pwc-archive/papers-with-abstracts huggingface dataset:
Should I Stay or Should I Go: Coordinating Biological Needs with Continuously-updated Assessments of the Environment
Authors: Liane Gabora
Date: 2013-09-25 00:00:00

This paper presents Wanderer, a model of how autonomous adaptive systems coordinate internal biological needs with moment-by-moment assessments of the probabilities of events in the external world. The extent to which Wanderer moves about or explores its environment reflects the relative activations of two competing motivational sub-systems: one represents the need to acquire energy and it excites exploration, and the other represents the need to avoid predators and it inhibits exploration. The environment contains food, predators, and neutral stimuli. Wanderer responds to these events in a way that is adaptive in the short turn, and reassesses the probabilities of these events so that it can modify its long term behaviour appropriately. When food appears, Wanderer be-comes satiated and exploration temporarily decreases. When a predator appears, Wanderer both decreases exploration in the short term, and becomes more "cautious" about exploring in the future. Wanderer also forms associations between neutral features and salient ones (food and predators) when they are present at the same time, and uses these associations to guide its behaviour.
Exploring Programmable Self-Assembly in Non-DNA based Molecular Computing
Authors: German Terrazas, Hector Zenil, Natalio Krasnogor
Date: 2013-09-25 00:00:00
Tasks: Clustering

Self-assembly is a phenomenon observed in nature at all scales where
autonomous entities build complex structures, without external influences nor
centralised master plan. Modelling such entities and programming correct
interactions among them is crucial for controlling the manufacture of desired
complex structures at the molecular and supramolecular scale. This work focuses
on a programmability model for non DNA-based molecules and complex behaviour
analysis of their self-assembled conformations. In particular, we look into
modelling, programming and simulation of porphyrin molecules self-assembly and
apply Kolgomorov complexity-based techniques to classify and assess simulation
results in terms of information content. The analysis focuses on phase
transition, clustering, variability and parameter discovery which as a whole
pave the way to the notion of complex systems programmability.
A Fuzzy expert system for goalkeeper quality recognition
Authors: Mohammad Bazmara, Shahram Jafari, Fatemeh Pasand
Date: 2013-09-25 00:00:00

Goalkeeper (GK) is an expert in soccer and goalkeeping is a complete
professional job. In fact, achieving success seems impossible without a
reliable GK. His effect in successes and failures is more dominant than other
players. The most visible mistakes in a game are those of goalkeeper's. In this
paper the expert fuzzy system is used as a suitable tool to study the quality
of a goalkeeper and compare it with others. Previously done researches are used
to find the goalkeepers' indexes in soccer. Soccer experts have found that a
successful GK should have some qualifications. A new pattern is offered here
which is called "Soccer goalkeeper quality recognition using fuzzy expert
systems". This pattern has some important capabilities. Firstly, among some
goalkeepers the one with the best quality for the main team arrange can be
chosen. Secondly, the need to expert coaches for choosing a GK using their
senses and experiences decreases a lot. Thirdly, in the survey of a GK,
quantitative criteria can be included, and finally this pattern is simple and
easy to understand.
Multiple-object tracking in cluttered and crowded public spaces
Authors: Rhys Martin, Ognjen Arandjelović
Date: 2013-09-25 00:00:00
Tasks: Motion Detection, Multiple Object Tracking, Object Tracking

This paper addresses the problem of tracking moving objects of variable
appearance in challenging scenes rich with features and texture. Reliable
tracking is of pivotal importance in surveillance applications. It is made
particularly difficult by the nature of objects encountered in such scenes:
these too change in appearance and scale, and are often articulated (e.g.
humans). We propose a method which uses fast motion detection and segmentation
as a constraint for both building appearance models and their robust
propagation (matching) in time. The appearance model is based on sets of local
appearances automatically clustered using spatio-kinetic similarity, and is
updated with each new appearance seen. This integration of all seen appearances
of a tracked object makes it extremely resilient to errors caused by occlusion
and the lack of permanence of due to low data quality, appearance change or
background clutter. These theoretical strengths of our algorithm are
empirically demonstrated on two hour long video footage of a busy city
marketplace.
Contextually learnt detection of unusual motion-based behaviour in crowded public spaces
Authors: Ognjen Arandjelović
Date: 2013-09-25 00:00:00
Tasks: Clustering

