Higher Order Anomaly Consistency Conditions: Renormalization and Non-Locality

We study the Slavnov-Taylor Identities (STI) breaking terms, up to the second order in perturbation theory. We investigate which requirements are needed for the first order Wess-Zumino consistency condition to hold true at the next order in perturbation theory. We find that: a) If the cohomologically trivial contributions to the first order ST breaking terms are not removed by a suitable choice of the first order normalization conditions, the Wess-Zumino consistency condition is modified at the second order. b) Moreover, if one fails to recover the cohomologically trivial part of the first order STI breaking local functional, the second order anomaly actually turns out to be a non-local functional of the fields and the external sources. By using the Wess-Zumino consistency condition and the Quantum Action Principle, we show that the cohomological analysis of the first order STI breaking terms can actually be performed also in a model (the massive Abelian Higgs-Kibble one) where the BRST transformations are not nilpotent.Comment: 14 pages, 4 figures, Latex and packages amsfonts, amssymb, amsthm and eps

Program Semantics in Model-Based WCET Analysis: A State of the Art Perspective

Advanced design techniques of safety-critical applications use specialized development model based methods. Under this setting, the application exists at several levels of description, as the result of a sequence of transformations. On the positive side, the application is developed in a systematic way, while on the negative side, its high-level semantics may be obfuscated when represented at the lower levels. The application should provide certain functional and non-functional guarantees. When the application is a hard real-time program, such guarantees could be deadlines, thus making the computation of worst-case execution time (WCET) bounds mandatory. This paper overviews, in the context of WCET analysis, what are the existing techniques to extract, express and exploit the program semantics along the model-based development workflow

Non-functional properties in the model-driven development of service-oriented systems

Systems based on the service-oriented architecture (SOA) principles have become an important cornerstone of the development of enterprise-scale software applications. They are characterized by separating functions into distinct software units, called services, which can be published, requested and dynamically combined in the production of business applications. Service-oriented systems (SOSs) promise high flexibility, improved maintainability, and simple re-use of functionality. Achieving these properties requires an understanding not only of the individual artifacts of the system but also their integration. In this context, non-functional aspects play an important role and should be analyzed and modeled as early as possible in the development cycle. In this paper, we discuss modeling of non-functional aspects of service-oriented systems, and the use of these models for analysis and deployment. Our contribution in this paper is threefold. First, we show how services and service compositions may be modeled in UML by using a profile for SOA (UML4SOA) and how non-functional properties of service-oriented systems can be represented using the non-functional extension of UML4SOA (UML4SOA-NFP) and the MARTE profile. This enables modeling of performance, security and reliable messaging. Second, we discuss formal analysis of models which respect this design, in particular we consider performance estimates and reliability analysis using the stochastically timed process algebra PEPA as the underlying analytical engine. Last but not least, our models are the source for the application of deployment mechanisms which comprise model-to-model and model-to-text transformations implemented in the framework VIATRA. All techniques presented in this work are illustrated by a running example from an eUniversity case study

From software architecture to analysis models and back: Model-driven refactoring aimed at availability improvement

Abstract Context With the ever-increasing evolution of software systems, their architecture is subject to frequent changes due to multiple reasons, such as new requirements. Appropriate architectural changes driven by non-functional requirements are particularly challenging to identify because they concern quantitative analyses that are usually carried out with specific languages and tools. A considerable number of approaches have been proposed in the last decades to derive non-functional analysis models from architectural ones. However, there is an evident lack of automation in the backward path that brings the analysis results back to the software architecture. Objective In this paper, we propose a model-driven approach to support designers in improving the availability of their software systems through refactoring actions. Method The proposed framework makes use of bidirectional model transformations to map UML models onto Generalized Stochastic Petri Nets (GSPN) analysis models and vice versa. In particular, after availability analysis, our approach enables the application of model refactoring, possibly based on well-known fault tolerance patterns, aimed at improving the availability of the architectural model. Results We validated the effectiveness of our approach on an Environmental Control System. Our results show that the approach can generate: (i) an analyzable availability model from a software architecture description, and (ii) valid software architecture models back from availability models. Finally, our results highlight that the application of fault tolerance patterns significantly improves the availability in each considered scenario. Conclusion The approach integrates bidirectional model transformation and fault tolerance techniques to support the availability-driven refactoring of architectural models. The results of our experiment showed the effectiveness of the approach in improving the software availability of the system

A Semi-Automatic Approach for Eliciting Cloud Security and Privacy Requirements

Cloud computing provides a wide range of services to organisations in a flexible and cost efficient manner. Nevertheless, inherent cloud security issues make organisations hesitant towards the migration of their services to cloud. In parallel, the cloud service-oriented nature requires a specific and more demanding description of the business functional requirements intended for migration. Organisations need to transform their functional requirements based on a specific language, taking into account the respective non-functional requirements of the migrating services. Thus, the need for an approach that will holistically capture organisations\u27 security and privacy requirements and transform them to cloud service requirements is immense. To this end, this paper presents an approach that takes as input abstract security and privacy requirements and produces through a semi-automatic process various alternative implementation options for cloud services. To achieve that a series of model transformations are utilised in order to create a mapping between the organisational and the operational level of the system\u27s analysis

Unsupervised image registration towards enhancing performance and explainability in cardiac and brain image analysis

Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as “modalities”). As each modality is designed to offer different anatomical and functional clinical information, there are evident disparities in the imaging content across modalities. Inter- and intra-modality affine and non-rigid image registration is an essential medical image analysis process in clinical imaging, as for example before imaging biomarkers need to be derived and clinically evaluated across different MRI modalities, time phases and slices. Although commonly needed in real clinical scenarios, affine and non-rigid image registration is not extensively investigated using a single unsupervised model architecture. In our work, we present an unsupervised deep learning registration methodology that can accurately model affine and non-rigid transformations, simultaneously. Moreover, inverse-consistency is a fundamental inter-modality registration property that is not considered in deep learning registration algorithms. To address inverse consistency, our methodology performs bi-directional cross-modality image synthesis to learn modality-invariant latent representations, and involves two factorised transformation networks (one per each encoder-decoder channel) and an inverse-consistency loss to learn topology-preserving anatomical transformations. Overall, our model (named “FIRE”) shows improved performances against the reference standard baseline method (i.e., Symmetric Normalization implemented using the ANTs toolbox) on multi-modality brain 2D and 3D MRI and intra-modality cardiac 4D MRI data experiments. We focus on explaining model-data components to enhance model explainability in medical image registration. On computational time experiments, we show that the FIRE model performs on a memory-saving mode, as it can inherently learn topology-preserving image registration directly in the training phase. We therefore demonstrate an efficient and versatile registration technique that can have merit in multi-modal image registrations in the clinical setting

Physical unitarity in the Lagrangian Sp(2)-symmetric formalism

The structure of state vector space for a general (non-anomalous) gauge theory is studied within the Lagrangian version of the $Sp(2)$-symmetric quantization method. The physical {\it S}-matrix unitarity conditions are formulated. The general results are illustrated on the basis of simple gauge theory models.Comment: 26 pages, LaTE