Towards more flexible Architecture Description Languages for industrial applications

Architecture Description Languages (ADLs) have emerged in recent years as a tool for providing high-level descriptions of software systems in terms of their architectural elements and the relationships among them. Most of the current ADLs exhibit limitations which prevent their widespread use in industrial applications. In this paper, we discuss these limitations and introduce ALI, an ADL that has been developed to address such limitations. The ALI language provides a rich and flexible syntax for describing component interfaces, architectural patterns, and meta-information. Multiple graphical architectural views can then be derived from ALI's textual notation.

Flawed Premises and Unexpected Consequences: Support of Regional Languages in Europe:Support of Regional Languages in Europe

This article discusses the relationship between three language communities in Europe with variant levels of official recognition, namely Kashub, Sorb, and Silesian, and the institutions of their host states as regards their respective use, promotion, and revital-ization. Most language communities across the world campaign for recognition within a geographic/political region, or on the basis of a historic/group identity to ensure their language's use and status. The examples discussed here illustrate that language recognition and policies resulting therefrom and promoting official monolin-gualism strengthen the symbolic status of the language but contribute little to the functionality of language communities outside the area. As this article illustrates, in increasingly multilingual societies, language policies cut off its speakers from the political, economic, and social opportunities accessible through the medium of languages that lack official recognition locally. © 2014 Taylor & Francis Group, LLC.

Advances in Learning Bayesian Networks of Bounded Treewidth

This work presents novel algorithms for learning Bayesian networks of bounded treewidth. Both exact and approximate methods are developed. The exact method combines mixed integer linear programming formulations for structure learning and treewidth computation. The approximate method consists in sampling k-trees (maximal graphs of treewidth k), and subsequently selecting, exactly or approximately, the best structure whose moral graph is a subgraph of that k-tree. The approaches are empirically compared to each other and to state-of-the-art methods on a collection of public data sets with up to 100 variables.

Fluorescent PET (Photoinduced Electron Transfer) Sensors with Targeting/Anchoring Modules as Molecular Versions of Submarine Periscopes for Mapping Membrane-Bounded Protons

The two families of fluorescent PET (photoinduced electron transfer) sensors (1-9) show that the effective proton density near the surface of several micelle membranes changes over 2-3 orders of magnitude as the microlocation of the sensor (with respect to the membrane) is altered via hydrophobic tuning.

Representing Irishness in Belfast’s Gaeltacht Quarter

This paper examines a place-making project in post-conflict Belfast, analyzing efforts to transform an area which has often been used as a byword for militant Irish nationalism and social deprivation into an inclusive, vibrant tourist destination and cultural hub themed around the Irish language (called the "Gaeltacht Quarter‟). The antagonistic and territorial assumptions about place that characterize divided cities now co-exist with global trends towards the commodification of difference as recreation or spectacle, and longstanding struggles over the representation of contested identities are intertwined with the struggle to compete for international tourism and investment. The proliferation of officially themed quarters in many cities across the world reflects the enthusiasm with which planning authorities have embraced the vision of difference as a benign resource for the creation of tourist revenue. Yet, analysis of „quartering‟ processes reveals that such commodification does not neutralise or evade the political potency of naming, representing and delimiting cultural difference. Indeed, this paper argues that such projects offer a valuable insight into the inseparable roles of physical and representational space as both loci and catalysts of contestation in urban conflicts. Bringing together a wide range of public and private interest groups, projects redefining parts of Belfast as distinctive quarters have been explicitly linked with efforts to deterritorialize the city. The creation of bounded, themed spaces as an attempt to leave behind the ethno-sectarian geographical segregation that parts of Belfast still experience has its particular ironies, but is in many ways typical of contemporary trends in urban planning. The Gaeltacht Quarter exemplifies both the importance and the challenge of representation within cities where culturally distinguishing features have acted as markers of violent division, and where negotiations about how to successfully encompass difference necessarily address multiple local and international audiences simultaneously.

The Complexity of MAP Inference in Bayesian Networks Specified Through Logical Languages

We study the computational complexity of finding maximum a posteriori configurations in Bayesian networks whose probabilities are specified by logical formulas. This approach leads to a fine grained study in which local information such as context-sensitive independence and determinism can be considered. It also allows us to characterize more precisely the jump from tractability to NP-hardness and beyond, and to consider the complexity introduced by evidence alone.

Learning Bounded Tree-Width Bayesian Networks via Sampling

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods [12, 14] tackle the problem by using k-trees to learn the optimal Bayesian network with tree-width up to k. In this paper, we propose a sampling method to efficiently find representative k-trees by introducing an Informative score function to characterize the quality of a k-tree. The proposed algorithm can efficiently learn a Bayesian network with tree-width at most k. Experiment results indicate that our approach is comparable with exact methods, but is much more computationally efficient.

Learning Bayesian Networks with Bounded Tree-Width via Guided Search

Bounding the tree-width of a Bayesian network can reduce the chance of overfitting, and allows exact inference to be performed efficiently. Several existing algorithms tackle the problem of learning bounded tree-width Bayesian networks by learning from k-trees as super-structures, but they do not scale to large domains and/or large tree-width. We propose a guided search algorithm to find k-trees with maximum Informative scores, which is a measure of quality for the k-tree in yielding good Bayesian networks. The algorithm achieves close to optimal performance compared to exact solutions in small domains, and can discover better networks than existing approximate methods can in large domains. It also provides an optimal elimination order of variables that guarantees small complexity for later runs of exact inference. Comparisons with well-known approaches in terms of learning and inference accuracy illustrate its capabilities.