In this paper we are interested in analyzing behaviour in crowded public
places at the level of holistic motion. Our aim is to learn, without user
input, strong scene priors or labelled data, the scope of "normal behaviour"
for a particular scene and thus alert to novelty in unseen footage. The first
contribution is a low-level motion model based on what we term tracklet
primitives, which are scene-specific elementary motions. We propose a
clustering-based algorithm for tracklet estimation from local approximations to
tracks of appearance features. This is followed by two methods for motion
novelty inference from tracklet primitives: (a) we describe an approach based
on a non-hierarchial ensemble of Markov chains as a means of capturing
behavioural characteristics at different scales, and (b) a more flexible
alternative which exhibits a higher generalizing power by accounting for
constraints introduced by intentionality and goal-oriented planning of human
motion in a particular scene. Evaluated on a 2h long video of a busy city
marketplace, both algorithms are shown to be successful at inferring unusual
behaviour, the latter model achieving better performance for novelties at a
larger spatial scale.
Diffeomorphic Metric Mapping and Probabilistic Atlas Generation of Hybrid Diffusion Imaging based on BFOR Signal Basis
Authors: Jia Du, A. Pasha Hosseinbor, Moo. K. Chung, Barbara B. Bendlin, Gaurav Suryawanshi, Andrew L. Alexander, Anqi Qiu
Date: 2013-09-25 00:00:00

We propose a large deformation diffeomorphic metric mapping algorithm to
align multiple b-value diffusion weighted imaging (mDWI) data, specifically
acquired via hybrid diffusion imaging (HYDI), denoted as LDDMM-HYDI. We then
propose a Bayesian model for estimating the white matter atlas from HYDIs. We
adopt the work given in Hosseinbor et al. (2012) and represent the q-space
diffusion signal with the Bessel Fourier orientation reconstruction (BFOR)
signal basis. The BFOR framework provides the representation of mDWI in the
q-space and thus reduces memory requirement. In addition, since the BFOR signal
basis is orthonormal, the L2 norm that quantifies the differences in the
q-space signals of any two mDWI datasets can be easily computed as the sum of
the squared differences in the BFOR expansion coefficients. In this work, we
show that the reorientation of the $q$-space signal due to spatial
transformation can be easily defined on the BFOR signal basis. We incorporate
the BFOR signal basis into the LDDMM framework and derive the gradient descent
algorithm for LDDMM-HYDI with explicit orientation optimization. Additionally,
we extend the previous Bayesian atlas estimation framework for scalar-valued
images to HYDIs and derive the expectation-maximization algorithm for solving
the HYDI atlas estimation problem. Using real HYDI datasets, we show the
Bayesian model generates the white matter atlas with anatomical details.
Moreover, we show that it is important to consider the variation of mDWI
reorientation due to a small change in diffeomorphic transformation in the
LDDMM-HYDI optimization and to incorporate the full information of HYDI for
aligning mDWI.
Using Nuances of Emotion to Identify Personality
Authors: Saif M. Mohammad, Svetlana Kiritchenko
Date: 2013-09-24 00:00:00
Tasks: Specificity

Past work on personality detection has shown that frequency of lexical
categories such as first person pronouns, past tense verbs, and sentiment words
have significant correlations with personality traits. In this paper, for the
first time, we show that fine affect (emotion) categories such as that of
excitement, guilt, yearning, and admiration are significant indicators of
personality. Additionally, we perform experiments to show that the gains
provided by the fine affect categories are not obtained by using coarse affect
categories alone or with specificity features alone. We employ these features
in five SVM classifiers for detecting five personality traits through essays.
We find that the use of fine emotion features leads to statistically
significant improvement over a competitive baseline, whereas the use of coarse
affect and specificity features does not.
Tracking Sentiment in Mail: How Genders Differ on Emotional Axes
Authors: Saif M. Mohammad, Tony, Yang
Date: 2013-09-24 00:00:00
Tasks: Sentiment Analysis

