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Our new methods use the EIoEU approximation paired with high quality candidate solutions .
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Experimentally we show that our new approaches lead to improved performance on both image segmentation tasks .
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Later , however , Breiman cast serious doubt on this explanation by introducing a boosting algorithm , arc-gv , that can generate a higher margins distribution than AdaBoost and yet performs worse .
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Although we can reproduce his main finding , we find that the poorer performance of arc-gv can be explained by the increased complexity of the base classifiers it uses , an explanation supported by our experiments and entirely consistent with the margins theory .
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The transfer phase in machine translation -LRB- MT -RRB- systems has been considered to be more complicated than analysis and generation , since it is inherently a conglomeration of individual lexical rules .
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Currently some attempts are being made to use case-based reasoning in machine translation , that is , to make decisions on the basis of translation examples at appropriate pints in MT .
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This paper proposes a new type of transfer system , called a Similarity-driven Transfer System -LRB- SimTran -RRB- , for use in such case-based MT -LRB- CBMT -RRB- .
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This paper addresses the problem of optimal alignment of non-rigid surfaces from multi-view video observations to obtain a temporally consistent representation .
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Conventional non-rigid surface tracking performs frame-to-frame alignment which is subject to the accumulation of errors resulting in a drift over time .
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Recently , non-sequential tracking approaches have been introduced which reorder the input data based on a dissimilarity measure .
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They demonstrate a reduced drift and increased robustness to large non-rigid deformations .
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Optimisation of the tree for non-sequential tracking , which minimises the errors in temporal consistency due to both the drift and the jumps , is proposed .
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A novel cluster tree enforces sequential tracking in local segments of the sequence while allowing global non-sequential traversal among these segments .
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Comprehensive evaluation is performed on a variety of challenging non-rigid surfaces including face , cloth and people .
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It demonstrates that the proposed cluster tree achieves better temporal consistency than the previous sequential and non-sequential tracking approaches .
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Quantitative analysis on a created synthetic facial performance also shows an improvement by the cluster tree .
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The translation of English text into American Sign Language -LRB- ASL -RRB- animation tests the limits of traditional MT architectural designs .
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A new semantic representation is proposed that uses virtual reality 3D scene modeling software to produce spatially complex ASL phenomena called '' classifier predicates . ''
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The model acts as an interlingua within a new multi-pathway MT architecture design that also incorporates transfer and direct approaches into a single system .
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An extension to the GPSG grammatical formalism is proposed , allowing non-terminals to consist of finite sequences of category labels , and allowing schematic variables to range over such sequences .
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The extension is shown to be sufficient to provide a strongly adequate grammar for crossed serial dependencies , as found in e.g. Dutch subordinate clauses .
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The extension is shown to be parseable by a simple extension to an existing parsing method for GPSG .
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This paper presents an approach to localizing functional objects in surveillance videos without domain knowledge about semantic object classes that may appear in the scene .
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A Bayesian framework is used to probabilistically model : people 's trajectories and intents , constraint map of the scene , and locations of functional objects .
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A data-driven Markov Chain Monte Carlo -LRB- MCMC -RRB- process is used for inference .
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Our evaluation on videos of public squares and courtyards demonstrates our effectiveness in localizing functional objects and predicting people 's trajectories in unobserved parts of the video footage .
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We propose a process model for hierarchical perceptual sound organization , which recognizes perceptual sounds included in incoming sound signals .
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We consider perceptual sound organization as a scene analysis problem in the auditory domain .
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Our model consists of multiple processing modules and a hypothesis network for quantitative integration of multiple sources of information .
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On the hypothesis network , individual information is integrated and an optimal internal model of perceptual sounds is automatically constructed .
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Based on the model , a music scene analysis system has been developed for acoustic signals of ensemble music , which recognizes rhythm , chords , and source-separated musical notes .
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Experimental results show that our method has permitted autonomous , stable and effective information integration to construct the internal model of hierarchical perceptual sounds .
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We directly investigate a subject of much recent debate : do word sense disambigation models help statistical machine translation quality ?
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Using a state-of-the-art Chinese word sense disambiguation model to choose translation candidates for a typical IBM statistical MT system , we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone .
