pwc-archive/papers-with-abstracts huggingface dataset:
Use of Modality and Negation in Semantically-Informed Syntactic MT
Authors: Kathryn Baker, Michael Bloodgood, Bonnie J. Dorr, Chris Callison-Burch, Nathaniel W. Filardo, Christine Piatko, Lori Levin, Scott Miller
Date: 2015-02-05 00:00:00
Tasks: Machine Translation, Negation, Translation

This paper describes the resource- and system-building efforts of an
eight-week Johns Hopkins University Human Language Technology Center of
Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on
Semantically-Informed Machine Translation (SIMT). We describe a new
modality/negation (MN) annotation scheme, the creation of a (publicly
available) MN lexicon, and two automated MN taggers that we built using the
annotation scheme and lexicon. Our annotation scheme isolates three components
of modality and negation: a trigger (a word that conveys modality or negation),
a target (an action associated with modality or negation) and a holder (an
experiencer of modality). We describe how our MN lexicon was semi-automatically
produced and we demonstrate that a structure-based MN tagger results in
precision around 86% (depending on genre) for tagging of a standard LDC data
set.
  We apply our MN annotation scheme to statistical machine translation using a
syntactic framework that supports the inclusion of semantic annotations.
Syntactic tags enriched with semantic annotations are assigned to parse trees
in the target-language training texts through a process of tree grafting. While
the focus of our work is modality and negation, the tree grafting procedure is
general and supports other types of semantic information. We exploit this
capability by including named entities, produced by a pre-existing tagger, in
addition to the MN elements produced by the taggers described in this paper.
The resulting system significantly outperformed a linguistically naive baseline
model (Hiero), and reached the highest scores yet reported on the NIST 2009
Urdu-English test set. This finding supports the hypothesis that both syntactic
and semantic information can improve translation quality.
Estimating Optimal Active Learning via Model Retraining Improvement
Authors: Lewis P. G. Evans, Niall M. Adams, Christoforos Anagnostopoulos
Date: 2015-02-05 00:00:00
Tasks: Active Learning, model

A central question for active learning (AL) is: "what is the optimal
selection?" Defining optimality by classifier loss produces a new
characterisation of optimal AL behaviour, by treating expected loss reduction
as a statistical target for estimation. This target forms the basis of model
retraining improvement (MRI), a novel approach providing a statistical
estimation framework for AL. This framework is constructed to address the
central question of AL optimality, and to motivate the design of estimation
algorithms. MRI allows the exploration of optimal AL behaviour, and the
examination of AL heuristics, showing precisely how they make sub-optimal
selections. The abstract formulation of MRI is used to provide a new guarantee
for AL, that an unbiased MRI estimator should outperform random selection. This
MRI framework reveals intricate estimation issues that in turn motivate the
construction of new statistical AL algorithms. One new algorithm in particular
performs strongly in a large-scale experimental study, compared to standard AL
methods. This competitive performance suggests that practical efforts to
minimise estimation bias may be important for AL applications.
Learning Articulated Motions From Visual Demonstration
Authors: Sudeep Pillai, Matthew R. Walter, Seth Teller
Date: 2015-02-05 00:00:00
Tasks: Motion Segmentation, Object, Pose Estimation

Many functional elements of human homes and workplaces consist of rigid
components which are connected through one or more sliding or rotating
linkages. Examples include doors and drawers of cabinets and appliances;
laptops; and swivel office chairs. A robotic mobile manipulator would benefit
from the ability to acquire kinematic models of such objects from observation.
This paper describes a method by which a robot can acquire an object model by
capturing depth imagery of the object as a human moves it through its range of
motion. We envision that in future, a machine newly introduced to an
environment could be shown by its human user the articulated objects particular
to that environment, inferring from these "visual demonstrations" enough
information to actuate each object independently of the user.
  Our method employs sparse (markerless) feature tracking, motion segmentation,
component pose estimation, and articulation learning; it does not require prior
object models. Using the method, a robot can observe an object being exercised,
infer a kinematic model incorporating rigid, prismatic and revolute joints,
then use the model to predict the object's motion from a novel vantage point.
We evaluate the method's performance, and compare it to that of a previously
published technique, for a variety of household objects.
Performance Analysis of Cone Detection Algorithms
Authors: Letizia Mariotti, Nicholas Devaney
Date: 2015-02-05 00:00:00

