Input: Visually-guided arm reaching movements are produced by distributed neural networks within parietal and frontal regions of the cerebral cortex. 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-. However, the NPV provides only a rough estimate of movement parameters -LRB- direction, velocity -RRB- and may even fail to reflect the parameters of movement when arm posture is changed. 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. 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. The network was trained by motor babbling in 5 positions. Simulations showed that -LRB- 1 -RRB- the network produced appropriate movement direction over a large part of the workspace; -LRB- 2 -RRB- small deviations of the actual trajectory from the desired trajectory existed at the extremities of the workspace; -LRB- 3 -RRB- these deviations were accompanied by large deviations of the NPV from both trajectories. These results suggest the NPV does not give a faithful image of cortical processing during arm reaching movements.

Visually-guided arm reaching movements

coordinated action of neu-rons

neuronal population vector -LRB- NPV -RRB-

NPV

arm posture

cortical motor command

NPV

motor cortex

broadly-tuned -LRB- muscular -RRB- proprioceptive

-LRB- cartesian -RRB- visual information

-LRB- angular -RRB- motor commands

two-link arm

NPV

NPV

image of cortical processing

arm reaching movements

distributed neural networks

two-layer self-organizing neural network

model

model

network

network

distributed neural networks

Visually-guided arm reaching movements

neuronal population vector -LRB- NPV -RRB-

coordinated action of neu-rons

model

cortical motor command

NPV

motor cortex

two-layer self-organizing neural network

-LRB- angular -RRB- motor commands

broadly-tuned -LRB- muscular -RRB- proprioceptive

two-layer self-organizing neural network

-LRB- cartesian -RRB- visual information

two-layer self-organizing neural network

broadly-tuned -LRB- muscular -RRB- proprioceptive

-LRB- cartesian -RRB- visual information

arm reaching movements

image of cortical processing


Input: It is well-known that diversity among base classifiers is crucial for constructing a strong ensemble. Most existing ensemble methods obtain diverse individual learners through resampling the instances or features. 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. Using pairwise constraints for ensemble construction is challenging because it remains unknown how to influence the base classifiers with the sampled pairwise constraints. We solve this problem with a two-step process. First, we transform the original instances into a new data representation using projections learnt from pairwise constraints. Then, we build the base clas-sifiers with the new data representation. We propose two methods for resampling pairwise constraints following the standard Bagging and Boosting algorithms, respectively. Extensive experiments validate the effectiveness of our method.

base classifiers

ensemble methods

base classifiers

data representation

base clas-sifiers

data representation

Bagging and Boosting algorithms

ensemble

ensemble construction

resampling pairwise constraints

ensemble construction

resampling pairwise constraints

pairwise constraints

sampled pairwise constraints

projections

pairwise constraints

base classifiers

ensemble

resampling pairwise constraints

ensemble construction

pairwise constraints

ensemble construction

projections

data representation

pairwise constraints

projections

data representation

base clas-sifiers

Bagging and Boosting algorithms

resampling pairwise constraints


Input: A new algorithm for solving the three dimensional container packing problem is proposed in this paper. This new algorithm deviates from the traditional approach of wall building and layering. It uses the concept of'' building growing'' from multiple sides of the container. We tested our method using all 760 test cases from the OR-Library. Experimental results indicate that the new algorithm is able to achieve an average packing utilization of more than 87%. This is better than the results reported in the literature.

algorithm

algorithm

It

method

algorithm

dimensional container packing problem

approach of wall building and layering

OR-Library

average packing utilization

algorithm

dimensional container packing problem

algorithm

approach of wall building and layering

OR-Library

method

average packing utilization

algorithm


Input: Current approaches to object category recognition require datasets of training images to be manually prepared, with varying degrees of supervision. 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. 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. Our approach can handle the high intra-class variability and large proportion of unrelated images returned by search engines. We evaluate the models on standard test sets, showing performance competitive with existing methods trained on hand prepared datasets.

