As the performance of electronic display systems continues to increase, the limitations of current signal coding methods become more and more apparent. With bit depth limitations set by industry standard interfaces, a more efficient coding system is desired to allow image quality to increase without requiring expansion of legacy infrastructure bandwidth. A good approach to this problem is to let the human visual system determine the quantization curve used to encode video signals. In this way optimal efficiency is maintained across the luminance range of interest, and the visibility of quantization artifacts is kept to a uniformly small level.

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| In an asynchronous direct-sequence codedivision multiple access (DS-CDMA) communication system the parameter estimation problem, i.e., estimating the propagation delay, attenuation and phase shift of each users' transmitted signal, may be complicated by the so-called near-far problem. The near-far problem occurs when the amplitudes of the users' received signals are very dissimilar, as the case might be in many important applications. In particular, the standard method for estimating the propagation delays will fail in a near-far situation. Several new estimators, the maximum likelihood, an approximative maximum likelihood and a subspace based estimator, are therefore proposed and are shown to be robust against the near-far problem. No knowledge of the transmitted bits is assumed and the proposed estimators can thus be used for both acquisition and tracking. In addition, the Cram er-Rao bound is derived for the parameter estimation problem.

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We present a natural language interface system which is based entirely on trained statistical models. The system consists of three stages of processing: parsing, semantic interpretation, and discourse.Each of these stages is modeled as a statistical process. The models are fully integrated, resulting in an end-to-end system that maps input utterances into meaning representation frames.

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This paper describes the resource-and system-building efforts of an eight-week Johns Hopkins University Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically-Informed Machine Translation (SIMT). We describe a new modality/negation (MN) annotation scheme, the creation of a (publicly available) MN lexicon, and two automated MN taggers that we built using the annotation scheme and lexicon. Our annotation scheme isolates three components of modality and negation: a trigger (a word that conveys modality or negation), a target (an action associated with modality or negation) and a holder (an experiencer of modality). We describe how our MN lexicon was semi-automatically produced and we demonstrate that a structure-based MN tagger results in precision around 86% (depending on genre) for tagging of a standard LDC data set.We apply our MN annotation scheme to statistical machine translation using a syntactic framework that supports the inclusion of semantic annotations. Syntactic tags enriched with semantic annotations are assigned to parse trees in the target-language training texts through a process of tree grafting. While the focus of our work is modality and negation, the tree grafting procedure is general and supports other types of semantic information. We exploit this capability by including named entities, produced by a pre-existing tagger, in addition to the MN elements * Fort Meade, MD, Computational LinguisticsVolume X, Number Y produced by the taggers described in this paper. The resulting system significantly outperformed a linguistically naïve baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English test set. This finding supports the hypothesis that both syntactic and semantic information can improve translation quality.