Many algorithms have been proposed to help clinicians evaluate cone density
and spacing, as these may be related to the onset of retinal diseases. However,
there has been no rigorous comparison of the performance of these algorithms.
In addition, the performance of such algorithms is typically determined by
comparison with human observers. Here we propose a technique to simulate
realistic images of the cone mosaic. We use the simulated images to test the
performance of two popular cone detection algorithms and we introduce an
algorithm which is used by astronomers to detect stars in astronomical images.
We use Free Response Operating Characteristic (FROC) curves to evaluate and
compare the performance of the three algorithms. This allows us to optimize the
performance of each algorithm. We observe that performance is significantly
enhanced by up-sampling the images. We investigate the effect of noise and
image quality on cone mosaic parameters estimated using the different
algorithms, finding that the estimated regularity is the most sensitive
parameter.
  This paper was published in JOSA A and is made available as an electronic
reprint with the permission of OSA. The paper can be found at the following URL
on the OSA website: http://www.opticsinfobase.org/abstract.cfm?msid=224577.
Systematic or multiple reproduction or distribution to multiple locations via
electronic or other means is prohibited and is subject to penalties under law.
A Simple Expression for Mill's Ratio of the Student's $t$-Distribution
Authors: Francesco Orabona
Date: 2015-02-05 00:00:00

I show a simple expression of the Mill's ratio of the Student's
t-Distribution. I use it to prove Conjecture 1 in P. Auer, N. Cesa-Bianchi, and
P. Fischer. Finite-time analysis of the multiarmed bandit problem. Mach.
Learn., 47(2-3):235--256, May 2002.
A PARTAN-Accelerated Frank-Wolfe Algorithm for Large-Scale SVM Classification
Authors: Emanuele Frandi, Ricardo Nanculef, Johan A. K. Suykens
Date: 2015-02-05 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Frank-Wolfe algorithms have recently regained the attention of the Machine
Learning community. Their solid theoretical properties and sparsity guarantees
make them a suitable choice for a wide range of problems in this field. In
addition, several variants of the basic procedure exist that improve its
theoretical properties and practical performance. In this paper, we investigate
the application of some of these techniques to Machine Learning, focusing in
particular on a Parallel Tangent (PARTAN) variant of the FW algorithm that has
not been previously suggested or studied for this type of problems. We provide
experiments both in a standard setting and using a stochastic speed-up
technique, showing that the considered algorithms obtain promising results on
several medium and large-scale benchmark datasets for SVM classification.
Semantic Embedding Space for Zero-Shot Action Recognition
Authors: Xun Xu, Timothy Hospedales, Shaogang Gong
Date: 2015-02-05 00:00:00
Tasks: Action Recognition, Attribute, Data Augmentation, Temporal Action Localization, Zero-Shot Action Recognition, Zero-Shot Learning

The number of categories for action recognition is growing rapidly. It is
thus becoming increasingly hard to collect sufficient training data to learn
conventional models for each category. This issue may be ameliorated by the
increasingly popular 'zero-shot learning' (ZSL) paradigm. In this framework a
mapping is constructed between visual features and a human interpretable
semantic description of each category, allowing categories to be recognised in
the absence of any training data. Existing ZSL studies focus primarily on image
data, and attribute-based semantic representations. In this paper, we address
zero-shot recognition in contemporary video action recognition tasks, using
semantic word vector space as the common space to embed videos and category
labels. This is more challenging because the mapping between the semantic space
and space-time features of videos containing complex actions is more complex
and harder to learn. We demonstrate that a simple self-training and data
augmentation strategy can significantly improve the efficacy of this mapping.
Experiments on human action datasets including HMDB51 and UCF101 demonstrate
that our approach achieves the state-of-the-art zero-shot action recognition
performance.
Using temporal abduction for biosignal interpretation: A case study on QRS detection
Authors: Tomás Teijeiro, Paulo Félix, Jesús Presedo
Date: 2015-02-05 00:00:00
Tasks: Rhythm, Specificity

