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The << compact description of a video sequence >> through a single image map and a [[ dominant motion ]] has applications in several domains , including video browsing and retrieval , compression , mosaicing , and visual summarization .

The compact description of a video sequence through a single image map and a dominant motion has applications in several << domains >> , including [[ video browsing and retrieval ]] , compression , mosaicing , and visual summarization .

The compact description of a video sequence through a single image map and a dominant motion has applications in several domains , including [[ video browsing and retrieval ]] , << compression >> , mosaicing , and visual summarization .

The compact description of a video sequence through a single image map and a dominant motion has applications in several << domains >> , including video browsing and retrieval , [[ compression ]] , mosaicing , and visual summarization .

The compact description of a video sequence through a single image map and a dominant motion has applications in several domains , including video browsing and retrieval , [[ compression ]] , << mosaicing >> , and visual summarization .

The compact description of a video sequence through a single image map and a dominant motion has applications in several << domains >> , including video browsing and retrieval , compression , [[ mosaicing ]] , and visual summarization .

The compact description of a video sequence through a single image map and a dominant motion has applications in several domains , including video browsing and retrieval , compression , [[ mosaicing ]] , and << visual summarization >> .

Building such a representation requires the capability to register all the frames with respect to the dominant object in the scene , a << task >> which has been , in the past , addressed through temporally [[ localized motion estimates ]] .

To avoid this oscillation , we augment the << motion model >> with a [[ generic temporal constraint ]] which increases the robustness against competing interpretations , leading to more meaningful content summarization .

To avoid this oscillation , we augment the motion model with a [[ generic temporal constraint ]] which increases the robustness against competing interpretations , leading to more meaningful << content summarization >> .

To avoid this oscillation , we augment the motion model with a << generic temporal constraint >> which increases the [[ robustness ]] against competing interpretations , leading to more meaningful content summarization .

In cross-domain learning , there is a more challenging problem that the << domain divergence >> involves more than one [[ dominant factors ]] , e.g. , different viewpoints , various resolutions and changing illuminations .

In cross-domain learning , there is a more challenging problem that the domain divergence involves more than one << dominant factors >> , e.g. , different [[ viewpoints ]] , various resolutions and changing illuminations .

In cross-domain learning , there is a more challenging problem that the domain divergence involves more than one dominant factors , e.g. , different [[ viewpoints ]] , various << resolutions >> and changing illuminations .

In cross-domain learning , there is a more challenging problem that the domain divergence involves more than one << dominant factors >> , e.g. , different viewpoints , various [[ resolutions ]] and changing illuminations .

In cross-domain learning , there is a more challenging problem that the domain divergence involves more than one dominant factors , e.g. , different viewpoints , various [[ resolutions ]] and changing << illuminations >> .

Fortunately , an [[ intermediate domain ]] could often be found to build a bridge across them to facilitate the << learning problem >> .

In this paper , we propose a [[ Coupled Marginalized Denoising Auto-encoders framework ]] to address the << cross-domain problem >> .

Specifically , we design two << marginalized denoising auto-encoders >> , [[ one ]] for the target and the other for source as well as the intermediate one .

Specifically , we design two marginalized denoising auto-encoders , [[ one ]] for the target and the << other >> for source as well as the intermediate one .

Specifically , we design two << marginalized denoising auto-encoders >> , one for the target and the [[ other ]] for source as well as the intermediate one .

To better couple the two << denoising auto-encoders learning >> , we incorporate a [[ feature mapping ]] , which tends to transfer knowledge between the intermediate domain and the target one .

To better couple the two denoising auto-encoders learning , we incorporate a [[ feature mapping ]] , which tends to transfer knowledge between the << intermediate domain >> and the target one .

Furthermore , the << maximum margin criterion >> , e.g. , [[ intra-class com-pactness ]] and inter-class penalty , on the output layer is imposed to seek more discriminative features across different domains .

