pwc-archive/papers-with-abstracts huggingface dataset:
Even the Abstract have Colour: Consensus in Word-Colour Associations
Authors: Saif M. Mohammad
Date: 2013-09-20 00:00:00
Tasks: Marketing

Colour is a key component in the successful dissemination of information.
Since many real-world concepts are associated with colour, for example danger
with red, linguistic information is often complemented with the use of
appropriate colours in information visualization and product marketing. Yet,
there is no comprehensive resource that captures concept-colour associations.
We present a method to create a large word-colour association lexicon by
crowdsourcing. A word-choice question was used to obtain sense-level
annotations and to ensure data quality. We focus especially on abstract
concepts and emotions to show that even they tend to have strong colour
associations. Thus, using the right colours can not only improve semantic
coherence, but also inspire the desired emotional response.
Recovering Non-negative and Combined Sparse Representations
Authors: Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Andreas Spanias
Date: 2013-03-12 00:00:00

The non-negative solution to an underdetermined linear system can be uniquely
recovered sometimes, even without imposing any additional sparsity constraints.
In this paper, we derive conditions under which a unique non-negative solution
for such a system can exist, based on the theory of polytopes. Furthermore, we
develop the paradigm of combined sparse representations, where only a part of
the coefficient vector is constrained to be non-negative, and the rest is
unconstrained (general). We analyze the recovery of the unique, sparsest
solution, for combined representations, under three different cases of
coefficient support knowledge: (a) the non-zero supports of non-negative and
general coefficients are known, (b) the non-zero support of general
coefficients alone is known, and (c) both the non-zero supports are unknown.
For case (c), we propose the combined orthogonal matching pursuit algorithm for
coefficient recovery and derive the deterministic sparsity threshold under
which recovery of the unique, sparsest coefficient vector is possible. We
quantify the order complexity of the algorithms, and examine their performance
in exact and approximate recovery of coefficients under various conditions of
noise. Furthermore, we also obtain their empirical phase transition
characteristics. We show that the basis pursuit algorithm, with partial
non-negative constraints, and the proposed greedy algorithm perform better in
recovering the unique sparse representation when compared to their
unconstrained counterparts. Finally, we demonstrate the utility of the proposed
methods in recovering images corrupted by saturation noise.
Recognizing Speech in a Novel Accent: The Motor Theory of Speech Perception Reframed
Authors: Clément Moulin-Frier, M. A. Arbib
Date: 2013-09-20 00:00:00

The motor theory of speech perception holds that we perceive the speech of
another in terms of a motor representation of that speech. However, when we
have learned to recognize a foreign accent, it seems plausible that recognition
of a word rarely involves reconstruction of the speech gestures of the speaker
rather than the listener. To better assess the motor theory and this
observation, we proceed in three stages. Part 1 places the motor theory of
speech perception in a larger framework based on our earlier models of the
adaptive formation of mirror neurons for grasping, and for viewing extensions
of that mirror system as part of a larger system for neuro-linguistic
processing, augmented by the present consideration of recognizing speech in a
novel accent. Part 2 then offers a novel computational model of how a listener
comes to understand the speech of someone speaking the listener's native
language with a foreign accent. The core tenet of the model is that the
listener uses hypotheses about the word the speaker is currently uttering to
update probabilities linking the sound produced by the speaker to phonemes in
the native language repertoire of the listener. This, on average, improves the
recognition of later words. This model is neutral regarding the nature of the
representations it uses (motor vs. auditory). It serve as a reference point for
the discussion in Part 3, which proposes a dual-stream neuro-linguistic
architecture to revisits claims for and against the motor theory of speech
perception and the relevance of mirror neurons, and extracts some implications
for the reframing of the motor theory.
A modeling approach to design a software sensor and analyze agronomical features - Application to sap flow and grape quality relationship
Authors: Aurélie Thébaut, Thibault Scholash, Brigitte Charnomordic, Nadine Hilgert
Date: 2013-09-20 00:00:00
Tasks: Experimental Design

