A Type-Safe Model of Adaptive Object Groups

Services are autonomous, self-describing, technology-neutral software units that can be described, published, discovered, and composed into software applications at runtime. Designing software services and composing services in order to form applications or composite services requires abstractions beyond those found in typical object-oriented programming languages. This paper explores service-oriented abstractions such as service adaptation, discovery, and querying in an object-oriented setting. We develop a formal model of adaptive object-oriented groups which offer services to their environment. These groups fit directly into the object-oriented paradigm in the sense that they can be dynamically created, they have an identity, and they can receive method calls. In contrast to objects, groups are not used for structuring code. A group exports its services through interfaces and relies on objects to implement these services. Objects may join or leave different groups. Groups may dynamically export new interfaces, they support service discovery, and they can be queried at runtime for the interfaces they support. We define an operational semantics and a static type system for this model of adaptive object groups, and show that well-typed programs do not cause method-not-understood errors at runtime.Comment: In Proceedings FOCLASA 2012, arXiv:1208.432

A Geometric Approach to the Problem of Unique Decomposition of Processes

This paper proposes a geometric solution to the problem of prime decomposability of concurrent processes first explored by R. Milner and F. Moller in [MM93]. Concurrent programs are given a geometric semantics using cubical areas, for which a unique factorization theorem is proved. An effective factorization method which is correct and complete with respect to the geometric semantics is derived from the factorization theorem. This algorithm is implemented in the static analyzer ALCOOL.Comment: 15 page

Leveraging OpenStack and Ceph for a Controlled-Access Data Cloud

While traditional HPC has and continues to satisfy most workflows, a new generation of researchers has emerged looking for sophisticated, scalable, on-demand, and self-service control of compute infrastructure in a cloud-like environment. Many also seek safe harbors to operate on or store sensitive and/or controlled-access data in a high capacity environment. To cater to these modern users, the Minnesota Supercomputing Institute designed and deployed Stratus, a locally-hosted cloud environment powered by the OpenStack platform, and backed by Ceph storage. The subscription-based service complements existing HPC systems by satisfying the following unmet needs of our users: a) on-demand availability of compute resources, b) long-running jobs (i.e., $> 30$ days), c) container-based computing with Docker, and d) adequate security controls to comply with controlled-access data requirements. This document provides an in-depth look at the design of Stratus with respect to security and compliance with the NIH's controlled-access data policy. Emphasis is placed on lessons learned while integrating OpenStack and Ceph features into a so-called "walled garden", and how those technologies influenced the security design. Many features of Stratus, including tiered secure storage with the introduction of a controlled-access data "cache", fault-tolerant live-migrations, and fully integrated two-factor authentication, depend on recent OpenStack and Ceph features.Comment: 7 pages, 5 figures, PEARC '18: Practice and Experience in Advanced Research Computing, July 22--26, 2018, Pittsburgh, PA, US

Photographic Dependence/Independence

None provided

Modeling visual attention on scenes

International audienceLa modélisation computationelle de l'attention visuelle connaît actuellement un essor considérable. Les premières modèles, purement basés sur l'attention dite exogène, permettent de calculer une carte de saillance indiquant les zones d'intérêt visuel d'une image. Cependant, afin d'améliorer cette prédiction, il s'avère nécessaire de prendre en compte des informations de plus haut niveaux relatives à l'attention endogène, c'est à dire des informations liées aux processus cognitifs. Afin de rendre compte de cette problématique, le présent article décrit un certain nombre de modèles exogènes ainsi que des modèles intégrant de la connaissance a priori. Les méthodes d'évaluation des performances sont également décrites. Afin d'aller plus loin dans la modélisation et dans la compréhension des processus cognitifs, de nouvelles perspectives et direction d'études sont exposées