Furthermore , the maximum margin criterion , e.g. , [[ intra-class com-pactness ]] and << inter-class penalty >> , on the output layer is imposed to seek more discriminative features across different domains .

Furthermore , the << maximum margin criterion >> , e.g. , intra-class com-pactness and [[ inter-class penalty ]] , on the output layer is imposed to seek more discriminative features across different domains .

Extensive experiments on two [[ tasks ]] have demonstrated the superiority of our << method >> over the state-of-the-art methods .

Extensive experiments on two tasks have demonstrated the superiority of our [[ method ]] over the << state-of-the-art methods >> .

Basically , a set of << age-group specific dictionaries >> are learned , where the [[ dictionary bases ]] corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups , and a linear combination of these patterns expresses a particular personalized aging process .

Basically , a set of age-group specific dictionaries are learned , where the dictionary bases corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups , and a [[ linear combination ]] of these patterns expresses a particular << personalized aging process >> .

Basically , a set of age-group specific dictionaries are learned , where the dictionary bases corresponding to the same index yet from different dictionaries form a particular aging process pattern cross different age groups , and a << linear combination >> of these [[ patterns ]] expresses a particular personalized aging process .

First , beyond the aging dictionaries , each subject may have extra << personalized facial characteristics >> , e.g. [[ mole ]] , which are invariant in the aging process .

Thus a [[ personality-aware coupled reconstruction loss ]] is utilized to learn the << dictionaries >> based on face pairs from neighboring age groups .

Extensive experiments well demonstrate the advantages of our proposed [[ solution ]] over other << state-of-the-arts >> in term of personalized aging progression , as well as the performance gain for cross-age face verification by synthesizing aging faces .

Extensive experiments well demonstrate the advantages of our proposed [[ solution ]] over other state-of-the-arts in term of << personalized aging progression >> , as well as the performance gain for cross-age face verification by synthesizing aging faces .

Extensive experiments well demonstrate the advantages of our proposed solution over other [[ state-of-the-arts ]] in term of << personalized aging progression >> , as well as the performance gain for cross-age face verification by synthesizing aging faces .

Extensive experiments well demonstrate the advantages of our proposed solution over other state-of-the-arts in term of personalized aging progression , as well as the performance gain for << cross-age face verification >> by [[ synthesizing aging faces ]] .

We propose a draft scheme of the [[ model ]] formalizing the << structure of communicative context >> in dialogue interaction .

We propose a draft scheme of the model formalizing the << structure of communicative context >> in [[ dialogue interaction ]] .

Visitors who browse the web from wireless PDAs , cell phones , and pagers are frequently stymied by [[ web interfaces ]] optimized for << desktop PCs >> .

In this paper we develop an [[ algorithm ]] , MINPATH , that automatically improves << wireless web navigation >> by suggesting useful shortcut links in real time .

In this paper we develop an [[ algorithm ]] , MINPATH , that automatically improves << wireless web navigation >> by suggesting useful shortcut links in real time .

<< MINPATH >> finds shortcuts by using a learned [[ model ]] of web visitor behavior to estimate the savings of shortcut links , and suggests only the few best links .

MINPATH finds shortcuts by using a learned [[ model ]] of << web visitor behavior >> to estimate the savings of shortcut links , and suggests only the few best links .

MINPATH finds shortcuts by using a learned [[ model ]] of web visitor behavior to estimate the << savings of shortcut links >> , and suggests only the few best links .

We explore a variety of << predictive models >> , including [[ Na ¨ ıve Bayes mixture models ]] and mixtures of Markov models , and report empirical evidence that MINPATH finds useful shortcuts that save substantial navigational effort .

We explore a variety of predictive models , including [[ Na ¨ ıve Bayes mixture models ]] and << mixtures of Markov models >> , and report empirical evidence that MINPATH finds useful shortcuts that save substantial navigational effort .

We explore a variety of << predictive models >> , including Na ¨ ıve Bayes mixture models and [[ mixtures of Markov models ]] , and report empirical evidence that MINPATH finds useful shortcuts that save substantial navigational effort .