approaches

datasets

approach

model

approach

models

test sets

methods

object category recognition

supervision

object category

visual words

spatial information

intra-class variability

unrelated images

image search engines

TSI-pLSA

pLSA

search engines

hand prepared datasets

approaches

object category recognition

datasets

approaches

approach

object category

pLSA

TSI-pLSA

pLSA

visual words

approach

intra-class variability

approach

unrelated images

search engines

unrelated images

hand prepared datasets

methods

spatial information

TSI-pLSA

intra-class variability

unrelated images

test sets

models

test sets

methods

methods

models


Input: 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. Also presented are the commercial applications of some of the research projects. When applicable, the discussion is placed in the framework of international collaborations.

speech processing

Human-Machine Communication

Natural Language Processing

Non Verbal and Multimodal Communication

speech processing

Human-Machine Communication

Natural Language Processing

Non Verbal and Multimodal Communication

Natural Language Processing

Human-Machine Communication

Non Verbal and Multimodal Communication

Human-Machine Communication


Input: 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. We illustrate classes of learning curve behavior in various chains, and show the manner in which TD is sensitive to the choice of its step-size and eligibility trace parameters.

analytical expressions

Markov chains

learning curve behavior

step-size and eligibility trace parameters

temporal difference value estimation algorithms

lookup table representations

lookup table representations

analytical expressions


Input: In this paper, we describe the pronominal anaphora resolution module of Lucy, a portable English understanding system. The design of this module was motivated by the observation that, although there exist many theories of anaphora resolution, no one of these theories is complete. Thus we have implemented a blackboard-like architecture in which individual partial theories can be encoded as separate modules that can interact to propose candidate antecedents and to evaluate each other's proposals.

pronominal anaphora resolution module

Lucy

English understanding system

blackboard-like architecture

module

anaphora resolution

pronominal anaphora resolution module

Lucy

Lucy

English understanding system


Input: 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. 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. Although preliminary, reported results are in line with state-of-the-art lexical acquisition systems. The issue of whether verbs sharing similar SCFs distributions happen to share similar semantic properties as well was also explored by clustering verbs that share frames with the same distribution using the Minimum Description Length Principle -LRB- MDL -RRB-. First experiments in this direction were carried out on Italian verbs with encouraging results.

unsupervised automatic acquisition of Italian and English verb subcategorization frames -LRB- SCFs -RRB-

Italian and English verb subcategorization frames -LRB- SCFs -RRB-

lexico-syntactic knowledge

SCFs

SCFs distributions

similar semantic properties

general and domain corpora

syntactically shallow-parsed corpora

technique

search heuristics

lexical acquisition systems

Minimum Description Length Principle -LRB- MDL -RRB-

general and domain corpora

unsupervised automatic acquisition of Italian and English verb subcategorization frames -LRB- SCFs -RRB-

syntactically shallow-parsed corpora

technique

search heuristics

technique

SCFs

lexico-syntactic knowledge


Input: Graph-cuts optimization is prevalent in vision and graphics problems. It is thus of great practical importance to parallelize the graph-cuts optimization using to-day's ubiquitous multi-core machines. 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. It is non-trivial to parallelize as expensive synchronization overhead easily offsets the advantage of parallelism. In this paper, we propose a novel adaptive bottom-up approach to parallelize the BK algorithm. We first uniformly partition the graph into a number of regularly-shaped dis-joint subgraphs and process them in parallel, then we incre-mentally merge the subgraphs in an adaptive way to obtain the global optimum. The new algorithm has three benefits: 1 -RRB- it is more cache-friendly within smaller subgraphs; 2 -RRB- it keeps balanced workloads among computing cores; 3 -RRB- it causes little overhead and is adaptable to the number of available cores. Extensive experiments in common applications such as 2D/3D image segmentations and 3D surface fitting demonstrate the effectiveness of our approach.