This work proposes a framework using temporal data and domain knowledge in
order to analyze complex agronomical features. The expertise is first
formalized in an ontology, under the form of concepts and relationships between
them, and then used in conjunction with raw data and mathematical models to
design a software sensor. Next the software sensor outputs are put in relation
to product quality, assessed by quantitative measurements. This requires the
use of advanced data analysis methods, such as functional regression. The
methodology is applied to a case study involving an experimental design in
French vineyards. The temporal data consist of sap flow measurements, and the
goal is to explain fruit quality (sugar concentration and weight), using vine's
water courses through the various vine phenological stages. The results are
discussed, as well as the method genericity and robustness.
An introduction to the Europe Media Monitor family of applications
Authors: Ralf Steinberger, Bruno Pouliquen, Erik van der Goot
Date: 2013-09-20 00:00:00

Most large organizations have dedicated departments that monitor the media to
keep up-to-date with relevant developments and to keep an eye on how they are
represented in the news. Part of this media monitoring work can be automated.
In the European Union with its 23 official languages, it is particularly
important to cover media reports in many languages in order to capture the
complementary news content published in the different countries. It is also
important to be able to access the news content across languages and to merge
the extracted information. We present here the four publicly accessible systems
of the Europe Media Monitor (EMM) family of applications, which cover between
19 and 50 languages (see http://press.jrc.it/overview.html). We give an
overview of their functionality and discuss some of the implications of the
fact that they cover quite so many languages. We discuss design issues
necessary to be able to achieve this high multilinguality, as well as the
benefits of this multilinguality.
Scalable Anomaly Detection in Large Homogenous Populations
Authors: Henrik Ohlsson, Tianshi Chen, Sina Khoshfetrat Pakazad, Lennart Ljung, S. Shankar Sastry
Date: 2013-09-20 00:00:00
Tasks: Anomaly Detection, Combinatorial Optimization

Anomaly detection in large populations is a challenging but highly relevant
problem. The problem is essentially a multi-hypothesis problem, with a
hypothesis for every division of the systems into normal and anomal systems.
The number of hypothesis grows rapidly with the number of systems and
approximate solutions become a necessity for any problems of practical
interests. In the current paper we take an optimization approach to this
multi-hypothesis problem. We first observe that the problem is equivalent to a
non-convex combinatorial optimization problem. We then relax the problem to a
convex problem that can be solved distributively on the systems and that stays
computationally tractable as the number of systems increase. An interesting
property of the proposed method is that it can under certain conditions be
shown to give exactly the same result as the combinatorial multi-hypothesis
problem and the relaxation is hence tight.
DGT-TM: A freely Available Translation Memory in 22 Languages
Authors: Ralf Steinberger, Andreas Eisele, Szymon Klocek, Spyridon Pilos, Patrick Schlüter
Date: 2013-09-20 00:00:00
Proceeding: LREC 2012 5
Tasks: Clustering, General Classification, Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Translation

The European Commission's (EC) Directorate General for Translation, together
with the EC's Joint Research Centre, is making available a large translation
memory (TM; i.e. sentences and their professionally produced translations)
covering twenty-two official European Union (EU) languages and their 231
language pairs. Such a resource is typically used by translation professionals
in combination with TM software to improve speed and consistency of their
translations. However, this resource has also many uses for translation studies
and for language technology applications, including Statistical Machine
Translation (SMT), terminology extraction, Named Entity Recognition (NER),
multilingual classification and clustering, and many more. In this reference
paper for DGT-TM, we introduce this new resource, provide statistics regarding
its size, and explain how it was produced and how to use it.
JRC EuroVoc Indexer JEX - A freely available multi-label categorisation tool
Authors: Ralf Steinberger, Mohamed Ebrahim, Marco Turchi
Date: 2013-09-20 00:00:00
Proceeding: LREC 2012 5
Tasks: Classification, Clustering, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Retrieval, Sentence