This paper describes a particular [[ approach ]] to << parsing >> that utilizes recent advances in unification-based parsing and in classification-based knowledge representation .

This paper describes a particular << approach >> to parsing that utilizes recent advances in [[ unification-based parsing ]] and in classification-based knowledge representation .

This paper describes a particular << approach >> to parsing that utilizes recent advances in unification-based parsing and in [[ classification-based knowledge representation ]] .

This paper describes a particular approach to parsing that utilizes recent advances in << unification-based parsing >> and in [[ classification-based knowledge representation ]] .

As [[ unification-based grammatical frameworks ]] are extended to handle richer descriptions of << linguistic information >> , they begin to share many of the properties that have been developed in KL-ONE-like knowledge representation systems .

As unification-based grammatical frameworks are extended to handle richer descriptions of linguistic information , << they >> begin to share many of the properties that have been developed in [[ KL-ONE-like knowledge representation systems ]] .

This commonality suggests that some of the [[ classification-based representation techniques ]] can be applied to << unification-based linguistic descriptions >> .

This merging supports the integration of [[ semantic and syntactic information ]] into the same << system >> , simultaneously subject to the same types of processes , in an efficient manner .

The use of a [[ KL-ONE style representation ]] for << parsing >> and semantic interpretation was first explored in the PSI-KLONE system -LSB- 2 -RSB- , in which parsing is characterized as an inference process called incremental description refinement .

The use of a [[ KL-ONE style representation ]] for parsing and << semantic interpretation >> was first explored in the PSI-KLONE system -LSB- 2 -RSB- , in which parsing is characterized as an inference process called incremental description refinement .

The use of a KL-ONE style representation for [[ parsing ]] and << semantic interpretation >> was first explored in the PSI-KLONE system -LSB- 2 -RSB- , in which parsing is characterized as an inference process called incremental description refinement .

The use of a << KL-ONE style representation >> for parsing and semantic interpretation was first explored in the [[ PSI-KLONE system ]] -LSB- 2 -RSB- , in which parsing is characterized as an inference process called incremental description refinement .

The use of a KL-ONE style representation for parsing and semantic interpretation was first explored in the PSI-KLONE system -LSB- 2 -RSB- , in which << parsing >> is characterized as an inference process called [[ incremental description refinement ]] .

The use of a KL-ONE style representation for parsing and semantic interpretation was first explored in the PSI-KLONE system -LSB- 2 -RSB- , in which parsing is characterized as an << inference process >> called [[ incremental description refinement ]] .

In this paper we discuss a proposed [[ user knowledge modeling architecture ]] for the << ICICLE system >> , a language tutoring application for deaf learners of written English .

In this paper we discuss a proposed user knowledge modeling architecture for the [[ ICICLE system ]] , a << language tutoring application >> for deaf learners of written English .

In this paper we discuss a proposed user knowledge modeling architecture for the ICICLE system , a [[ language tutoring application ]] for << deaf learners >> of written English .

In this paper we discuss a proposed user knowledge modeling architecture for the ICICLE system , a << language tutoring application >> for deaf learners of [[ written English ]] .

The [[ model ]] will represent the language proficiency of the user and is designed to be referenced during both << writing analysis >> and feedback production .

The [[ model ]] will represent the language proficiency of the user and is designed to be referenced during both writing analysis and << feedback production >> .

The model will represent the language proficiency of the user and is designed to be referenced during both [[ writing analysis ]] and << feedback production >> .

We motivate our << model design >> by citing relevant research on [[ second language and cognitive skill acquisition ]] , and briefly discuss preliminary empirical evidence supporting the design .

We conclude by showing how our [[ design ]] can provide a rich and robust information base to a << language assessment / correction application >> by modeling user proficiency at a high level of granularity and specificity .

We conclude by showing how our [[ design ]] can provide a rich and robust information base to a language assessment / correction application by modeling << user proficiency >> at a high level of granularity and specificity .