Graph-cuts optimization

graph-cuts optimization

serial algorithm

BK algorithm

adaptive bottom-up approach

BK algorithm

vision and graphics problems

2D/3D image segmentations

3D surface fitting

multi-core machines

synchronization overhead

parallelism

graph

regularly-shaped dis-joint subgraphs

subgraphs

global optimum

subgraphs

balanced workloads

overhead

them

algorithm

applications

approach

Graph-cuts optimization

vision and graphics problems

multi-core machines

graph-cuts optimization

adaptive bottom-up approach

BK algorithm

BK algorithm

serial algorithm

2D/3D image segmentations

applications

3D surface fitting

applications

applications

approach

2D/3D image segmentations

3D surface fitting


Input: 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. 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. We begin by asking which approach we should favor for two typical image seg-mentation tasks. Studying this question leads to two new methods that draw ideas from both existing approaches. Our new methods use the EIoEU approximation paired with high quality candidate solutions. Experimentally we show that our new approaches lead to improved performance on both image segmentation tasks.

loss-aware predictions

loss-aware predictions in image segmentation settings

image segmentation settings

image seg-mentation tasks

image segmentation tasks

evaluation function

Expected-IoU -LRB- EIoU -RRB- score

Expected-Intersection-over-Expected-Union -LRB- EIoEU -RRB-

EIoU

EIoEU

Intersection-over-Union -LRB- IoU -RRB- measure

image segmentation systems

EIoEU approximation

dominant approaches

first

second approach

methods

approaches

Intersection-over-Union -LRB- IoU -RRB- measure

evaluation function

first

dominant approaches

second approach

dominant approaches

Intersection-over-Union -LRB- IoU -RRB- measure

image segmentation systems

image segmentation tasks

approaches

EIoEU approximation

methods


Input: Boosting methods are known not to usually overfit training data even as the size of the generated classifiers becomes large. Schapire et al. attempted to explain this phenomenon in terms of the margins the classifier achieves on training examples. 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. In this paper, we take a close look at Breiman's compelling but puzzling results. 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. Thus, we find maximizing the margins is desirable, but not necessarily at the expense of other factors, especially base-classifier complexity.

Boosting methods

classifiers

classifier

boosting algorithm

arc-gv

AdaBoost

arc-gv

base classifiers

margins theory

margins distribution

complexity

base-classifier complexity

arc-gv

boosting algorithm

base classifiers

arc-gv

arc-gv

margins distribution

arc-gv

AdaBoost

complexity

base classifiers


Input: 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. 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. 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-.

transfer phase

machine translation -LRB- MT -RRB- systems

analysis

generation

machine translation

MT

case-based MT -LRB- CBMT -RRB-

it

lexical rules

case-based reasoning

transfer system

Similarity-driven Transfer System -LRB- SimTran -RRB-

transfer phase

machine translation -LRB- MT -RRB- systems

transfer phase

analysis

transfer phase

generation

analysis

generation

case-based reasoning

machine translation

Similarity-driven Transfer System -LRB- SimTran -RRB-

case-based MT -LRB- CBMT -RRB-

Similarity-driven Transfer System -LRB- SimTran -RRB-

transfer system


Input: This paper addresses the problem of optimal alignment of non-rigid surfaces from multi-view video observations to obtain a temporally consistent representation. Conventional non-rigid surface tracking performs frame-to-frame alignment which is subject to the accumulation of errors resulting in a drift over time. Recently, non-sequential tracking approaches have been introduced which reorder the input data based on a dissimilarity measure. One or more input sequences are represented in a tree with reduced alignment path length. They demonstrate a reduced drift and increased robustness to large non-rigid deformations. However, jumps may occur in the aligned mesh sequence where branches of the tree meet due to different error accumulation along them. 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. A novel cluster tree enforces sequential tracking in local segments of the sequence while allowing global non-sequential traversal among these segments. This provides a mechanism to create a tree structure which reduces the number and size of jumps between branches and limits the lenght of branches as well. Comprehensive evaluation is performed on a variety of challenging non-rigid surfaces including face, cloth and people. It demonstrates that the proposed cluster tree achieves better temporal consistency than the previous sequential and non-sequential tracking approaches. Quantitative analysis on a created synthetic facial performance also shows an improvement by the cluster tree.