EuroVoc (2012) is a highly multilingual thesaurus consisting of over 6,700
hierarchically organised subject domains used by European Institutions and many
authorities in Member States of the European Union (EU) for the classification
and retrieval of official documents. JEX is JRC-developed multi-label
classification software that learns from manually labelled data to
automatically assign EuroVoc descriptors to new documents in a profile-based
category-ranking task. The JEX release consists of trained classifiers for 22
official EU languages, of parallel training data in the same languages, of an
interface that allows viewing and amending the assignment results, and of a
module that allows users to re-train the tool on their own document
collections. JEX allows advanced users to change the document representation so
as to possibly improve the categorisation result through linguistic
pre-processing. JEX can be used as a tool for interactive EuroVoc descriptor
assignment to increase speed and consistency of the human categorisation
process, or it can be used fully automatically. The output of JEX is a
language-independent EuroVoc feature vector lending itself also as input to
various other Language Technology tasks, including cross-lingual clustering and
classification, cross-lingual plagiarism detection, sentence selection and
ranking, and more.
Maxout Networks
Authors: Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio
Date: 2013-02-18 00:00:00
Tasks: General Classification, Image Classification

We consider the problem of designing models to leverage a recently introduced
approximate model averaging technique called dropout. We define a simple new
model called maxout (so named because its output is the max of a set of inputs,
and because it is a natural companion to dropout) designed to both facilitate
optimization by dropout and improve the accuracy of dropout's fast approximate
model averaging technique. We empirically verify that the model successfully
accomplishes both of these tasks. We use maxout and dropout to demonstrate
state of the art classification performance on four benchmark datasets: MNIST,
CIFAR-10, CIFAR-100, and SVHN.
mTim: Rapid and accurate transcript reconstruction from RNA-Seq data
Authors: Georg Zeller, Nico Goernitz, Andre Kahles, Jonas Behr, Pramod Mudrakarta, Soeren Sonnenburg, Gunnar Raetsch
Date: 2013-09-20 00:00:00

Recent advances in high-throughput cDNA sequencing (RNA-Seq) technology have
revolutionized transcriptome studies. A major motivation for RNA-Seq is to map
the structure of expressed transcripts at nucleotide resolution. With accurate
computational tools for transcript reconstruction, this technology may also
become useful for genome (re-)annotation, which has mostly relied on de novo
gene finding where gene structures are primarily inferred from the genome
sequence. We developed a machine-learning method, called mTim (margin-based
transcript inference method) for transcript reconstruction from RNA-Seq read
alignments that is based on discriminatively trained hidden Markov support
vector machines. In addition to features derived from read alignments, it
utilizes characteristic genomic sequences, e.g. around splice sites, to improve
transcript predictions. mTim inferred transcripts that were highly accurate and
relatively robust to alignment errors in comparison to those from Cufflinks, a
widely used transcript assembly method.
A finite axiomatization of conditional independence and inclusion dependencies
Authors: Miika Hannula, Juha Kontinen
Date: 2013-09-19 00:00:00

We present a complete finite axiomatization of the unrestricted implication
problem for inclusion and conditional independence atoms in the context of
dependence logic. For databases, our result implies a finite axiomatization of
the unrestricted implication problem for inclusion, functional, and embedded
multivalued dependencies in the unirelational case.
Saying What You're Looking For: Linguistics Meets Video Search
Authors: Andrei Barbu, N. Siddharth, Jeffrey Mark Siskind
Date: 2013-09-20 00:00:00
Tasks: object-detection, Object Detection, Sentence

