Asynchronous data retrieval from an object-oriented database

We present an object-oriented semantic database model which, similar to other object-oriented systems, combines the virtues of four concepts: the functional data model, a property inheritance hierarchy, abstract data types and message-driven computation. The main emphasis is on the last of these four concepts. We describe generic procedures that permit queries to be processed in a purely message-driven manner. A database is represented as a network of nodes and directed arcs, in which each node is a logical processing element, capable of communicating with other nodes by exchanging messages. This eliminates the need for shared memory and for centralized control during query processing. Hence, the model is suitable for implementation on a multiprocessor computer architecture, consisting of large numbers of loosely coupled processing elements

Measuring and Predicting Importance of Objects in Our Visual World

Associating keywords with images automatically is an approachable and useful goal for visual recognition researchers. Keywords are distinctive and informative objects. We argue that keywords need to be sorted by 'importance', which we define as the probability of being mentioned first by an observer. We propose a method for measuring the `importance' of words using the object labels that multiple human observers give an everyday scene photograph. We model object naming as drawing balls from an urn, and fit this model to estimate `importance'; this combines order and frequency, enabling precise prediction under limited human labeling. We explore the relationship between the importance of an object in a particular image and the area, centrality, and saliency of the corresponding image patches. Furthermore, our data shows that many words are associated with even simple environments, and that few frequently appearing objects are shared across environments

Handling Parallelism in a Concurrency Model

Programming models for concurrency are optimized for dealing with nondeterminism, for example to handle asynchronously arriving events. To shield the developer from data race errors effectively, such models may prevent shared access to data altogether. However, this restriction also makes them unsuitable for applications that require data parallelism. We present a library-based approach for permitting parallel access to arrays while preserving the safety guarantees of the original model. When applied to SCOOP, an object-oriented concurrency model, the approach exhibits a negligible performance overhead compared to ordinary threaded implementations of two parallel benchmark programs.Comment: MUSEPAT 201

Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration

Are face and object recognition abilities independent? Although it is commonly believed that they are, Gauthier et al.(2014) recently showed that these abilities become more correlated as experience with nonface categories increases. They argued that there is a single underlying visual ability, v, that is expressed in performance with both face and nonface categories as experience grows. Using the Cambridge Face Memory Test and the Vanderbilt Expertise Test, they showed that the shared variance between Cambridge Face Memory Test and Vanderbilt Expertise Test performance increases monotonically as experience increases. Here, we address why a shared resource across different visual domains does not lead to competition and to an inverse correlation in abilities? We explain this conundrum using our neurocomputational model of face and object processing (The Model, TM). Our results show that, as in the behavioral data, the correlation between subordinate level face and object recognition accuracy increases as experience grows. We suggest that different domains do not compete for resources because the relevant features are shared between faces and objects. The essential power of experience is to generate a "spreading transform" for faces that generalizes to objects that must be individuated. Interestingly, when the task of the network is basic level categorization, no increase in the correlation between domains is observed. Hence, our model predicts that it is the type of experience that matters and that the source of the correlation is in the fusiform face area, rather than in cortical areas that subserve basic level categorization. This result is consistent with our previous modeling elucidating why the FFA is recruited for novel domains of expertise (Tong et al., 2008)

A Comparison of Two Paradigms for Distributed Shared Memory

This paper compares two paradigms for Distributed Shared Memory on loosely coupled computing systems: the shared data-object model as used in Orca, a programming language specially designed for loosely coupled computing systems and the Shared Virtual Memory model. For both paradigms two systems are described, one using only point-to-point messages, the other using broadcasting as well. The two paradigms and their implementations are described briefly. Their performances on four applications are compared: the travelling-salesman problem, alpha-beta search, matrix multiplication and the all-pairs shortest paths problem. The relevant measurements were obtained on a system consisting of 10 MC68020 processors connected by an Ethernet. For comparison purposes, the applications have also been run on a system with physical shared memory. In addition, the paper gives measurements for the first two applications above when Remote Procedure Call is used as the communication mechanism. The measurements show that both paradigms can be used efficiently for programming large-grain parallel applications, with significant speed-ups. The structured shared data-object model achieves the highest speed-ups and is easiest to program and to debug. KEYWORDS: Amoeba Distributed shared memory Distributed programming Orc

Unifying Internet Services Using Distributed Shared Objects

Developing wide area applications such as those for sharing data across the Internet is unnecessarily difficult. The main problem is the widespread use of a communication paradigm that is too low level. We will show how wide area application development can be made easier when using distributed shared objects instead of a communication-oriented model. An object in our model is physically distributed, with multiple copies of its state on different machines. All implementation aspects such as replication, distribution, and migration of state, are hidden from users through an object's interface. In this paper, we concentrate on the application of distributed shared objects, by providing an outline of a middleware solution that permits integration of the Internet services for e-mail, News, file transfer, and Web documents. vrije Universiteit Faculty of Mathematics and Computer Science 1 Introduction Constructing wide area applications, such as those for sharing data across the Internet,..

Supporting Shared Information Systems: Boundary Objects, Communities, and Brokering

Organizations increasingly rely upon integrated and shared information systems and databasessuch as ERP systems and data warehouses. Such shared systems pose new and unique support challenges for systems professionals. A review of the literature reveals that comprehensive models to study the support of shared information systems do not yet exist. Based on the theory of communities of practice, and on the concepts of convergence and divergence of systems and practice, the boundary object brokering model of shared information systems is developed. This model is applied to an interpretive case study of a large company, illustrating how shared systems can be seen as boundary objects that connect disparate communities of practice. The model and case study show how the traditional role of systems professionals has been augmented to include brokering tasks, providing new issues and implications for theory and practice