optimal alignment of non-rigid surfaces

Optimisation of the tree

sequential tracking in local segments

multi-view video observations

reduced drift

non-rigid deformations

branches

tree

error accumulation

local segments

global non-sequential traversal

tree structure

non-rigid surfaces

face

cloth

people

temporally consistent representation

non-rigid surface tracking

frame-to-frame alignment

non-sequential tracking approaches

non-sequential tracking

cluster tree

cluster tree

sequential and non-sequential tracking approaches

cluster tree

dissimilarity measure

robustness

temporal consistency

temporal consistency

synthetic facial performance

segments

optimal alignment of non-rigid surfaces

temporally consistent representation

multi-view video observations

optimal alignment of non-rigid surfaces

frame-to-frame alignment

non-rigid surface tracking

dissimilarity measure

non-sequential tracking approaches

Optimisation of the tree

non-sequential tracking

cluster tree

sequential tracking in local segments

cluster tree

global non-sequential traversal

robustness

non-rigid deformations

temporal consistency

Optimisation of the tree

temporal consistency

cluster tree

synthetic facial performance

cluster tree

face

non-rigid surfaces

cloth

non-rigid surfaces

people

non-rigid surfaces

face

cloth

cloth

people

cluster tree

sequential and non-sequential tracking approaches


Input: The translation of English text into American Sign Language -LRB- ASL -RRB- animation tests the limits of traditional MT architectural designs. A new semantic representation is proposed that uses virtual reality 3D scene modeling software to produce spatially complex ASL phenomena called'' classifier predicates.'' 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.

translation of English text into American Sign Language -LRB- ASL -RRB- animation

MT architectural designs

semantic representation

virtual reality 3D scene modeling software

multi-pathway MT architecture design

spatially complex ASL phenomena

classifier predicates

interlingua

transfer

model

direct approaches

system

MT architectural designs

translation of English text into American Sign Language -LRB- ASL -RRB- animation

semantic representation

spatially complex ASL phenomena

virtual reality 3D scene modeling software

semantic representation

classifier predicates

spatially complex ASL phenomena

transfer

direct approaches

transfer

system

direct approaches

system


Input: 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. 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. The structures induced for such constructions are argued to be more appropriate to data involving conjunction than some previous proposals have been. The extension is shown to be parseable by a simple extension to an existing parsing method for GPSG.

extension

extension

extension

extension

GPSG grammatical formalism

grammar

parsing method

GPSG

non-terminals

schematic variables

crossed serial dependencies

Dutch subordinate clauses

constructions

GPSG grammatical formalism

extension

extension

grammar

grammar

crossed serial dependencies

extension

extension

parsing method

extension

parsing method

GPSG


Input: 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. Functional objects do not have discriminative appearance and shape, but they affect behavior of people in the scene. For example, they'' attract'' people to approach them for satisfying certain needs -LRB- e.g., vending machines could quench thirst -RRB-, or'' repel'' people to avoid them -LRB- e.g., grass lawns -RRB-. Therefore, functional objects can be viewed as'' dark matter'', emanating'' dark energy'' that affects people's trajectories in the video. To detect'' dark matter'' and infer their'' dark energy'' field, we extend the La-grangian mechanics. People are treated as particle-agents with latent intents to approach'' dark matter'' and thus satisfy their needs, where their motions are subject to a composite'' dark energy'' field of all functional objects in the scene. We make the assumption that people take globally optimal paths toward the intended'' dark matter'' while avoiding latent obstacles. A Bayesian framework is used to probabilistically model: people's trajectories and intents, constraint map of the scene, and locations of functional objects. A data-driven Markov Chain Monte Carlo -LRB- MCMC -RRB- process is used for inference. 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.