We present an approach to searching large video corpora for video clips which
depict a natural-language query in the form of a sentence. This approach uses
compositional semantics to encode subtle meaning that is lost in other systems,
such as the difference between two sentences which have identical words but
entirely different meaning: "The person rode the horse} vs. \emph{The horse
rode the person". Given a video-sentence pair and a natural-language parser,
along with a grammar that describes the space of sentential queries, we produce
a score which indicates how well the video depicts the sentence. We produce
such a score for each video clip in a corpus and return a ranked list of clips.
Furthermore, this approach addresses two fundamental problems simultaneously:
detecting and tracking objects, and recognizing whether those tracks depict the
query. Because both tracking and object detection are unreliable, this uses
knowledge about the intended sentential query to focus the tracker on the
relevant participants and ensures that the resulting tracks are described by
the sentential query. While earlier work was limited to single-word queries
which correspond to either verbs or nouns, we show how one can search for
complex queries which contain multiple phrases, such as prepositional phrases,
and modifiers, such as adverbs. We demonstrate this approach by searching for
141 queries involving people and horses interacting with each other in 10
full-length Hollywood movies.
An ant colony optimization algorithm for job shop scheduling problem
Authors: Edson Flórez, Wilfredo Gómez, Lola Bautista
Date: 2013-09-19 00:00:00
Tasks: Combinatorial Optimization, Job Shop Scheduling, Scheduling

The nature has inspired several metaheuristics, outstanding among these is
Ant Colony Optimization (ACO), which have proved to be very effective and
efficient in problems of high complexity (NP-hard) in combinatorial
optimization. This paper describes the implementation of an ACO model algorithm
known as Elitist Ant System (EAS), applied to a combinatorial optimization
problem called Job Shop Scheduling Problem (JSSP). We propose a method that
seeks to reduce delays designating the operation immediately available, but
considering the operations that lack little to be available and have a greater
amount of pheromone. The performance of the algorithm was evaluated for
problems of JSSP reference, comparing the quality of the solutions obtained
regarding the best known solution of the most effective methods. The solutions
were of good quality and obtained with a remarkable efficiency by having to
make a very low number of objective function evaluations.
A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics
Authors: Sean Whalen, Gaurav Pandey
Date: 2013-09-19 00:00:00
Tasks: Diversity, Meta-Learning

The combination of multiple classifiers using ensemble methods is
increasingly important for making progress in a variety of difficult prediction
problems. We present a comparative analysis of several ensemble methods through
two case studies in genomics, namely the prediction of genetic interactions and
protein functions, to demonstrate their efficacy on real-world datasets and
draw useful conclusions about their behavior. These methods include simple
aggregation, meta-learning, cluster-based meta-learning, and ensemble selection
using heterogeneous classifiers trained on resampled data to improve the
diversity of their predictions. We present a detailed analysis of these methods
across 4 genomics datasets and find the best of these methods offer
statistically significant improvements over the state of the art in their
respective domains. In addition, we establish a novel connection between
ensemble selection and meta-learning, demonstrating how both of these disparate
methods establish a balance between ensemble diversity and performance.
Semantic Advertising
Authors: Ben Zamanzadeh, Naveen Ashish, Cartic Ramakrishnan, John Zimmerman
Date: 2013-09-19 00:00:00

We present the concept of Semantic Advertising which we see as the future of
online advertising. Semantic Advertising is online advertising powered by
semantic technology which essentially enables us to represent and reason with
concepts and the meaning of things. This paper aims to 1) Define semantic
advertising, 2) Place it in the context of broader and more widely used
concepts such as the Semantic Web and Semantic Search, 3) Provide a survey of
work in related areas such as context matching, and 4) Provide a perspective on
successful emerging technologies and areas of future work. We base our work on
our experience as a company developing semantic technologies aimed at realizing
the full potential of online advertising.
Constructing Runge-Kutta Methods with the Use of Artificial Neural Networks
Authors: Angelos A. Anastassi
Date: 2011-06-06 00:00:00