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Image sequence processing techniques are used to study exchange , growth , and transport processes and to tackle key questions in environmental physics and biology .
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These applications require high accuracy for the estimation of the motion field since the most interesting parameters of the dynamical processes studied are contained in first-order derivatives of the motion field or in dynamical changes of the moving objects .
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A tensor method tuned with carefully optimized derivative filters yields reliable and dense displacement vector fields -LRB- DVF -RRB- with an accuracy of up to a few hundredth pixels/frame for real-world images .
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The accuracy of the tensor method is verified with computer-generated sequences and a calibrated image sequence .
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With the improvements in accuracy the motion estimation is now rather limited by imperfections in the CCD sensors , especially the spatial nonuni-formity in the responsivity .
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The application of the techniques to the analysis of plant growth , to ocean surface microturbulence in IR image sequences , and to sediment transport is demonstrated .
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We present a Czech-English statistical machine translation system which performs tree-to-tree translation of dependency structures .
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The only bilingual resource required is a sentence-aligned parallel corpus .
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We also refer to an evaluation method and plan to compare our system 's output with a benchmark system .
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This paper describes the understanding process of the spatial descriptions in Japanese .
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To reconstruct the model , the authors extract the qualitative spatial constraints from the text , and represent them as the numerical constraints on the spatial attributes of the entities .
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Such context information is therefore important to characterize the intrinsic representation of a video frame .
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In this paper , we present a novel approach to learn the deep video representation by exploring both local and holistic contexts .
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Specifically , we propose a triplet sampling mechanism to encode the local temporal relationship of adjacent frames based on their deep representations .
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In addition , we incorporate the graph structure of the video , as a priori , to holistically preserve the inherent correlations among video frames .
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Our approach is fully unsupervised and trained in an end-to-end deep convolutional neu-ral network architecture .
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By extensive experiments , we show that our learned representation can significantly boost several video recognition tasks -LRB- retrieval , classification , and highlight detection -RRB- over traditional video representations .
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For mobile speech application , speaker DOA estimation accuracy , interference robustness and compact physical size are three key factors .
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It is achieved by deriving the inter-sensor data ratio model of an AVS in bispectrum domain -LRB- BISDR -RRB- and exploring the favorable properties of bispectrum , such as zero value of Gaussian process and different distribution of speech and NSI .
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Specifically , a reliable bispectrum mask is generated to guarantee that the speaker DOA cues , derived from BISDR , are robust to NSI in terms of speech sparsity and large bispectrum amplitude of the captured signals .
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Intensive experiments demonstrate an improved performance of our proposed algorithm under various NSI conditions even when SIR is smaller than 0dB .
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In this paper , we want to show how the morphological component of an existing NLP-system for Dutch -LRB- Dutch Medical Language Processor - DMLP -RRB- has been extended in order to produce output that is compatible with the language independent modules of the LSP-MLP system -LRB- Linguistic String Project - Medical La...
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The former can take advantage of the language independent developments of the latter , while focusing on idiosyncrasies for Dutch .
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This general strategy will be illustrated by a practical application , namely the highlighting of relevant information in a patient discharge summary -LRB- PDS -RRB- by means of modern HyperText Mark-Up Language -LRB- HTML -RRB- technology .
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Such an application can be of use for medical administrative purposes in a hospital environment .
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CriterionSM Online Essay Evaluation Service includes a capability that labels sentences in student writing with essay-based discourse elements -LRB- e.g. , thesis statements -RRB- .
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We describe a new system that enhances Criterion 's capability , by evaluating multiple aspects of coherence in essays .
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This system identifies features of sentences based on semantic similarity measures and discourse structure .
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A support vector machine uses these features to capture breakdowns in coherence due to relatedness to the essay question and relatedness between discourse elements .
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Intra-sentential quality is evaluated with rule-based heuristics .
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Results indicate that the system yields higher performance than a baseline on all three aspects .
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This paper presents an algorithm for labeling curvilinear structure at multiple scales in line drawings and edge images Symbolic CURVE-ELEMENT tokens residing in a spatially-indexed and scale-indexed data structure denote circular arcs fit to image data .
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