approach

they

localizing functional objects

inference

localizing functional objects

predicting people's trajectories

surveillance videos

videos of public squares and courtyards

domain knowledge

semantic object classes

Functional objects

discriminative appearance and shape

functional objects

functional objects

people's trajectories and intents

constraint map of the scene

locations of functional objects

video footage

La-grangian mechanics

Bayesian framework

data-driven Markov Chain Monte Carlo -LRB- MCMC -RRB- process

approach

localizing functional objects

surveillance videos

localizing functional objects

Bayesian framework

people's trajectories and intents

Bayesian framework

constraint map of the scene

Bayesian framework

locations of functional objects

data-driven Markov Chain Monte Carlo -LRB- MCMC -RRB- process

inference

semantic object classes

domain knowledge

people's trajectories and intents

constraint map of the scene

constraint map of the scene

locations of functional objects

localizing functional objects

predicting people's trajectories

videos of public squares and courtyards

localizing functional objects

videos of public squares and courtyards

predicting people's trajectories


Input: We propose a process model for hierarchical perceptual sound organization, which recognizes perceptual sounds included in incoming sound signals. We consider perceptual sound organization as a scene analysis problem in the auditory domain. Our model consists of multiple processing modules and a hypothesis network for quantitative integration of multiple sources of information. When input information for each processing module is available, the module rises to process it and asynchronously writes output information to the hypothesis network. On the hypothesis network, individual information is integrated and an optimal internal model of perceptual sounds is automatically constructed. 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. Experimental results show that our method has permitted autonomous, stable and effective information integration to construct the internal model of hierarchical perceptual sounds.

process model

processing modules

hypothesis network

processing module

hypothesis network

hypothesis network

internal model

model

music scene analysis system

internal model

hierarchical perceptual sound organization

perceptual sound organization

scene analysis problem

perceptual sounds

incoming sound signals

acoustic signals of ensemble music

information integration

auditory domain

perceptual sounds

rhythm

chords

source-separated musical notes

hierarchical perceptual sounds

model

module

method

process model

hierarchical perceptual sound organization

scene analysis problem

perceptual sound organization

internal model

perceptual sounds

music scene analysis system

acoustic signals of ensemble music

music scene analysis system

rhythm

music scene analysis system

chords

music scene analysis system

source-separated musical notes

information integration

internal model

internal model

hierarchical perceptual sounds

perceptual sounds

incoming sound signals

processing modules

model

hypothesis network

model

internal model

hypothesis network

auditory domain

scene analysis problem

information integration

method

processing modules

hypothesis network

rhythm

chords

chords

source-separated musical notes


Input: We directly investigate a subject of much recent debate: do word sense disambigation models help statistical machine translation quality? We present empirical results casting doubt on this common, but unproved, assumption. 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. Error analysis suggests several key factors behind this surprising finding, including inherent limitations of current statistical MT architectures.

word sense disambigation models

Chinese word sense disambiguation model

IBM statistical MT system

word sense disambiguation

statistical machine translation system

Error analysis

statistical MT architectures

statistical machine translation quality

translation quality

translation candidates

word sense disambigation models

statistical machine translation quality

Chinese word sense disambiguation model

translation candidates

translation candidates

IBM statistical MT system

word sense disambiguation

statistical machine translation system

translation quality

word sense disambiguation

translation quality

statistical machine translation system


Input: Image sequence processing techniques are used to study exchange, growth, and transport processes and to tackle key questions in environmental physics and biology. 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. Therefore the performance and optimization of low-level motion estimators is discussed. 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. The accuracy of the tensor method is verified with computer-generated sequences and a calibrated image sequence. 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. With a simple two-point calibration, these effects can efficiently be suppressed. 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.

