Phrase linguistic classification and generalization for improving statistical machine translation

In this paper a method to incorporate linguistic information regarding single-word and compound verbs is proposed, as a first step towards an SMT model based on linguistically-classified phrases. By substituting these verb structures by the base form of the head verb, we achieve a better statistical word alignment performance, and are able to better estimate the translation model and generalize to unseen verb forms during translation. Preliminary experiments for the English - Spanish language pair are performed, and future research lines are detailed. © 2005 Association for Computational Linguistics.

Comparison of manual and user guided methodologies for the classification and retrieval of construction site images

The amount of original imaging information produced yearly during the last decade has experienced a tremendous growth in all industries due to the technological breakthroughs in digital imaging and electronic storage capabilities. This trend is affecting the construction industry as well, where digital cameras and image databases are gradually replacing traditional photography. Owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks like monitoring an activity's progress and keeping evidence of the "as built" in case any disputes arise. So far, retrieval methodologies are done manually with the user being responsible for imaging classification according to specific rules that serve a limited number of construction management tasks. New methods that, with the guidance of the user, can automatically classify and retrieve construction site images are being developed and promise to remove the heavy burden of manually indexing images. In this paper, both the existing methods and a novel image retrieval method developed by the authors for the classification and retrieval of construction site images are described and compared. Specifically a number of examples are deployed in order to present their advantages and limitations. The results from this comparison demonstrates that the content based image retrieval method developed by the authors can reduce the overall time spent for the classification and retrieval of construction images while providing the user with the flexibility to retrieve images according different classification schemes.

A Monte Carlo expectation maximisation algorithm for multiple target tracking

In this paper, we present an expectation-maximisation (EM) algorithm for maximum likelihood estimation in multiple target models (MTT) with Gaussian linear state-space dynamics. We show that estimation of sufficient statistics for EM in a single Gaussian linear state-space model can be extended to the MTT case along with a Monte Carlo approximation for inference of unknown associations of targets. The stochastic approximation EM algorithm that we present here can be used along with any Monte Carlo method which has been developed for tracking in MTT models, such as Markov chain Monte Carlo and sequential Monte Carlo methods. We demonstrate the performance of the algorithm with a simulation. © 2012 ISIF (Intl Society of Information Fusi).

A classification of data quality assessment methods

Data quality (DQ) assessment can be significantly enhanced with the use of the right DQ assessment methods, which provide automated solutions to assess DQ. The range of DQ assessment methods is very broad: from data profiling and semantic profiling to data matching and data validation. This paper gives an overview of current methods for DQ assessment and classifies the DQ assessment methods into an existing taxonomy of DQ problems. Specific examples of the placement of each DQ method in the taxonomy are provided and illustrate why the method is relevant to the particular taxonomy position. The gaps in the taxonomy, where no current DQ methods exist, show where new methods are required and can guide future research and DQ tool development.

A preliminary study of target multiplicity for ADSRs

The Accelerator Driven Subcritical Reactor (ADSR) concept is based on the coupling of a particle accelerator to a subcritical reactor core by means of a neutron spallation target interface. This paper investigates the benefits of multiple spallation targets in ADSRs. The motivation behind this is, firstly, to improve the overall reliability of the accelerator-reactor system, and, secondly, to evaluate other potential advantages such as lower beam power requirements. The results show that a system containing two or three spallation targets, coupled to independent accelerators, offers better neutronic performance. This is demonstrated through the increased effective multiplication factor (keff) in the two- and three-target configurations and a more uniform neutron flux distribution. A multiple-target ADSR also proves effective in mitigating the impact of frequent beam interruptions, a pressing issue that needs to be addressed for the ADSR concept to advance. Assuming no simultaneous beam shutdowns, the two- and three-target configurations reduce the risk of fuel cladding failure due to thermal cyclic fatigue. © 2013 Elsevier B.V. All rights reserved.

User target intention recognition from cursor position using kalman filter

This paper discusses user target intention recognition algorithms for pointing - clicking tasks to reduce users' pointing time and difficulty. Predicting targets by comparing the bearing angles to targets proposed as one of the first algorithms [1] is compared with a Kalman Filter prediction algorithm. Accuracy and sensitivity of prediction are used as performance criteria. The outcomes of a standard point and click experiment are used for performance comparison, collected from both able-bodied and impaired users. © 2013 Springer-Verlag Berlin Heidelberg.

Unsteady penetration of a target by a liquid jet.

It is widely acknowledged that ceramic armor experiences an unsteady penetration response: an impacting projectile may erode on the surface of a ceramic target without substantial penetration for a significant amount of time and then suddenly start to penetrate the target. Although known for more than four decades, this phenomenon, commonly referred to as dwell, remains largely unexplained. Here, we use scaled analog experiments with a low-speed water jet and a soft, translucent target material to investigate dwell. The transient target response, in terms of depth of penetration and impact force, is captured using a high-speed camera in combination with a piezoelectric force sensor. We observe the phenomenon of dwell using a soft (noncracking) target material. The results show that the penetration rate increases when the flow of the impacting water jet is reversed due to the deformation of the jet-target interface--this reversal is also associated with an increase in the force exerted by the jet on the target. Creep penetration experiments with a constant indentation force did not show an increase in the penetration rate, confirming that flow reversal is the cause of the unsteady penetration rate. Our results suggest that dwell can occur in a ductile noncracking target due to flow reversal. This phenomenon of flow reversal is rather widespread and present in a wide range of impact situations, including water-jet cutting, needleless injection, and deposit removal via a fluid jet.

Calibrating the Gaussian multi-target tracking model

We present novel batch and online (sequential) versions of the expectation-maximisation (EM) algorithm for inferring the static parameters of a multiple target tracking (MTT) model. Online EM is of particular interest as it is a more practical method for long data sets since in batch EM, or a full Bayesian approach, a complete browse of the data is required between successive parameter updates. Online EM is also suited to MTT applications that demand real-time processing of the data. Performance is assessed in numerical examples using simulated data for various scenarios. For batch estimation our method significantly outperforms an existing gradient based maximum likelihood technique, which we show to be significantly biased. © 2014 Springer Science+Business Media New York.

Classification using log Gaussian Cox processes

McCullagh and Yang (2006) suggest a family of classification algorithms based on Cox processes. We further investigate the log Gaussian variant which has a number of appealing properties. Conditioned on the covariates, the distribution over labels is given by a type of conditional Markov random field. In the supervised case, computation of the predictive probability of a single test point scales linearly with the number of training points and the multiclass generalization is straightforward. We show new links between the supervised method and classical nonparametric methods. We give a detailed analysis of the pairwise graph representable Markov random field, which we use to extend the model to semi-supervised learning problems, and propose an inference method based on graph min-cuts. We give the first experimental analysis on supervised and semi-supervised datasets and show good empirical performance.