pwc-archive/papers-with-abstracts huggingface dataset:
Towards Active Logic Programming
Authors: Stefania Costantini
Date: 2014-03-21 00:00:00

In this paper we present the new logic programming language DALI, aimed at
defining agents and agent systems. A main design objective for DALI has been
that of introducing in a declarative fashion all the essential features, while
keeping the language as close as possible to the syntax and semantics of the
plain Horn--clause language. Special atoms and rules have been introduced, for
representing: external events, to which the agent is able to respond
(reactivity); actions (reactivity and proactivity); internal events (previous
conclusions which can trigger further activity); past and present events (to be
aware of what has happened). An extended resolution is provided, so that a DALI
agent is able to answer queries like in the plain Horn--clause language, but is
also able to cope with the different kinds of events, and exhibit a (rational)
reactive and proactive behaviour.
Missing Data Prediction and Classification: The Use of Auto-Associative Neural Networks and Optimization Algorithms
Authors: Collins Leke, Bhekisipho Twala, T. Marwala
Date: 2014-03-21 00:00:00
Tasks: General Classification

This paper presents methods which are aimed at finding approximations to
missing data in a dataset by using optimization algorithms to optimize the
network parameters after which prediction and classification tasks can be
performed. The optimization methods that are considered are genetic algorithm
(GA), simulated annealing (SA), particle swarm optimization (PSO), random
forest (RF) and negative selection (NS) and these methods are individually used
in combination with auto-associative neural networks (AANN) for missing data
estimation and the results obtained are compared. The methods suggested use the
optimization algorithms to minimize an error function derived from training the
auto-associative neural network during which the interrelationships between the
inputs and the outputs are obtained and stored in the weights connecting the
different layers of the network. The error function is expressed as the square
of the difference between the actual observations and predicted values from an
auto-associative neural network. In the event of missing data, all the values
of the actual observations are not known hence, the error function is
decomposed to depend on the known and unknown variable values. Multi-layer
perceptron (MLP) neural network is employed to train the neural networks using
the scaled conjugate gradient (SCG) method. Prediction accuracy is determined
by mean squared error (MSE), root mean squared error (RMSE), mean absolute
error (MAE), and correlation coefficient (r) computations. Accuracy in
classification is obtained by plotting ROC curves and calculating the areas
under these. Analysis of results depicts that the approach using RF with AANN
produces the most accurate predictions and classifications while on the other
end of the scale is the approach which entails using NS with AANN.
Adaptive piecewise polynomial estimation via trend filtering
Authors: Ryan J. Tibshirani
Date: 2013-04-10 00:00:00
Tasks: regression

We study trend filtering, a recently proposed tool of Kim et al. [SIAM Rev.
51 (2009) 339-360] for nonparametric regression. The trend filtering estimate
is defined as the minimizer of a penalized least squares criterion, in which
the penalty term sums the absolute $k$th order discrete derivatives over the
input points. Perhaps not surprisingly, trend filtering estimates appear to
have the structure of $k$th degree spline functions, with adaptively chosen
knot points (we say ``appear'' here as trend filtering estimates are not really
functions over continuous domains, and are only defined over the discrete set
of inputs). This brings to mind comparisons to other nonparametric regression
tools that also produce adaptive splines; in particular, we compare trend
filtering to smoothing splines, which penalize the sum of squared derivatives
across input points, and to locally adaptive regression splines [Ann. Statist.
25 (1997) 387-413], which penalize the total variation of the $k$th derivative.
Empirically, we discover that trend filtering estimates adapt to the local
level of smoothness much better than smoothing splines, and further, they
exhibit a remarkable similarity to locally adaptive regression splines. We also
provide theoretical support for these empirical findings; most notably, we
prove that (with the right choice of tuning parameter) the trend filtering
estimate converges to the true underlying function at the minimax rate for
functions whose $k$th derivative is of bounded variation. This is done via an
asymptotic pairing of trend filtering and locally adaptive regression splines,
which have already been shown to converge at the minimax rate [Ann. Statist. 25
(1997) 387-413]. At the core of this argument is a new result tying together
the fitted values of two lasso problems that share the same outcome vector, but
have different predictor matrices.
The quasispecies regime for the simple genetic algorithm with ranking selection
Authors: Raphaël Cerf
Date: 2014-03-21 00:00:00