Image sequence processing techniques

dynamical processes

tensor method

derivative filters

tensor method

two-point calibration

exchange, growth, and transport processes

estimation of the motion field

optimization of low-level motion estimators

motion estimation

analysis of plant growth

ocean surface microturbulence in IR image sequences

sediment transport

environmental physics

biology

real-world images

computer-generated sequences

calibrated image sequence

applications

techniques

accuracy

accuracy

pixels/frame

accuracy

accuracy

first-order derivatives of the motion field

dynamical changes of the moving objects

displacement vector fields -LRB- DVF -RRB-

CCD sensors

spatial nonuni-formity

responsivity

Image sequence processing techniques

exchange, growth, and transport processes

estimation of the motion field

applications

derivative filters

tensor method

real-world images

pixels/frame

CCD sensors

motion estimation

techniques

analysis of plant growth

techniques

ocean surface microturbulence in IR image sequences

techniques

sediment transport

environmental physics

biology

first-order derivatives of the motion field

dynamical changes of the moving objects

computer-generated sequences

calibrated image sequence

analysis of plant growth

ocean surface microturbulence in IR image sequences

ocean surface microturbulence in IR image sequences

sediment transport

accuracy

estimation of the motion field

pixels/frame

displacement vector fields -LRB- DVF -RRB-

accuracy

tensor method

computer-generated sequences

tensor method

calibrated image sequence

tensor method

accuracy

motion estimation

spatial nonuni-formity

responsivity

responsivity

CCD sensors


Input: We present a Czech-English statistical machine translation system which performs tree-to-tree translation of dependency structures. The only bilingual resource required is a sentence-aligned parallel corpus. All other resources are monolingual. We also refer to an evaluation method and plan to compare our system's output with a benchmark system.

Czech-English statistical machine translation system

tree-to-tree translation of dependency structures

bilingual resource

sentence-aligned parallel corpus

system

benchmark system

Czech-English statistical machine translation system

tree-to-tree translation of dependency structures

sentence-aligned parallel corpus

bilingual resource

system

benchmark system


Input: This paper describes the understanding process of the spatial descriptions in Japanese. In order to understand the described world, the authors try to reconstruct the geometric model of the global scene from the scenic descriptions drawing a space. It is done by an experimental computer program SPRINT, which takes natural language texts and produces a model of the described world. 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. This makes it possible to express the vagueness of the spatial concepts and to derive the maximally plausible interpretation from a chunk of information accumulated as the constraints. The interpretation reflects the temporary belief about the world.

spatial descriptions

qualitative spatial constraints

numerical constraints

spatial attributes of the entities

spatial concepts

Japanese

natural language texts

geometric model

computer program SPRINT

model

model

Japanese

spatial descriptions

spatial attributes of the entities

numerical constraints


Input: Learning video representation is not a trivial task, as video is an information-intensive media where each frame does not exist independently. Locally, a video frame is visually and semantically similar with its adjacent frames. Holistically, a video has its inherent structure -- the correlations among video frames. For example, even the frames far from each other may also hold similar semantics. Such context information is therefore important to characterize the intrinsic representation of a video frame. In this paper, we present a novel approach to learn the deep video representation by exploring both local and holistic contexts. Specifically, we propose a triplet sampling mechanism to encode the local temporal relationship of adjacent frames based on their deep representations. In addition, we incorporate the graph structure of the video, as a priori, to holistically preserve the inherent correlations among video frames. Our approach is fully unsupervised and trained in an end-to-end deep convolutional neu-ral network architecture. 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.

Learning video representation

video recognition tasks

retrieval

classification

highlight detection

semantics

context information

local and holistic contexts

local temporal relationship of adjacent frames

graph structure of the video

priori

intrinsic representation of a video frame

deep video representation

triplet sampling mechanism

deep representations

end-to-end deep convolutional neu-ral network architecture

approach

approach

learned representation

video representations

context information

intrinsic representation of a video frame

approach

deep video representation

local and holistic contexts

approach

triplet sampling mechanism

local temporal relationship of adjacent frames

deep representations

triplet sampling mechanism

graph structure of the video

priori

end-to-end deep convolutional neu-ral network architecture

approach

learned representation

video representations

video recognition tasks

learned representation

video recognition tasks

video representations

retrieval

video recognition tasks

classification

video recognition tasks

highlight detection

video recognition tasks

retrieval

classification

classification

highlight detection


Input: For mobile speech application, speaker DOA estimation accuracy, interference robustness and compact physical size are three key factors. Considering the size, we utilized acoustic vector sensor -LRB- AVS -RRB- and proposed a DOA estimation algorithm previously -LSB- 1 -RSB-, offering high accuracy with larger-than-15dB SNR but is deteriorated by non-speech interferences -LRB- NSI -RRB-. This paper develops a robust speaker DOA estimation algorithm. 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. 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. Intensive experiments demonstrate an improved performance of our proposed algorithm under various NSI conditions even when SIR is smaller than 0dB.

