Context-sensitive dependency pairs

Termination is one of the most interesting problems when dealing with context-sensitive rewrite systems. Although a good number of techniques for proving termination of context-sensitive rewriting (CSR) have been proposed so far, the adaptation to CSR of the dependency pair approach, one of the most powerful techniques for proving termination of rewriting, took some time and was possible only after introducing some new notions like collapsing dependency pairs, which are specific for CSR. In this paper, we develop the notion of context-sensitive dependency pair (CSDP) and show how to use CSDPs in proofs of termination of CSR. The implementation and practical use of the developed techniques yield a novel and powerful framework which improves the current state-of-the-art of methods for automatically proving termination of CSR.     

A geometric view of conjugate priors

In Bayesian machine learning, conjugate priors are popular, mostly due to mathematical convenience. In this paper, we show that there are deeper reasons for choosing a conjugate prior. Specifically, we formulate the conjugate prior in the form of Bregman divergence and show that it is the inherent geometry of conjugate priors that makes them appropriate and intuitive. This geometric interpretation allows one to view the hyperparameters of conjugate priors as the effective sample points, thus providing additional intuition. We use this geometric understanding of conjugate priors to derive the hyperparameters and expression of the prior used to couple the generative and discriminative components of a hybrid model for semi-supervised learning.     

Designing and evaluating an energy efficient Cloud

Cloud infrastructures have recently become a center of attention. They can support dynamic operational infrastructures adapted to the requirements of distributed applications. As large-scale distributed systems reach enormous sizes in terms of equipment, the energy consumption issue becomes one of the main challenges for large-scale integration. Like any other large-scale distributed system, Clouds face an increasing demand in energy. In this paper, we explore the energy issue by analyzing how much energy virtualized environments cost. We provide an energy-efficient framework dedicated to Cloud architectures and we validate it through different experimentations on a modern multicore platform. We show on a realistic example that our infrastructure could save 25% of the Cloud nodes’ electrical consumption.     

New techniques for simulating high performance MPI applications on large storage networks

In this work, we propose new techniques to analyze the behavior, the performance, and specially the scalability of High Performance Computing (in short, HPC) applications on different computing architectures. Our final objective is to test applications using a wide range of architectures (real or merely designed) and scaling it to any number of nodes or components. This paper presents a new simulation framework, called SIMCAN, for HPC architectures. The main characteristic of the proposed simulation framework is the ability to be configured for simulating a wide range of possible architectures that involve any number of components. SIMCAN is developed to simulate complete HPC architectures, but putting special emphasis on the storage and network subsystems. The SIMCAN framework can handle complete components (nodes, racks, switches, routers, etc.), but also key elements of the storage and network subsystems (disks, caches, sockets, file systems, schedulers, etc.). We also propose several methods to implement the behavior of HPC applications. Each method has its own advantages and drawbacks. In order to evaluate the possibilities and the accuracy of the SIMCAN framework, we have tested it by executing a HPC application called BIPS3D on a hardware-based computing cluster and on a modeled environment that represent the real cluster. We also checked the scalability of the application using this kind of architecture by simulating the same application with an increased number of computing nodes.     

On the Length and Area Regularization for Multiphase Level Set Segmentation

In this paper we introduce novel regularization techniques for level set segmentation that target specifically the problem of multiphase segmentation. When the multiphase model is used to obtain a partitioning of the image in more than two regions, a new set of issues arise with respect to the single phase case in terms of regularization strategies. For example, if smoothing or shrinking each contour individually could be a good model in the single phase case, this is not necessarily true in the multiphase scenario.     

Guaranteed cost control analysis and iterative design for constrained Takagi–Sugeno systems

This paper presents a novel control design technique in order to obtain a guaranteed cost fuzzy controller subject to constraints on the input channel. This guaranteed cost control law is obtained via multi-parametric quadratic programming. The result is a piecewise fuzzy control law where the state partition is defined by fuzzy inequalities. The parameters of the Lyapunov function can be obtained previously using Linear Matrix Inequalities optimization.     

A genetic solution based on lexicographical goal programming for a multiobjective job shop with uncertainty

In this work we consider a multiobjective job shop problem with uncertain durations and crisp due dates. Ill-known durations are modelled as fuzzy numbers. We take a fuzzy goal programming approach to propose a generic multiobjective model based on lexicographical minimisation of expected values. To solve the resulting problem, we propose a genetic algorithm searching in the space of possibly active schedules. Experimental results are presented for several problem instances, solved by the GA according to the proposed model, considering three objectives: makespan, tardiness and idleness. The results illustrate the potential of the proposed multiobjective model and genetic algorithm.     

A technique for defining agent-oriented engineering processes with tool support

The formalization of engineering processes is necessary for guaranteeing the quality and cost of the products involved. Agent-oriented engineering has already proposed development processes that still need to be further formalized to be applicable by non-researchers. This paper proposes a technique to instantiate processes for specific agent-oriented methodologies. This technique is based on three orthogonal views that are respectively related with lifecycles, disciplines and guidances. In addition, processes are modeled with a tool, which is automatically generated from a process metamodel inspired by the software & systems process engineering metamodel. Accordingly, engineers can choose the methodology–process pair best-suited for the characteristics of their project. The paper illustrates the approach based on the unified development process and the scrum process for the INGENIAS methodology and compares the results with other existing alternatives.     

Analysing IOIS adoption through structural contradictions

Despite extensive research and practitioner literature on inter-organizational information systems (IOIS), our understanding of non-adoption still has some under-explored issues. This paper offers an explanation of non-adoption that focuses on the potential structural changes that IOIS adoption entails; in particular, we contend that the contradictions between the initial social structure and the structure enacted in the use of an IOIS can be a cause of non-adoption. This paper analyses adoption from a logic of opposition where non-adoption occurs because forces of transformation are offset by forces of persistence. Relying on the concepts of social structure, duality of structure, and structural contradiction from Structuration theory, we develop a framework that categorizes the underlying social structure where an IOIS is to be adopted, and formulate some theoretical propositions. From a practitioner perspective, this paper contends that the fact that IOIS management uncovers the contradictions in the social structure that result from IOIS adoption can help avoid dead ends. Accordingly, this framework, which can be used as a coarse-grained picture for anticipating adoption problems, can inform managers when devising an IOIS implementation strategy.     

Modeling of the intrinsic stress effect on the resonant frequency of NEMS resonators integrated by beams with variable cross-section

Nano-electro-mechanical systems (NEMS) resonators integrated by a double clamped beam with variable cross-section are used in several applications such as chemical and biological detectors, high-frequency filters, and signal processing. The structure of these resonators can experience intrinsic stresses produced during their fabrication process. We present an analytical model to estimate the first bending resonant frequency of NEMS resonators based on a double clamped beam with three cross-sections, which considers the intrinsic stress effect on the resonant structure. This model is obtained using the Rayleigh and Macaulay methods, as well as the Euler–Bernoulli beam theory. We applied the analytical model to a silicon carbide (SiC) resonator of 186 nm thickness reported in the literature. This resonator has a total length ranking from 80 to 258 μm and is subjected to a tensile intrinsic stress close to 110 MPa. Results from this model show good agreement with experimental results. The analytic frequencies have a maximum relative difference less than 6.3% respect to the measured frequencies. The tensile intrinsic stress on the resonant structure causes a significantly increase on its bending resonant frequency. The proposed model provides an insight into the study of the intrinsic stress influence on the resonant frequency of this nanostructure. In addition, this model can estimate the frequency shift due to the variations of the resonator geometrical parameters.