We conclude by showing how our design can provide a rich and robust information base to a language assessment / correction application by modeling << user proficiency >> at a high level of [[ granularity ]] and specificity .

We conclude by showing how our design can provide a rich and robust information base to a language assessment / correction application by modeling user proficiency at a high level of [[ granularity ]] and << specificity >> .

We conclude by showing how our design can provide a rich and robust information base to a language assessment / correction application by modeling << user proficiency >> at a high level of granularity and [[ specificity ]] .

[[ Constraint propagation ]] is one of the key techniques in << constraint programming >> , and a large body of work has built up around it .

In this paper we present << SHORTSTR2 >> , a development of the [[ Simple Tabular Reduction algorithm STR2 + ]] .

We show that [[ SHORTSTR2 ]] is complementary to the existing algorithms << SHORTGAC >> and HAGGISGAC that exploit short supports , while being much simpler .

We show that [[ SHORTSTR2 ]] is complementary to the existing algorithms SHORTGAC and << HAGGISGAC >> that exploit short supports , while being much simpler .

We show that SHORTSTR2 is complementary to the existing algorithms [[ SHORTGAC ]] and << HAGGISGAC >> that exploit short supports , while being much simpler .

When a constraint is amenable to short supports , the [[ short support set ]] can be exponentially smaller than the << full-length support set >> .

We also show that [[ SHORTSTR2 ]] can be combined with a simple algorithm to identify << short supports >> from full-length supports , to provide a superior drop-in replacement for STR2 + .

We also show that [[ SHORTSTR2 ]] can be combined with a simple algorithm to identify short supports from full-length supports , to provide a superior << drop-in replacement >> for STR2 + .

We also show that << SHORTSTR2 >> can be combined with a simple [[ algorithm ]] to identify short supports from full-length supports , to provide a superior drop-in replacement for STR2 + .

We also show that SHORTSTR2 can be combined with a simple [[ algorithm ]] to identify << short supports >> from full-length supports , to provide a superior drop-in replacement for STR2 + .

We also show that << SHORTSTR2 >> can be combined with a simple algorithm to identify short supports from [[ full-length supports ]] , to provide a superior drop-in replacement for STR2 + .

We also show that << SHORTSTR2 >> can be combined with a simple algorithm to identify short supports from [[ full-length supports ]] , to provide a superior drop-in replacement for STR2 + .

We also show that SHORTSTR2 can be combined with a simple << algorithm >> to identify short supports from [[ full-length supports ]] , to provide a superior drop-in replacement for STR2 + .

We also show that SHORTSTR2 can be combined with a simple << algorithm >> to identify short supports from [[ full-length supports ]] , to provide a superior drop-in replacement for STR2 + .

We also show that SHORTSTR2 can be combined with a simple algorithm to identify short supports from full-length supports , to provide a superior [[ drop-in replacement ]] for << STR2 + >> .

We propose a [[ detection method ]] for << orthographic variants >> caused by transliteration in a large corpus .

The << method >> employs two [[ similarities ]] .

One is << string similarity >> based on [[ edit distance ]] .

The other is << contextual similarity >> by a [[ vector space model ]] .

Experimental results show that the << method >> performed a 0.889 [[ F-measure ]] in an open test .

[[ Uncertainty handling ]] plays an important role during << shape tracking >> .

We have recently shown that the [[ fusion of measurement information with system dynamics and shape priors ]] greatly improves the << tracking >> performance for very noisy images such as ultrasound sequences -LSB- 22 -RSB- .

We have recently shown that the fusion of measurement information with system dynamics and shape priors greatly improves the [[ tracking ]] performance for very << noisy images >> such as ultrasound sequences -LSB- 22 -RSB- .

We have recently shown that the fusion of measurement information with system dynamics and shape priors greatly improves the tracking performance for very << noisy images >> such as [[ ultrasound sequences ]] -LSB- 22 -RSB- .

Nevertheless , this << approach >> required [[ user initialization ]] of the tracking process .