In this work, we propose an abductive framework for biosignal interpretation,
based on the concept of Temporal Abstraction Patterns. A temporal abstraction
pattern defines an abstraction relation between an observation hypothesis and a
set of observations constituting its evidence support. New observations are
generated abductively from any subset of the evidence of a pattern, building an
abstraction hierarchy of observations in which higher levels contain those
observations with greater interpretative value of the physiological processes
underlying a given signal. Non-monotonic reasoning techniques have been applied
to this model in order to find the best interpretation of a set of initial
observations, permitting even to correct these observations by removing, adding
or modifying them in order to make them consistent with the available domain
knowledge. Some preliminary experiments have been conducted to apply this
framework to a well known and bounded problem: the QRS detection on ECG
signals. The objective is not to provide a new better QRS detector, but to test
the validity of an abductive paradigm. These experiments show that a knowledge
base comprising just a few very simple rhythm abstraction patterns can enhance
the results of a state of the art algorithm by significantly improving its
detection F1-score, besides proving the ability of the abductive framework to
correct both sensitivity and specificity failures.
A mixture Cox-Logistic model for feature selection from survival and classification data
Authors: Samuel Branders, Roberto D'Ambrosio, Pierre Dupont
Date: 2015-02-05 00:00:00
Tasks: feature selection, General Classification

This paper presents an original approach for jointly fitting survival times
and classifying samples into subgroups. The Coxlogit model is a generalized
linear model with a common set of selected features for both tasks. Survival
times and class labels are here assumed to be conditioned by a common risk
score which depends on those features. Learning is then naturally expressed as
maximizing the joint probability of subgroup labels and the ordering of
survival events, conditioned to a common weight vector. The model is estimated
by minimizing a regularized log-likelihood through a coordinate descent
algorithm.
  Validation on synthetic and breast cancer data shows that the proposed
approach outperforms a standard Cox model or logistic regression when both
predicting the survival times and classifying new samples into subgroups. It is
also better at selecting informative features for both tasks.
Ring artifacts correction in compressed sensing tomographic reconstruction
Authors: Pierre Paleo, Alessandro Mirone
Date: 2015-02-05 00:00:00
Tasks: compressed sensing

We present a novel approach to handle ring artifacts correction in compressed
sensing tomographic reconstruction. The correction is part of the
reconstruction process, which differs from classical sinogram pre-processing
and image post-processing techniques. The principle of compressed sensing
tomographic reconstruction is presented. Then, we show that the ring artifacts
correction can be integrated in the reconstruction problem formalism. We
provide numerical results for both simulated and real data. This technique is
included in the PyHST2 code which is used at the European Synchrotron Radiation
Facility for tomographic reconstruction.
Fast Constraint Propagation for Image Segmentation
Authors: Peng Han
Date: 2015-02-05 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

This paper presents a novel selective constraint propagation method for
constrained image segmentation. In the literature, many pairwise constraint
propagation methods have been developed to exploit pairwise constraints for
cluster analysis. However, since most of these methods have a polynomial time
complexity, they are not much suitable for segmentation of images even with a
moderate size, which is actually equivalent to cluster analysis with a large
data size. Considering the local homogeneousness of a natural image, we choose
to perform pairwise constraint propagation only over a selected subset of
pixels, but not over the whole image. Such a selective constraint propagation
problem is then solved by an efficient graph-based learning algorithm. To
further speed up our selective constraint propagation, we also discard those
less important propagated constraints during graph-based learning. Finally, the
selectively propagated constraints are exploited based on $L_1$-minimization
for normalized cuts over the whole image. The experimental results demonstrate
the promising performance of the proposed method for segmentation with
selectively propagated constraints.
Exponentiated Subgradient Algorithm for Online Optimization under the Random Permutation Model
Authors: Reza Eghbali, Jon Swenson, Maryam Fazel
Date: 2014-10-27 00:00:00