mobile speech application

speaker DOA estimation accuracy

interference robustness

compact physical size

SIR

acoustic vector sensor -LRB- AVS -RRB-

DOA estimation algorithm

robust speaker DOA estimation algorithm

inter-sensor data ratio model

AVS

bispectrum domain -LRB- BISDR -RRB-

bispectrum

bispectrum mask

BISDR

non-speech interferences -LRB- NSI -RRB-

zero value of Gaussian process

distribution of speech and NSI

NSI

speaker DOA cues

NSI

speech sparsity

bispectrum amplitude

NSI conditions

It

favorable properties

algorithm

speaker DOA estimation accuracy

mobile speech application

interference robustness

mobile speech application

compact physical size

mobile speech application

speaker DOA estimation accuracy

interference robustness

interference robustness

compact physical size

zero value of Gaussian process

distribution of speech and NSI

It

favorable properties

inter-sensor data ratio model

AVS

bispectrum domain -LRB- BISDR -RRB-

AVS

bispectrum mask

speaker DOA cues

BISDR

speaker DOA cues

algorithm

NSI conditions

zero value of Gaussian process

favorable properties

distribution of speech and NSI

favorable properties


Input: 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 Language Processor -RRB- of the New York University. The former can take advantage of the language independent developments of the latter, while focusing on idiosyncrasies for Dutch. 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. Such an application can be of use for medical administrative purposes in a hospital environment.

morphological component

NLP-system for Dutch -LRB- Dutch Medical Language Processor-DMLP -RRB-

language independent modules

LSP-MLP system -LRB- Linguistic String Project-Medical Language Processor -RRB-

HyperText Mark-Up Language -LRB- HTML -RRB- technology

former

latter

application

relevant information

application

idiosyncrasies

patient discharge summary -LRB- PDS -RRB-

Dutch

highlighting of relevant information

medical administrative purposes

morphological component

NLP-system for Dutch -LRB- Dutch Medical Language Processor-DMLP -RRB-

language independent modules

LSP-MLP system -LRB- Linguistic String Project-Medical Language Processor -RRB-

relevant information

patient discharge summary -LRB- PDS -RRB-

latter

former

Dutch

idiosyncrasies

HyperText Mark-Up Language -LRB- HTML -RRB- technology

highlighting of relevant information

application

medical administrative purposes


Input: 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-. We describe a new system that enhances Criterion's capability, by evaluating multiple aspects of coherence in essays. This system identifies features of sentences based on semantic similarity measures and discourse structure. A support vector machine uses these features to capture breakdowns in coherence due to relatedness to the essay question and relatedness between discourse elements. Intra-sentential quality is evaluated with rule-based heuristics. Results indicate that the system yields higher performance than a baseline on all three aspects.

CriterionSM Online Essay Evaluation Service

essay-based discourse elements

thesis statements

Criterion's capability

features

discourse structure

features

breakdowns in coherence

discourse elements

system

system

system

baseline

coherence in essays

semantic similarity measures

Intra-sentential quality

support vector machine

rule-based heuristics

essay-based discourse elements

CriterionSM Online Essay Evaluation Service

thesis statements

essay-based discourse elements

system

Criterion's capability

system

features

semantic similarity measures

features

discourse structure

features

features

support vector machine

features

breakdowns in coherence

coherence in essays

system

rule-based heuristics

Intra-sentential quality

discourse structure

semantic similarity measures

system

baseline


Input: 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. Tokens are computed via a small-to-large scale grouping procedure employing a'' greedy'', best-first, strategy for choosing the support of new tokens. The resulting image description is rich and redundant in that a given segment of image contour may be described by multiple tokens at different scales, and by more than one token at any given scale. This property facilitates selection and characterization of portions of the image based on local CURVE-ELEMENT attributes.

algorithm

labeling curvilinear structure

image description

line drawings

edge images

image data

CURVE-ELEMENT tokens

spatially-indexed and scale-indexed data structure

image contour

local CURVE-ELEMENT attributes

small-to-large scale grouping procedure

algorithm

labeling curvilinear structure

line drawings

labeling curvilinear structure

edge images

labeling curvilinear structure

line drawings

edge images

CURVE-ELEMENT tokens

spatially-indexed and scale-indexed data structure