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3,000 | [[ Honorifics ]] are used extensively in << Japanese >> , reflecting the social relationship -LRB- e.g. social ranks and age -RRB- of the referents . | 3,000 | 3 |
3,001 | This [[ referential information ]] is vital for resolving << zero pronouns >> and improving machine translation outputs . | 3,001 | 3 |
3,002 | This [[ referential information ]] is vital for resolving zero pronouns and improving << machine translation outputs >> . | 3,002 | 3 |
3,003 | << Visually-guided arm reaching movements >> are produced by [[ distributed neural networks ]] within parietal and frontal regions of the cerebral cortex . | 3,003 | 3 |
3,004 | Experimental data indicate that -LRB- I -RRB- single neurons in these regions are broadly tuned to parameters of movement ; -LRB- 2 -RRB- appropriate commands are elaborated by populations of neurons ; -LRB- 3 -RRB- the << coordinated action of neu-rons >> can be visualized using a [[ neuronal population vector -LRB- NPV -RRB- ]] . | 3,004 | 3 |
3,005 | We designed a [[ model ]] of the << cortical motor command >> to investigate the relation between the desired direction of the movement , the actual direction of movement and the direction of the NPV in motor cortex . | 3,005 | 3 |
3,006 | We designed a model of the cortical motor command to investigate the relation between the desired direction of the movement , the actual direction of movement and the direction of the [[ NPV ]] in << motor cortex >> . | 3,006 | 3 |
3,007 | The model is a [[ two-layer self-organizing neural network ]] which combines broadly-tuned -LRB- muscular -RRB- proprioceptive and -LRB- cartesian -RRB- visual information to calculate << -LRB- angular -RRB- motor commands >> for the initial part of the movement of a two-link arm . | 3,007 | 3 |
3,008 | The model is a << two-layer self-organizing neural network >> which combines [[ broadly-tuned -LRB- muscular -RRB- proprioceptive ]] and -LRB- cartesian -RRB- visual information to calculate -LRB- angular -RRB- motor commands for the initial part of the movement of a two-link arm . | 3,008 | 3 |
3,009 | The model is a two-layer self-organizing neural network which combines [[ broadly-tuned -LRB- muscular -RRB- proprioceptive ]] and << -LRB- cartesian -RRB- visual information >> to calculate -LRB- angular -RRB- motor commands for the initial part of the movement of a two-link arm . | 3,009 | 0 |
3,010 | The model is a << two-layer self-organizing neural network >> which combines broadly-tuned -LRB- muscular -RRB- proprioceptive and [[ -LRB- cartesian -RRB- visual information ]] to calculate -LRB- angular -RRB- motor commands for the initial part of the movement of a two-link arm . | 3,010 | 3 |
3,011 | These results suggest the NPV does not give a faithful << image of cortical processing >> during [[ arm reaching movements ]] . | 3,011 | 1 |
3,012 | It is well-known that diversity among [[ base classifiers ]] is crucial for constructing a strong << ensemble >> . | 3,012 | 3 |
3,013 | In this paper , we propose an alternative way for << ensemble construction >> by [[ resampling pairwise constraints ]] that specify whether a pair of instances belongs to the same class or not . | 3,013 | 3 |
3,014 | Using [[ pairwise constraints ]] for << ensemble construction >> is challenging because it remains unknown how to influence the base classifiers with the sampled pairwise constraints . | 3,014 | 3 |
3,015 | First , we transform the original instances into a new << data representation >> using [[ projections ]] learnt from pairwise constraints . | 3,015 | 3 |
3,016 | First , we transform the original instances into a new data representation using << projections >> learnt from [[ pairwise constraints ]] . | 3,016 | 3 |
3,017 | Then , we build the << base clas-sifiers >> with the new [[ data representation ]] . | 3,017 | 3 |
3,018 | We propose two methods for << resampling pairwise constraints >> following the standard [[ Bagging and Boosting algorithms ]] , respectively . | 3,018 | 3 |
3,019 | A new [[ algorithm ]] for solving the three << dimensional container packing problem >> is proposed in this paper . | 3,019 | 3 |
3,020 | This new [[ algorithm ]] deviates from the traditional << approach of wall building and layering >> . | 3,020 | 5 |
