Presupposition failure — A comedy of errors

Presuppositions of utterances are the pieces of information you convey with an utterance no matter whether your utterance is true or not. We first study presupposition in a very simple framework of updating propositional information, with examples of how presuppositions of complex propositional updates can be calculated. Next we move on to presuppositions and quantification, in the context of a dynamic version of predicate logic, suitably modified to allow for presupposition failure. In both the propositional and the quantificational case, presupposition failure can be viewed as error abortion of procedures. Thus, a dynamic assertion logic which describes the preconditions for error abortion is the suitable tool for analysing presupposition.     

Error in the UNITY substitution rule for subscripted operators

UNITY, introduced by Chandy and Misra [ChM88], is a programming logic intended to reason about temporal properties of distributed programs. Despite the fact that UNITY does not have the full power of, for example, linear temporal logic, it enjoys popularity due to its simplicity.There was however a serious problem with the Substitution Rule. The logic is incomplete without the rule, and with the rule it is inconsistent.     

Prolog/Rex-a way to extend Prolog for better knowledgerepresentation

Prolog/Rex represents a powerful amalgamation of the latest techniques for knowledge representation and processing, rich in semantic features that ease the difficult task of encoding heterogeneous knowledge of real-world applications. The Prolog/Rex concept mechanism lets a user represent domain entities in terms of their structural and behavioral properties, including multiple inheritance, arbitrary user-defined relations among entities, annotated values (demons), incomplete knowledge, etc. A flexible rule language helps the knowledge engineer capture human expertise and provide flexible control of the reasoning process. Additional Prolog/Rex strength that cannot be found in any other hybrid language made on top of Prolog is language level support for keeping many potentially contradictory solutions to a problem, allowing possible solutions and their implications to be automatically generated and completely explored before they are committed. The same mechanism is used to model time-states, which are useful in planning and scheduling applications of Prolog/Rex     

Processor membership in asynchronous distributed systems

Presents protocols for determining processor membership in asynchronous distributed systems that are subject to processor and communication faults. These protocols depend on the placement of a total order on broadcast messages. The types of systems for which each of these protocols is applicable are characterized by the properties of the communication mechanisms and by the availability of stable storage. In the absence of stable storage or of a mechanism for distinguishing promptly delivered messages, the authors show that no membership protocol can exist. They also discuss their experience in implementing these membership protocols     

The classification, fusion, and parallelization of array languageprimitives

We present a classification scheme for array language primitives that quantifies the variation in parallelism and data locality that results from the fusion of any two primitives. We also present an algorithm based on this scheme that efficiently determines when it is beneficial to fuse any two primitives. Experimental results show that five LINPACK routines report 50% performance improvement from the fusion of array operators     

Unicast-based multicast communication in wormhole-routed networks

Multicast communication, in which the same message is delivered from a source node to an arbitrary number of destination nodes, is being increasingly demanded in parallel computing. System supported multicast services can potentially offer improved performance, increased functionality, and simplified programming, and may in turn be used to support various higher-level operations for data movement and global process control. This paper presents efficient algorithms to implement multicast communication in wormhole-routed direct networks, in the absence of hardware multicast support, by exploiting the properties of the switching technology. Minimum-time multicast algorithms are presented for n-dimensional meshes and hypercubes that use deterministic, dimension-ordered routing of unicast messages. Both algorithms can deliver a multicast message to m-1 destinations in [log ₂ m] message passing steps, while avoiding contention among the constituent unicast messages. Performance results of implementations on a 64-node nCUBE-2 hypercube and a 168-node Symult 2010 2-D mesh are given     

Weight smoothing to improve network generalization

A weight smoothing algorithm is proposed in this paper to improve a neural network's generalization capability. The algorithm can be used when the data patterns to be classified are presented on an n-dimensional grid (n>/=1) and there exists some correlations among neighboring data points within a pattern. For a fully-interconnected feedforward net, no such correlation information is embedded into the architecture. Consequently, the correlations can only be extracted through sufficient amount of network training. With the proposed algorithm, a smoothing constraint is incorporated into the objective function of backpropagation to reflect the neighborhood correlations and to seek those solutions that have smooth connection weights. Experiments were performed on problems of waveform classification, multifont alphanumeric character recognition, and handwritten numeral recognition. The results indicate that (1) networks trained with the algorithm do have smooth connection weights, and (2) they generalize better.     

Network information criterion-determining the number of hiddenunits for an artificial neural network model

The problem of model selection, or determination of the number of hidden units, can be approached statistically, by generalizing Akaike's information criterion (AIC) to be applicable to unfaithful (i.e., unrealizable) models with general loss criteria including regularization terms. The relation between the training error and the generalization error is studied in terms of the number of the training examples and the complexity of a network which reduces to the number of parameters in the ordinary statistical theory of AIC. This relation leads to a new network information criterion which is useful for selecting the optimal network model based on a given training set.