With the widespread use of email, we now have access to unprecedented amounts
of text that we ourselves have written. In this paper, we show how sentiment
analysis can be used in tandem with effective visualizations to quantify and
track emotions in many types of mail. We create a large word--emotion
association lexicon by crowdsourcing, and use it to compare emotions in love
letters, hate mail, and suicide notes. We show that there are marked
differences across genders in how they use emotion words in work-place email.
For example, women use many words from the joy--sadness axis, whereas men
prefer terms from the fear--trust axis. Finally, we show visualizations that
can help people track emotions in their emails.
Generating Explanations for Biomedical Queries
Authors: Esra Erdem, Umut Oztok
Date: 2013-09-24 00:00:00
Tasks: Drug Discovery

We introduce novel mathematical models and algorithms to generate (shortest
or k different) explanations for biomedical queries, using answer set
programming. We implement these algorithms and integrate them in BIOQUERY-ASP.
We illustrate the usefulness of these methods with some complex biomedical
queries related to drug discovery, over the biomedical knowledge resources
PHARMGKB, DRUGBANK, BIOGRID, CTD, SIDER, DISEASE ONTOLOGY and ORPHADATA. To
appear in Theory and Practice of Logic Programming (TPLP).
Sentiment Analysis in the News
Authors: Alexandra Balahur, Ralf Steinberger, Mijail Kabadjov, Vanni Zavarella, Erik van der Goot, Matina Halkia, Bruno Pouliquen, Jenya Belyaeva
Date: 2013-09-24 00:00:00
Tasks: Articles, Opinion Mining, Sentiment Analysis, World Knowledge

Recent years have brought a significant growth in the volume of research in
sentiment analysis, mostly on highly subjective text types (movie or product
reviews). The main difference these texts have with news articles is that their
target is clearly defined and unique across the text. Following different
annotation efforts and the analysis of the issues encountered, we realised that
news opinion mining is different from that of other text types. We identified
three subtasks that need to be addressed: definition of the target; separation
of the good and bad news content from the good and bad sentiment expressed on
the target; and analysis of clearly marked opinion that is expressed
explicitly, not needing interpretation or the use of world knowledge.
Furthermore, we distinguish three different possible views on newspaper
articles - author, reader and text, which have to be addressed differently at
the time of analysing sentiment. Given these definitions, we present work on
mining opinions about entities in English language news, in which (a) we test
the relative suitability of various sentiment dictionaries and (b) we attempt
to separate positive or negative opinion from good or bad news. In the
experiments described here, we tested whether or not subject domain-defining
vocabulary should be ignored. Results showed that this idea is more appropriate
in the context of news opinion mining and that the approaches taking this into
consideration produce a better performance.
Acronym recognition and processing in 22 languages
Authors: Maud Ehrmann, Leonida della Rocca, Ralf Steinberger, Hristo Tanev
Date: 2013-09-24 00:00:00
Proceeding: RANLP 2013 9
Tasks: Articles, Form

We are presenting work on recognising acronyms of the form Long-Form
(Short-Form) such as "International Monetary Fund (IMF)" in millions of news
articles in twenty-two languages, as part of our more general effort to
recognise entities and their variants in news text and to use them for the
automatic analysis of the news, including the linking of related news across
languages. We show how the acronym recognition patterns, initially developed
for medical terms, needed to be adapted to the more general news domain and we
present evaluation results. We describe our effort to automatically merge the
numerous long-form variants referring to the same short-form, while keeping
non-related long-forms separate. Finally, we provide extensive statistics on
the frequency and the distribution of short-form/long-form pairs across
languages.
JRC-Names: A freely available, highly multilingual named entity resource
Authors: Ralf Steinberger, Bruno Pouliquen, Mijail Kabadjov, Erik van der Goot
Date: 2013-09-24 00:00:00
Tasks: Machine Translation, Morphological Inflection, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Translation

This paper describes a new, freely available, highly multilingual named
entity resource for person and organisation names that has been compiled over
seven years of large-scale multilingual news analysis combined with Wikipedia
mining, resulting in 205,000 per-son and organisation names plus about the same
number of spelling variants written in over 20 different scripts and in many
more languages. This resource, produced as part of the Europe Media Monitor
activity (EMM, http://emm.newsbrief.eu/overview.html), can be used for a number
of purposes. These include improving name search in databases or on the
internet, seeding machine learning systems to learn named entity recognition
rules, improve machine translation results, and more. We describe here how this
resource was created; we give statistics on its current size; we address the
issue of morphological inflection; and we give details regarding its
functionality. Updates to this resource will be made available daily.
Random Forests on Distance Matrices for Imaging Genetics Studies
Authors: Aaron Sim, Dimosthenis Tsagkrasoulis, Giovanni Montana
Date: 2013-09-24 00:00:00
Tasks: regression