Online optimization problems arise in many resource allocation tasks, where
the future demands for each resource and the associated utility functions
change over time and are not known apriori, yet resources need to be allocated
at every point in time despite the future uncertainty. In this paper, we
consider online optimization problems with general concave utilities. We modify
and extend an online optimization algorithm proposed by Devanur et al. for
linear programming to this general setting. The model we use for the arrival of
the utilities and demands is known as the random permutation model, where a
fixed collection of utilities and demands are presented to the algorithm in
random order. We prove that under this model the algorithm achieves a
competitive ratio of $1-O(\epsilon)$ under a near-optimal assumption that the
bid to budget ratio is $O (\frac{\epsilon^2}{\log({m}/{\epsilon})})$, where $m$
is the number of resources, while enjoying a significantly lower computational
cost than the optimal algorithm proposed by Kesselheim et al. We draw a
connection between the proposed algorithm and subgradient methods used in
convex optimization. In addition, we present numerical experiments that
demonstrate the performance and speed of this algorithm in comparison to
existing algorithms.
Beyond Word-based Language Model in Statistical Machine Translation
Authors: Jiajun Zhang, Shujie Liu, Mu Li, Ming Zhou, Cheng-qing Zong
Date: 2015-02-05 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

Language model is one of the most important modules in statistical machine
translation and currently the word-based language model dominants this
community. However, many translation models (e.g. phrase-based models) generate
the target language sentences by rendering and compositing the phrases rather
than the words. Thus, it is much more reasonable to model dependency between
phrases, but few research work succeed in solving this problem. In this paper,
we tackle this problem by designing a novel phrase-based language model which
attempts to solve three key sub-problems: 1, how to define a phrase in language
model; 2, how to determine the phrase boundary in the large-scale monolingual
data in order to enlarge the training set; 3, how to alleviate the data
sparsity problem due to the huge vocabulary size of phrases. By carefully
handling these issues, the extensive experiments on Chinese-to-English
translation show that our phrase-based language model can significantly improve
the translation quality by up to +1.47 absolute BLEU score.
The Neutrosophic Entropy and its Five Components
Authors: Vasile Patrascu
Date: 2015-02-05 00:00:00
Tasks: Unity

This paper presents two variants of penta-valued representation for
neutrosophic entropy. The first is an extension of Kaufmann's formula and the
second is an extension of Kosko's formula.
  Based on the primary three-valued information represented by the degree of
truth, degree of falsity and degree of neutrality there are built some
penta-valued representations that better highlights some specific features of
neutrosophic entropy. Thus, we highlight five features of neutrosophic
uncertainty such as ambiguity, ignorance, contradiction, neutrality and
saturation. These five features are supplemented until a seven partition of
unity by adding two features of neutrosophic certainty such as truth and
falsity.
  The paper also presents the particular forms of neutrosophic entropy obtained
in the case of bifuzzy representations, intuitionistic fuzzy representations,
paraconsistent fuzzy representations and finally the case of fuzzy
representations.
Empirical Q-Value Iteration
Authors: Dileep Kalathil, Vivek S. Borkar, Rahul Jain
Date: 2014-11-30 00:00:00
Tasks: Q-Learning

We propose a new simple and natural algorithm for learning the optimal
Q-value function of a discounted-cost Markov Decision Process (MDP) when the
transition kernels are unknown. Unlike the classical learning algorithms for
MDPs, such as Q-learning and actor-critic algorithms, this algorithm doesn't
depend on a stochastic approximation-based method. We show that our algorithm,
which we call the empirical Q-value iteration (EQVI) algorithm, converges to
the optimal Q-value function. We also give a rate of convergence or a
non-asymptotic sample complexity bound, and also show that an asynchronous (or
online) version of the algorithm will also work. Preliminary experimental
results suggest a faster rate of convergence to a ball park estimate for our
algorithm compared to stochastic approximation-based algorithms.
Towards a Model Theory for Distributed Representations
Authors: Ramanathan Guha
Date: 2014-10-21 00:00:00
Tasks: model