3,021 | We tested our << method >> using all 760 test cases from the [[ OR-Library ]] . | 3,021 | 6 |
3,022 | Experimental results indicate that the new << algorithm >> is able to achieve an [[ average packing utilization ]] of more than 87 % . | 3,022 | 6 |
3,023 | Current [[ approaches ]] to << object category recognition >> require datasets of training images to be manually prepared , with varying degrees of supervision . | 3,023 | 3 |
3,024 | Current << approaches >> to object category recognition require [[ datasets ]] of training images to be manually prepared , with varying degrees of supervision . | 3,024 | 3 |
3,025 | We present an [[ approach ]] that can learn an << object category >> from just its name , by utilizing the raw output of image search engines available on the Internet . | 3,025 | 3 |
3,026 | We develop a new model , << TSI-pLSA >> , which extends [[ pLSA ]] -LRB- as applied to visual words -RRB- to include spatial information in a translation and scale invariant manner . | 3,026 | 3 |
3,027 | We develop a new model , TSI-pLSA , which extends [[ pLSA ]] -LRB- as applied to << visual words >> -RRB- to include spatial information in a translation and scale invariant manner . | 3,027 | 3 |
3,028 | We develop a new model , << TSI-pLSA >> , which extends pLSA -LRB- as applied to visual words -RRB- to include [[ spatial information ]] in a translation and scale invariant manner . | 3,028 | 4 |
3,029 | Our [[ approach ]] can handle the high << intra-class variability >> and large proportion of unrelated images returned by search engines . | 3,029 | 3 |
3,030 | Our [[ approach ]] can handle the high intra-class variability and large proportion of << unrelated images >> returned by search engines . | 3,030 | 3 |
3,031 | Our approach can handle the high [[ intra-class variability ]] and large proportion of << unrelated images >> returned by search engines . | 3,031 | 0 |
3,032 | Our approach can handle the high intra-class variability and large proportion of << unrelated images >> returned by [[ search engines ]] . | 3,032 | 3 |
3,033 | We evaluate the << models >> on standard [[ test sets ]] , showing performance competitive with existing methods trained on hand prepared datasets . | 3,033 | 6 |
3,034 | We evaluate the models on standard [[ test sets ]] , showing performance competitive with existing << methods >> trained on hand prepared datasets . | 3,034 | 6 |
3,035 | We evaluate the << models >> on standard test sets , showing performance competitive with existing [[ methods ]] trained on hand prepared datasets . | 3,035 | 5 |
3,036 | We evaluate the models on standard test sets , showing performance competitive with existing << methods >> trained on [[ hand prepared datasets ]] . | 3,036 | 3 |
3,037 | The paper provides an overview of the research conducted at LIMSI in the field of [[ speech processing ]] , but also in the related areas of << Human-Machine Communication >> , including Natural Language Processing , Non Verbal and Multimodal Communication . | 3,037 | 0 |
3,038 | The paper provides an overview of the research conducted at LIMSI in the field of speech processing , but also in the related areas of << Human-Machine Communication >> , including [[ Natural Language Processing ]] , Non Verbal and Multimodal Communication . | 3,038 | 2 |
3,039 | The paper provides an overview of the research conducted at LIMSI in the field of speech processing , but also in the related areas of Human-Machine Communication , including [[ Natural Language Processing ]] , << Non Verbal and Multimodal Communication >> . | 3,039 | 0 |
3,040 | The paper provides an overview of the research conducted at LIMSI in the field of speech processing , but also in the related areas of << Human-Machine Communication >> , including Natural Language Processing , [[ Non Verbal and Multimodal Communication ]] . | 3,040 | 2 |
3,041 | We have calculated << analytical expressions >> for how the bias and variance of the estimators provided by various temporal difference value estimation algorithms change with offline updates over trials in absorbing Markov chains using [[ lookup table representations ]] . | 3,041 | 3 |
3,042 | In this paper , we describe the [[ pronominal anaphora resolution module ]] of << Lucy >> , a portable English understanding system . | 3,042 | 4 |