We propose a non-parametric regression methodology, Random Forests on
Distance Matrices (RFDM), for detecting genetic variants associated to
quantitative phenotypes representing the human brain's structure or function,
and obtained using neuroimaging techniques. RFDM, which is an extension of
decision forests, requires a distance matrix as response that encodes all
pair-wise phenotypic distances in the random sample. We discuss ways to learn
such distances directly from the data using manifold learning techniques, and
how to define such distances when the phenotypes are non-vectorial objects such
as brain connectivity networks. We also describe an extension of RFDM to detect
espistatic effects while keeping the computational complexity low. Extensive
simulation results and an application to an imaging genetics study of
Alzheimer's Disease are presented and discussed.
Partition-Merge: Distributed Inference and Modularity Optimization
Authors: Vincent Blondel, Kyomin Jung, Pushmeet Kohli, Devavrat Shah
Date: 2013-09-24 00:00:00
Tasks: Community Detection

This paper presents a novel meta algorithm, Partition-Merge (PM), which takes
existing centralized algorithms for graph computation and makes them
distributed and faster. In a nutshell, PM divides the graph into small
subgraphs using our novel randomized partitioning scheme, runs the centralized
algorithm on each partition separately, and then stitches the resulting
solutions to produce a global solution. We demonstrate the efficiency of the PM
algorithm on two popular problems: computation of Maximum A Posteriori (MAP)
assignment in an arbitrary pairwise Markov Random Field (MRF), and modularity
optimization for community detection. We show that the resulting distributed
algorithms for these problems essentially run in time linear in the number of
nodes in the graph, and perform as well -- or even better -- than the original
centralized algorithm as long as the graph has geometric structures. Here we
say a graph has geometric structures, or polynomial growth property, when the
number of nodes within distance r of any given node grows no faster than a
polynomial function of r. More precisely, if the centralized algorithm is a
C-factor approximation with constant C \ge 1, the resulting distributed
algorithm is a (C+\delta)-factor approximation for any small \delta>0; but if
the centralized algorithm is a non-constant (e.g. logarithmic) factor
approximation, then the resulting distributed algorithm becomes a constant
factor approximation. For general graphs, we compute explicit bounds on the
loss of performance of the resulting distributed algorithm with respect to the
centralized algorithm.
Non-negative Matrix Factorization with Linear Constraints for Single-Channel Speech Enhancement
Authors: Nikolay Lyubimov, Mikhail Kotov
Date: 2013-09-24 00:00:00
Tasks: Speech Enhancement

This paper investigates a non-negative matrix factorization (NMF)-based
approach to the semi-supervised single-channel speech enhancement problem where
only non-stationary additive noise signals are given. The proposed method
relies on sinusoidal model of speech production which is integrated inside NMF
framework using linear constraints on dictionary atoms. This method is further
developed to regularize harmonic amplitudes. Simple multiplicative algorithms
are presented. The experimental evaluation was made on TIMIT corpus mixed with
various types of noise. It has been shown that the proposed method outperforms
some of the state-of-the-art noise suppression techniques in terms of
signal-to-noise ratio.
A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion
Authors: T. Tony Cai, Wen-Xin Zhou
Date: 2013-09-24 00:00:00
Tasks: Matrix Completion

We consider in this paper the problem of noisy 1-bit matrix completion under
a general non-uniform sampling distribution using the max-norm as a convex
relaxation for the rank. A max-norm constrained maximum likelihood estimate is
introduced and studied. The rate of convergence for the estimate is obtained.
Information-theoretical methods are used to establish a minimax lower bound
under the general sampling model. The minimax upper and lower bounds together
yield the optimal rate of convergence for the Frobenius norm loss.
Computational algorithms and numerical performance are also discussed.
An evolutionary approach to Function
Authors: Phillip Lord
Date: 2013-09-23 00:00:00