Distributed representations (such as those based on embeddings) and discrete
representations (such as those based on logic) have complementary strengths. We
explore one possible approach to combining these two kinds of representations.
We present a model theory/semantics for first order logic based on vectors of
reals. We describe the model theory, discuss some interesting properties of
such a system and present a simple approach to query answering.
Super-resolution MRI Using Finite Rate of Innovation Curves
Authors: Greg Ongie, Mathews Jacob
Date: 2015-01-08 00:00:00
Tasks: Super-Resolution

We propose a two-stage algorithm for the super-resolution of MR images from
their low-frequency k-space samples. In the first stage we estimate a
resolution-independent mask whose zeros represent the edges of the image. This
builds off recent work extending the theory of sampling signals of finite rate
of innovation (FRI) to two-dimensional curves. We enable its application to MRI
by proposing extensions of the signal models allowed by FRI theory, and by
developing a more robust and efficient means to determine the edge mask. In the
second stage of the scheme, we recover the super-resolved MR image using the
discretized edge mask as an image prior. We evaluate our scheme on simulated
single-coil MR data obtained from analytical phantoms, and compare against
total variation reconstructions. Our experiments show improved performance in
both noiseless and noisy settings.
Open System Categorical Quantum Semantics in Natural Language Processing
Authors: Robin Piedeleu, Dimitri Kartsaklis, Bob Coecke, Mehrnoosh Sadrzadeh
Date: 2015-02-03 00:00:00
Tasks: Sentence

Originally inspired by categorical quantum mechanics (Abramsky and Coecke,
LiCS'04), the categorical compositional distributional model of natural
language meaning of Coecke, Sadrzadeh and Clark provides a conceptually
motivated procedure to compute the meaning of a sentence, given its grammatical
structure within a Lambek pregroup and a vectorial representation of the
meaning of its parts. The predictions of this first model have outperformed
that of other models in mainstream empirical language processing tasks on large
scale data. Moreover, just like CQM allows for varying the model in which we
interpret quantum axioms, one can also vary the model in which we interpret
word meaning.
  In this paper we show that further developments in categorical quantum
mechanics are relevant to natural language processing too. Firstly, Selinger's
CPM-construction allows for explicitly taking into account lexical ambiguity
and distinguishing between the two inherently different notions of homonymy and
polysemy. In terms of the model in which we interpret word meaning, this means
a passage from the vector space model to density matrices. Despite this change
of model, standard empirical methods for comparing meanings can be easily
adopted, which we demonstrate by a small-scale experiment on real-world data.
This experiment moreover provides preliminary evidence of the validity of our
proposed new model for word meaning.
  Secondly, commutative classical structures as well as their non-commutative
counterparts that arise in the image of the CPM-construction allow for encoding
relative pronouns, verbs and adjectives, and finally, iteration of the
CPM-construction, something that has no counterpart in the quantum realm,
enables one to accommodate both entailment and ambiguity.
Authorship recognition via fluctuation analysis of network topology and word intermittency
Authors: Diego R. Amancio
Date: 2015-02-04 00:00:00

Statistical methods have been widely employed in many practical natural
language processing applications. More specifically, complex networks concepts
and methods from dynamical systems theory have been successfully applied to
recognize stylistic patterns in written texts. Despite the large amount of
studies devoted to represent texts with physical models, only a few studies
have assessed the relevance of attributes derived from the analysis of
stylistic fluctuations. Because fluctuations represent a pivotal factor for
characterizing a myriad of real systems, this study focused on the analysis of
the properties of stylistic fluctuations in texts via topological analysis of
complex networks and intermittency measurements. The results showed that
different authors display distinct fluctuation patterns. In particular, it was
found that it is possible to identify the authorship of books using the
intermittency of specific words. Taken together, the results described here
suggest that the patterns found in stylistic fluctuations could be used to
analyze other related complex systems. Furthermore, the discovery of novel
patterns related to textual stylistic fluctuations indicates that these
patterns could be useful to improve the state of the art of many
stylistic-based natural language processing tasks.