A methodology that can generate the optimal coefficients of a numerical
method with the use of an artificial neural network is presented in this work.
The network can be designed to produce a finite difference algorithm that
solves a specific system of ordinary differential equations numerically. The
case we are examining here concerns an explicit two-stage Runge-Kutta method
for the numerical solution of the two-body problem. Following the
implementation of the network, the latter is trained to obtain the optimal
values for the coefficients of the Runge-Kutta method. The comparison of the
new method to others that are well known in the literature proves its
efficiency and demonstrates the capability of the network to provide efficient
algorithms for specific problems.
Blind Deconvolution via Maximum Kurtosis Adaptive Filtering
Authors: Deborah Pereg, Doron Benzvi
Date: 2013-09-19 00:00:00

In this paper, we present an algorithm for identifying a parametrically
described destructive unknown system based on a non-gaussianity measure. It is
known that under certain conditions the output of a linear system is more
gaussian than the input. Hence, an inverse filter is searched, such that its
output is minimally gaussian. We use the kurtosis as a measure of the
non-gaussianity of the signal. A maximum of the kurtosis as a function of the
deconvolving filter coefficients is searched. The search is done iteratively
using the gradient ascent algorithm, and the coefficients at the maximum point
correspond to the inverse filter coefficients. This filter may be applied to
the distorted signal to obtain the original undistorted signal. While a similar
approach has been used before, it was always directed at a particular kind of a
signal, commonly of impulsive characteristics. In this paper a successful
attempt has been made to apply the algorithm to a wider range of signals, such
as to process distorted audio signals and destructed images. This innovative
implementation required the revelation of a way to preprocess the distorted
signal at hand. The experimental results show very good performance in terms of
recovering audio signals and blurred images, both for an FIR and IIR distorting
filters.
HOL(y)Hammer: Online ATP Service for HOL Light
Authors: Cezary Kaliszyk, Josef Urban
Date: 2013-09-19 00:00:00
Tasks: Automated Theorem Proving, CPU

HOL(y)Hammer is an online AI/ATP service for formal (computer-understandable)
mathematics encoded in the HOL Light system. The service allows its users to
upload and automatically process an arbitrary formal development (project)
based on HOL Light, and to attack arbitrary conjectures that use the concepts
defined in some of the uploaded projects. For that, the service uses several
automated reasoning systems combined with several premise selection methods
trained on all the project proofs. The projects that are readily available on
the server for such query answering include the recent versions of the
Flyspeck, Multivariate Analysis and Complex Analysis libraries. The service
runs on a 48-CPU server, currently employing in parallel for each task 7 AI/ATP
combinations and 4 decision procedures that contribute to its overall
performance. The system is also available for local installation by interested
users, who can customize it for their own proof development. An Emacs interface
allowing parallel asynchronous queries to the service is also provided. The
overall structure of the service is outlined, problems that arise and their
solutions are discussed, and an initial account of using the system is given.
Exploration and Exploitation in Visuomotor Prediction of Autonomous Agents
Authors: Laurens Bliek
Date: 2013-09-19 00:00:00
Tasks: Prediction

This paper discusses various techniques to let an agent learn how to predict
the effects of its own actions on its sensor data autonomously, and their
usefulness to apply them to visual sensors. An Extreme Learning Machine is used
for visuomotor prediction, while various autonomous control techniques that can
aid the prediction process by balancing exploration and exploitation are
discussed and tested in a simple system: a camera moving over a 2D greyscale
image.
Optimal detection of sparse principal components in high dimension
Authors: Quentin Berthet, Philippe Rigollet
Date: 2012-02-23 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We perform a finite sample analysis of the detection levels for sparse
principal components of a high-dimensional covariance matrix. Our minimax
optimal test is based on a sparse eigenvalue statistic. Alas, computing this
test is known to be NP-complete in general, and we describe a computationally
efficient alternative test using convex relaxations. Our relaxation is also
proved to detect sparse principal components at near optimal detection levels,
and it performs well on simulated datasets. Moreover, using polynomial time
reductions from theoretical computer science, we bring significant evidence
that our results cannot be improved, thus revealing an inherent trade off
between statistical and computational performance.