Hierarchical Set Decision Diagrams and Regular Models

This paper presents algorithms and data structures that exploit a compositional and hierarchical specification to enable more efficient symbolic model-checking. We encode the state space and transition relation using hierarchical Set Decision Diagrams (SDD) [9]. In SDD, arcs of the structure are labeled with sets, themselves stored as SDD. To exploit the hierarchy of SDD, a structured model representation is needed. We thus introduce a formalism integrating a simple notion of type and instance. Complex composite behaviors are obtained using a synchronization mechanism borrowed from process calculi. Using this relatively general framework, we investigate how to capture similarities in regular and concurrent models. Experimental results are presented, showing that this approach can outperform in time and memory previous work in this area

Parallel symbolic state-space exploration is difficult, but what is the alternative?

State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal

Информационные технологии в банковской системе

Almost all activities of the Bank subject to the domination systems. The system itself involves a procedure control, a set of interconnected elements, procedures, methods, and many similar concepts. When the Bank is recruiting employees, it applies to this particular system, which involves placing ads on job interviews, the definition of appropriate skills, discussion of working conditions and so on. This process is a slender organized system with its internal procedures and prescribed norms

Exposure to Endocrine Disruptors and Nuclear Receptors Gene Expression in Infertile and Fertile Men from Italian Areas with Different Environmental Features

Internal levels of selected endocrine disruptors (EDs) (i.e., perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), di-2-ethylhexyl-phthalate (DEHP), mono-(2-ethylhexyl)-phthalate (MEHP), and bisphenol A (BPA)) were analyzed in blood/serum of infertile and fertile men from metropolitan, urban and rural Italian areas. PFOS and PFOA levels were also evaluated in seminal plasma. In peripheral blood mononuclear cells (PBMCs) of same subjects, gene expression levels of a panel of nuclear receptors (NRs), namely estrogen receptor α (ERα) estrogen receptor β (ERβ), androgen receptor (AR), aryl hydrocarbon receptor (AhR), peroxisome proliferator-activated receptor γ (PPARγ) and pregnane X receptor (PXR) were also assessed. Infertile men from the metropolitan area had significantly higher levels of BPA and gene expression of all NRs, except PPARγ, compared to subjects from other areas. Subjects from urban areas had significantly higher levels of MEHP, whereas subjects from rural area had higher levels of PFOA in both blood and seminal plasma. Interestingly, ERα, ERβ, AR, PXR and AhR expression is directly correlated with BPA and inversely correlated with PFOA serum levels. Our study indicates the relevance of the living environment when investigating the exposure to specific EDs. Moreover, the NRs panel in PBMCs demonstrated to be a potential biomarker of effect to assess the EDs impact on reproductive health

Component-wise incremental LTL model checking

Efficient symbolic and explicit-state model checking approaches have been developed for the verification of linear time temporal logic (LTL) properties. Several attempts have been made to combine the advantages of the various algorithms. Model checking LTL properties usually poses two challenges: one must compute the synchronous product of the state space and the automaton model of the desired property, then look for counterexamples that is reduced to finding strongly connected components (SCCs) in the state space of the product. In case of concurrent systems, where the phenomenon of state space explosion often prevents the successful verification, the so-called saturation algorithm has proved its efficiency in state space exploration. This paper proposes a new approach that leverages the saturation algorithm both as an iteration strategy constructing the product directly, as well as in a new fixed-point computation algorithm to find strongly connected components on-the-fly by incrementally processing the components of the model. Complementing the search for SCCs, explicit techniques and component-wise abstractions are used to prove the absence of counterexamples. The resulting on-the-fly, incremental LTL model checking algorithm proved to scale well with the size of models, as the evaluation on models of the Model Checking Contest suggests

SAT0461 SHORT-TERM MONITORING OF DENOSUMAB EFFECT IN BREAST CANCER PATIENTS RECEIVING AROMATASE INHIBITORS USING REMS TECHNOLOGY ON LUMBAR SPINE

Background:Aromatase inhibitor (AI) therapy in women with estrogen receptor-positive (ER+) breast cancer (BC) causes accelerated bone loss and increased risk of osteoporosis and fractures as side effects. Denosumab (i.e. 60 mg twice a year) is a viable therapy against bone resorption, but the short-term monitoring of bone mineral density (BMD) change with time is still an unmet clinical need, since the current techniques (including dual-energy X-ray absorptiometry, DXA) require 1-2 years between two consecutive measurements [1]. Radiofrequency Echographic Multi Spectrometry (REMS), with high performance in terms of precision and repeatability [2], might be used in this setting of patients for short-term monitoring of bone health-related parameters.Objectives:The objective is the short-term monitoring of the effect of AIs with/without denosumab on bone health in BC patients using REMS and DXA scans at lumbar spine.Methods:Post-menopausal ER+ BC patients treated with adjuvant AIs were recruited. Two subgroups were identified, whether receiving also 60 mg of denosumab therapy every 6 months or not (named Group A and Group B, respectively). All patients underwent baseline DXA and REMS lumbar spine scans at time T0, previous to the first AI therapy, and after 12 months (time T1). REMS scan only was repeated also at 18 months (T2), since a 6-month interval between two consecutive scans is not recommended for DXA. The bone mineral density (BMD) was measured with both techniques.Results:Overall, 254 ER+ BC patients were enrolled (127 per group). The effect of denosumab on BMD is reported in Table. The BMD values obtained by DXA and REMS were not significantly different at T0 and T1, whereas the difference between Group A and B at T1 was statistically significant (p<0.001) both for REMS and DXA. At T2, REMS confirmed the increasing trend of BMD for Group A and the decreasing one for Group B, and the difference between groups was statistically significant (p<0.001). For each time point and each group, there were not statistically significant differences between DXA and REMS.Conclusion:Several studies have shown the effect of denosumab on BMD over a period not less than 2 years from the start of treatment. This study showed the feasibility of short-term follow-up using REMS lumbar spine scans at 6-month time steps.References:[1]Diez-Perez A et al, Aging Clin Exp Res 2019;31(10):1375–89[2]Di Paola M et al, Osteoporos Int 2018;30:391–402Table 1.BMD values, expressed as g/cm2, measured by DXA and REMS for Group A (patients receiving AIs only) and Group B (patients receiving AIs and denosumab) at baseline (T0), 12 months (T1) and 18 months (T2) from the start of therapy. Results are presented as median values with 25thand 75thpercentiles. P-values are obtained with a Mann-Whitney test.DXAREMSScan timeGroup AGroup BpGroup AGroup BpT00.840 (0.719-0.959)0.867 (0.723-0.958)0.990.833 (0.708-0.949)0.855 (0.714-0.973)0.77T10.823 (0.702-0.944)0.889 (0.749-0.990)0.0030.819 (0.691-0.927)0.887 (0.740-1.018)<0.001T2---0.801 (0.679-0.909)0.899 (0.754-1.020)<0.001Note:The authorsD. Ciardo, M. Ciccarese, F. Conversano, M. Di Paola, R. Forcignanò, A. Grimaldi, F.A. Lombardi, M. Muratore and P. Pisaniare listed in alphabetical orderDisclosure of Interests:None declare