3,043 | In this paper , we describe the pronominal anaphora resolution module of [[ Lucy ]] , a portable << English understanding system >> . | 3,043 | 2 |
3,044 | In this paper , we reported experiments of << unsupervised automatic acquisition of Italian and English verb subcategorization frames -LRB- SCFs -RRB- >> from [[ general and domain corpora ]] . | 3,044 | 3 |
3,045 | The proposed << technique >> operates on [[ syntactically shallow-parsed corpora ]] on the basis of a limited number of search heuristics not relying on any previous lexico-syntactic knowledge about SCFs . | 3,045 | 3 |
3,046 | The proposed << technique >> operates on syntactically shallow-parsed corpora on the basis of a limited number of [[ search heuristics ]] not relying on any previous lexico-syntactic knowledge about SCFs . | 3,046 | 3 |
3,047 | The proposed technique operates on syntactically shallow-parsed corpora on the basis of a limited number of search heuristics not relying on any previous << lexico-syntactic knowledge >> about [[ SCFs ]] . | 3,047 | 1 |
3,048 | [[ Graph-cuts optimization ]] is prevalent in << vision and graphics problems >> . | 3,048 | 3 |
3,049 | It is thus of great practical importance to parallelize the << graph-cuts optimization >> using to-day 's ubiquitous [[ multi-core machines ]] . | 3,049 | 3 |
3,050 | However , the current best << serial algorithm >> by Boykov and Kolmogorov -LSB- 4 -RSB- -LRB- called the [[ BK algorithm ]] -RRB- still has the superior empirical performance . | 3,050 | 2 |
3,051 | In this paper , we propose a novel [[ adaptive bottom-up approach ]] to parallelize the << BK algorithm >> . | 3,051 | 3 |
3,052 | Extensive experiments in common [[ applications ]] such as 2D/3D image segmentations and 3D surface fitting demonstrate the effectiveness of our << approach >> . | 3,052 | 6 |
3,053 | Extensive experiments in common << applications >> such as [[ 2D/3D image segmentations ]] and 3D surface fitting demonstrate the effectiveness of our approach . | 3,053 | 2 |
3,054 | Extensive experiments in common applications such as [[ 2D/3D image segmentations ]] and << 3D surface fitting >> demonstrate the effectiveness of our approach . | 3,054 | 0 |
3,055 | Extensive experiments in common << applications >> such as 2D/3D image segmentations and [[ 3D surface fitting ]] demonstrate the effectiveness of our approach . | 3,055 | 2 |
3,056 | We study the question of how to make loss-aware predictions in image segmentation settings where the << evaluation function >> is the [[ Intersection-over-Union -LRB- IoU -RRB- measure ]] that is used widely in evaluating image segmentation systems . | 3,056 | 2 |
3,057 | We study the question of how to make loss-aware predictions in image segmentation settings where the evaluation function is the [[ Intersection-over-Union -LRB- IoU -RRB- measure ]] that is used widely in evaluating << image segmentation systems >> . | 3,057 | 6 |
3,058 | Currently , there are two << dominant approaches >> : the [[ first ]] approximates the Expected-IoU -LRB- EIoU -RRB- score as Expected-Intersection-over-Expected-Union -LRB- EIoEU -RRB- ; and the second approach is to compute exact EIoU but only over a small set of high-quality candidate solutions . | 3,058 | 2 |
3,059 | Currently , there are two << dominant approaches >> : the first approximates the Expected-IoU -LRB- EIoU -RRB- score as Expected-Intersection-over-Expected-Union -LRB- EIoEU -RRB- ; and the [[ second approach ]] is to compute exact EIoU but only over a small set of high-quality candidate solutions . | 3,059 | 2 |
3,060 | Our new << methods >> use the [[ EIoEU approximation ]] paired with high quality candidate solutions . | 3,060 | 3 |
3,061 | Experimentally we show that our new << approaches >> lead to improved performance on both [[ image segmentation tasks ]] . | 3,061 | 6 |
3,062 | 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 . | 3,062 | 2 |
3,063 | 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 . | 3,063 | 3 |
3,064 | 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 . | 3,064 | 5 |
3,065 | 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 . | 3,065 | 6 |
3,066 | 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 . | 3,066 | 2 |
3,067 | 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 . | 3,067 | 4 |
3,068 | 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 . | 3,068 | 5 |