Background: Understanding the distinction between function and role is vexing
and difficult. While it appears to be useful, in practice this distinction is
hard to apply, particularly within biology.
  Results: I take an evolutionary approach, considering a series of examples,
to develop and generate definitions for these concepts. I test them in practice
against the Ontology for Biomedical Investigations (OBI). Finally, I give an
axiomatisation and discuss methods for applying these definitions in practice.
  Conclusions: The definitions in this paper are applicable, formalizing
current practice. As such, they make a significant contribution to the use of
these concepts within biomedical ontologies.
Asymptotic Analysis of LASSOs Solution Path with Implications for Approximate Message Passing
Authors: Ali Mousavi, Arian Maleki, Richard G. Baraniuk
Date: 2013-09-23 00:00:00
Tasks: Denoising

This paper concerns the performance of the LASSO (also knows as basis pursuit
denoising) for recovering sparse signals from undersampled, randomized, noisy
measurements. We consider the recovery of the signal $x_o \in \mathbb{R}^N$
from $n$ random and noisy linear observations $y= Ax_o + w$, where $A$ is the
measurement matrix and $w$ is the noise. The LASSO estimate is given by the
solution to the optimization problem $x_o$ with $\hat{x}_{\lambda} = \arg
\min_x \frac{1}{2} \|y-Ax\|_2^2 + \lambda \|x\|_1$. Despite major progress in
the theoretical analysis of the LASSO solution, little is known about its
behavior as a function of the regularization parameter $\lambda$. In this paper
we study two questions in the asymptotic setting (i.e., where $N \rightarrow
\infty$, $n \rightarrow \infty$ while the ratio $n/N$ converges to a fixed
number in $(0,1)$): (i) How does the size of the active set
$\|\hat{x}_\lambda\|_0/N$ behave as a function of $\lambda$, and (ii) How does
the mean square error $\|\hat{x}_{\lambda} - x_o\|_2^2/N$ behave as a function
of $\lambda$? We then employ these results in a new, reliable algorithm for
solving LASSO based on approximate message passing (AMP).
Efficient Sampling from Time-Varying Log-Concave Distributions
Authors: Hariharan Narayanan, Alexander Rakhlin
Date: 2013-09-23 00:00:00

We propose a computationally efficient random walk on a convex body which
rapidly mixes and closely tracks a time-varying log-concave distribution. We
develop general theoretical guarantees on the required number of steps; this
number can be calculated on the fly according to the distance from and the
shape of the next distribution. We then illustrate the technique on several
examples. Within the context of exponential families, the proposed method
produces samples from a posterior distribution which is updated as data arrive
in a streaming fashion. The sampling technique can be used to track
time-varying truncated distributions, as well as to obtain samples from a
changing mixture model, fitted in a streaming fashion to data. In the setting
of linear optimization, the proposed method has oracle complexity with best
known dependence on the dimension for certain geometries. In the context of
online learning and repeated games, the algorithm is an efficient method for
implementing no-regret mixture forecasting strategies. Remarkably, in some of
these examples, only one step of the random walk is needed to track the next
distribution.
Data Mining using Unguided Symbolic Regression on a Blast Furnace Dataset
Authors: Michael Kommenda, Gabriel Kronberger, Christoph Feilmayr, Michael Affenzeller
Date: 2013-09-23 00:00:00
Tasks: Implicit Relations, regression, Symbolic Regression, Variable Selection

In this paper a data mining approach for variable selection and knowledge
extraction from datasets is presented. The approach is based on unguided
symbolic regression (every variable present in the dataset is treated as the
target variable in multiple regression runs) and a novel variable relevance
metric for genetic programming. The relevance of each input variable is
calculated and a model approximating the target variable is created. The
genetic programming configurations with different target variables are executed
multiple times to reduce stochastic effects and the aggregated results are
displayed as a variable interaction network. This interaction network
highlights important system components and implicit relations between the
variables. The whole approach is tested on a blast furnace dataset, because of
the complexity of the blast furnace and the many interrelations between the
variables. Finally the achieved results are discussed with respect to existing
knowledge about the blast furnace process.