19177138
Reconstruction Thresholds on Regular Trees
We consider the model of broadcasting on a tree, with binary state space, on the infinite rooted tree T k in which each node has k children. The root of the tree takes a random value 0 or 1, and then each node passes a value independently to each of its children according to a 2x2 transition matrix P . We say that reconstruction is possible if the values at the d th level of the tree contain non-vanishing information about the value at the root as d→∞ . Extending a method of Brightwell and Winkler, we obtain new conditions under which reconstruction is impossible, both in the general case and in the special case p 11 =0 . The latter case is closely related to the hard-core model from statistical physics; a corollary of our results is that, for the hard-core model on the (k+1) -regular tree with activity λ=1 , the unique simple invariant Gibbs measure is extremal in the set of Gibbs measures, for any k ≥ 2 .
26598527
Large Tandem Queueing Networks with Blocking
Systems consisting of many queues in series have been considered by Glynn and Whitt (1991) and Baccelli, Borovkov and Mairesse (2000). We extend their results to apply to situations where the queues have finite capacity and so various types of “blocking” can occur. The models correspond to max-plus type recursions, of simple form but in infinitely many dimensions; they are related to “percolation” problems of finding paths of maximum weight through a 2-dimensional lattice with random weights at the vertices. Topics treated include: laws of large numbers for the speed of customers progressing through the system; stationary behaviour for systems with external arrival processes; a functional central limit theorem describing the behaviour of the “front of the wave” progressing through a system which starts empty; stochastic orderings for waiting times of customers at successive queues. Several open problems are noted.
44920541
Benefits of partial reconfigurability in circuit-switched WDM networks
We describe and study partially reconfigurable circuit-switched networks. These are dynamic networks in which partial, but not complete, flexibility of resource assignment is retained during the lifetime of a connection. In particular we analyse two examples of partially reconfigurable WDM networks with flexibility of wavelength assignment. In the first example, each connection in the network requires a main lightpath (whose wavelength must remain fixed) and a standby lightpath (whose wavelength may change). In the second example, the network contains two classes of connections with different service levels, one class reconfigurable and the other not. We propose two contrasting reconfiguration algorithms, and demonstrate that even a small amount of reconfigurability can yield significant performance gains, while a large amount of reconfigurability yields almost all the performance gain that would be attained by introducing complete wavelength conversion to the network.
618620
Corpus-based static branch prediction
Correctly predicting the direction that branches will take is increasingly important in today's wide-issue computer architectures. The name program-based branch prediction is given to static branch prediction techniques that base their prediction on a program's structure. In this paper, we investigate a new approach to program-based branch prediction that uses a body of existing programs to predict the branch behavior in a new program. We call this approach to program-based branch prediction, evidence-based static prediction, or ESP. The main idea of ESP is that the behavior of a corpus of programs can be used to infer the behavior of new programs. In this paper, we use a neural network to map static features associated with each branch to the probability that the branch will be taken. ESP shows significant advantages over other prediction mechanisms. Specifically, it is a program-based technique, it is effective across a range of programming languages and programming styles, and it does not rely on the use of expert-defined heuristics. 
In this paper, we describe the application of ESP to the problem of branch prediction and compare our results to existing program-based branch predictors. We also investigate the applicability of ESP across computer architectures, programming languages, compilers, and run-time systems. Averaging over a body of 43 C and Fortran programs, ESP branch prediction results in a miss rate of 20%, as compared with the 25% miss rate obtained using the best existing program-based heuristics.
15290012
Shallow Semantic Parsing using Support Vector Machines
In this paper, we propose a machine learning algorithm for shallow semantic parsing, extending the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our algorithm is based on Support Vector Machines which we show give an improvement in performance over earlier classifiers. We show performance improvements through a number of new features and measure their ability to generalize to a new test set drawn from the AQUAINT corpus.
8716869
Generating product-lines of product-families
GenVoca is a methodology and technology for generating product-lines, i.e. building variants of a program. The primitive components from which applications are constructed are refinements or layers, which are modules that implement a feature that many programs of a product-line can share. Unlike conventional components (e.g., COM, CORBA, EJB), a layer encapsulates fragments of multiple classes. Sets of fully formed classes can be produced by composing layers. Layers are modular, albeit unconventional, building blocks of programs. But what are the building blocks of layers? We argue that facets is an answer. A facet encapsulates fragments of multiple layers, and compositions of facets yields sets of fully formed layers. Facets arise when refinements scale from producing variants of individual programs to producing variants of multiple integrated programs, as typified byproduct families (e.g., MS Office). We present a mathematical model that explains relationships between layers and facets. We use the model to develop a generator for tools (i.e., product family) that are used in language-extensible Integrated Development Environments (IDEs).
15265
Experimental Evaluation of Bi-directional Multimodal Interaction with Conversational Agents
In the field of intuitive HCI, Embodied Conversational Agents (ECAs) are being developed mostly with speech input. In this paper, we study whether another input modality leads to a more effective and pleasant “bi-directional” multimodal communication. In a Wizard-of-Oz experiment, adults and children were videotaped while interacting with 2D animated agents within a game application. Each subject carried out a multimodal scenario (speech and/or pen input) and a speech-only scenario. The results confirm the usefulness of multimodal input, which yielded shorter scenarios, higher and more homogeneous ratings of easiness. Additional results underlined the importance of gesture interaction for children, and showed a modality specialization for certain actions. Finally, multidimensional analyses revealed links between behavioral and subjective data, such as an association of pen use and pleasantness for children. These results can be used for both developing the functional prototype and in the general framework of ECA-systems evaluation and specification.
12982947
Support Vector Learning for Semantic Argument Classification
The natural language processing community has recently experienced a growth of interest in domain independent shallow semantic parsing—the process of assigning a Who did What to Whom, When, Where, Why, How etc. structure to plain text. This process entails identifying groups of words in a sentence that represent these semantic arguments and assigning specific labels to them. It could play a key role in NLP tasks like Information Extraction, Question Answering and Summarization. We propose a machine learning algorithm for semantic role parsing, extending the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our algorithm is based on Support Vector Machines which we show give large improvement in performance over earlier classifiers. We show performance improvements through a number of new features designed to improve generalization to unseen data, such as automatic clustering of verbs. We also report on various analytic studies examining which features are most important, comparing our classifier to other machine learning algorithms in the literature, and testing its generalization to new test set from different genre. On the task of assigning semantic labels to the PropBank (Kingsbury, Palmer, & Marcus, 2002) corpus, our final system has a precision of 84% and a recall of 75%, which are the best results currently reported for this task. Finally, we explore a completely different architecture which does not requires a deep syntactic parse. We reformulate the task as a combined chunking and classification problem, thus allowing our algorithm to be applied to new languages or genres of text for which statistical syntactic parsers may not be available.