3,069 | 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 . | 3,069 | 5 |
3,070 | 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 . | 3,070 | 0 |
3,071 | 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 . | 3,071 | 3 |
3,072 | 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- . | 3,072 | 2 |
3,073 | 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- >> . | 3,073 | 3 |
3,074 | This paper addresses the problem of [[ optimal alignment of non-rigid surfaces ]] from multi-view video observations to obtain a << temporally consistent representation >> . | 3,074 | 3 |
3,075 | This paper addresses the problem of << optimal alignment of non-rigid surfaces >> from [[ multi-view video observations ]] to obtain a temporally consistent representation . | 3,075 | 3 |
3,076 | Conventional << non-rigid surface tracking >> performs [[ frame-to-frame alignment ]] which is subject to the accumulation of errors resulting in a drift over time . | 3,076 | 3 |
3,077 | Recently , << non-sequential tracking approaches >> have been introduced which reorder the input data based on a [[ dissimilarity measure ]] . | 3,077 | 3 |
3,078 | They demonstrate a reduced drift and increased [[ robustness ]] to large << non-rigid deformations >> . | 3,078 | 1 |
3,079 | [[ 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 . | 3,079 | 3 |
3,080 | << 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 . | 3,080 | 6 |
3,081 | A novel [[ cluster tree ]] enforces << sequential tracking in local segments >> of the sequence while allowing global non-sequential traversal among these segments . | 3,081 | 3 |
3,082 | A novel [[ cluster tree ]] enforces sequential tracking in local segments of the sequence while allowing << global non-sequential traversal >> among these segments . | 3,082 | 3 |
3,083 | Comprehensive evaluation is performed on a variety of challenging << non-rigid surfaces >> including [[ face ]] , cloth and people . | 3,083 | 2 |
3,084 | Comprehensive evaluation is performed on a variety of challenging non-rigid surfaces including [[ face ]] , << cloth >> and people . | 3,084 | 0 |
3,085 | Comprehensive evaluation is performed on a variety of challenging << non-rigid surfaces >> including face , [[ cloth ]] and people . | 3,085 | 2 |
3,086 | Comprehensive evaluation is performed on a variety of challenging non-rigid surfaces including face , [[ cloth ]] and << people >> . | 3,086 | 0 |
3,087 | Comprehensive evaluation is performed on a variety of challenging << non-rigid surfaces >> including face , cloth and [[ people ]] . | 3,087 | 2 |
3,088 | It demonstrates that the proposed [[ cluster tree ]] achieves better temporal consistency than the previous << sequential and non-sequential tracking approaches >> . | 3,088 | 5 |
3,089 | It demonstrates that the proposed << cluster tree >> achieves better [[ temporal consistency ]] than the previous sequential and non-sequential tracking approaches . | 3,089 | 6 |
3,090 | Quantitative analysis on a created [[ synthetic facial performance ]] also shows an improvement by the << cluster tree >> . | 3,090 | 6 |
3,091 | The << translation of English text into American Sign Language -LRB- ASL -RRB- animation >> tests the limits of traditional [[ MT architectural designs ]] . | 3,091 | 3 |
3,092 | A new [[ semantic representation ]] is proposed that uses virtual reality 3D scene modeling software to produce << spatially complex ASL phenomena >> called '' classifier predicates . '' | 3,092 | 3 |
3,093 | A new << semantic representation >> is proposed that uses [[ virtual reality 3D scene modeling software ]] to produce spatially complex ASL phenomena called '' classifier predicates . '' | 3,093 | 3 |
3,094 | A new semantic representation is proposed that uses virtual reality 3D scene modeling software to produce << spatially complex ASL phenomena >> called '' [[ classifier predicates ]] . '' | 3,094 | 2 |
3,095 | 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 . | 3,095 | 0 |
3,096 | 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 >> . | 3,096 | 4 |
3,097 | 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 >> . | 3,097 | 4 |
3,098 | 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 . | 3,098 | 3 |
3,099 | 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 . | 3,099 | 3 |