In the thesis, we demonstrate on a series of benchmarks that our new algorithm outperforms other SAT-based model-checking algorithms for STE