We study the simple genetic algorithm with a ranking selection mechanism
(linear ranking or tournament). We denote by $\ell$ the length of the
chromosomes, by $m$ the population size, by $p_C$ the crossover probability and
by $p_M$ the mutation probability. We introduce a parameter $\sigma$, called
the selection drift, which measures the selection intensity of the fittest
chromosome. We show that the dynamics of the genetic algorithm depend in a
critical way on the parameter $$\pi \,=\,\sigma(1-p_C)(1-p_M)^\ell\,.$$ If
$\pi<1$, then the genetic algorithm operates in a disordered regime: an
advantageous mutant disappears with probability larger than $1-1/m^\beta$,
where $\beta$ is a positive exponent. If $\pi>1$, then the genetic algorithm
operates in a quasispecies regime: an advantageous mutant invades a positive
fraction of the population with probability larger than a constant $p^*$ (which
does not depend on $m$). We estimate next the probability of the occurrence of
a catastrophe (the whole population falls below a fitness level which was
previously reached by a positive fraction of the population). The asymptotic
results suggest the following rules: $\pi=\sigma(1-p_C)(1-p_M)^\ell$ should be
slightly larger than $1$; $p_M$ should be of order $1/\ell$; $m$ should be
larger than $\ell\ln\ell$; the running time should be of exponential order in
$m$. The first condition requires that $ \ell p_M +p_C< \ln\sigma$. These
conclusions must be taken with great care: they come from an asymptotic regime,
and it is a formidable task to understand the relevance of this regime for a
real-world problem. At least, we hope that these conclusions provide
interesting guidelines for the practical implementation of the simple genetic
algorithm.
Model-Driven Applications of Fractional Derivatives and Integrals
Authors: William A. Sethares, Selçuk Ş. Bayın
Date: 2014-03-21 00:00:00
Tasks: model

Fractional order derivatives and integrals (differintegrals) are viewed from
a frequency-domain perspective using the formalism of Riesz, providing a
computational tool as well as a way to interpret the operations in the
frequency domain. Differintegrals provide a logical extension of current
techniques, generalizing the notion of integral and differential operators and
acting as kind of frequency-domain filtering that has many of the advantages of
a nonlocal linear operator. Several important properties of differintegrals are
presented, and sample applications are given to one- and two-dimensional
signals. Computer code to carry out the computations is made available on the
author's website.
Computing Entropy Rate Of Symbol Sources & A Distribution-free Limit Theorem
Authors: Ishanu Chattopadhyay, Hod Lipson
Date: 2014-01-03 00:00:00

Entropy rate of sequential data-streams naturally quantifies the complexity
of the generative process. Thus entropy rate fluctuations could be used as a
tool to recognize dynamical perturbations in signal sources, and could
potentially be carried out without explicit background noise characterization.
However, state of the art algorithms to estimate the entropy rate have markedly
slow convergence; making such entropic approaches non-viable in practice. We
present here a fundamentally new approach to estimate entropy rates, which is
demonstrated to converge significantly faster in terms of input data lengths,
and is shown to be effective in diverse applications ranging from the
estimation of the entropy rate of English texts to the estimation of complexity
of chaotic dynamical systems. Additionally, the convergence rate of entropy
estimates do not follow from any standard limit theorem, and reported
algorithms fail to provide any confidence bounds on the computed values.
Exploiting a connection to the theory of probabilistic automata, we establish a
convergence rate of $O(\log \vert s \vert/\sqrt[3]{\vert s \vert})$ as a
function of the input length $\vert s \vert$, which then yields explicit
uncertainty estimates, as well as required data lengths to satisfy
pre-specified confidence bounds.
Using n-grams models for visual semantic place recognition
Authors: Mathieu Dubois, Frenoux Emmanuelle, Philippe Tarroux
Date: 2014-03-21 00:00:00
Tasks: Visual Place Recognition

The aim of this paper is to present a new method for visual place
recognition. Our system combines global image characterization and visual
words, which allows to use efficient Bayesian filtering methods to integrate
several images. More precisely, we extend the classical HMM model with
techniques inspired by the field of Natural Language Processing. This paper
presents our system and the Bayesian filtering algorithm. The performance of
our system and the influence of the main parameters are evaluated on a standard
database. The discussion highlights the interest of using such models and
proposes improvements.
An efficiency dependency parser using hybrid approach for tamil language
Authors: K. Sureka, K. G. Srinivasagan, S. Suganthi
Date: 2014-03-21 00:00:00
Tasks: Machine Translation, Sentence, Translation

Natural language processing is a prompt research area across the country.
Parsing is one of the very crucial tool in language analysis system which aims
to forecast the structural relationship among the words in a given sentence.
Many researchers have already developed so many language tools but the accuracy
is not meet out the human expectation level, thus the research is still exists.
Machine translation is one of the major application area under Natural Language
Processing. While translation between one language to another language, the
structure identification of a sentence play a key role. This paper introduces
the hybrid way to solve the identification of relationship among the given
words in a sentence. In existing system is implemented using rule based
approach, which is not suited in huge amount of data. The machine learning
approaches is suitable for handle larger amount of data and also to get better
accuracy via learning and training the system. The proposed approach takes a
Tamil sentence as an input and produce the result of a dependency relation as a
tree like structure using hybrid approach. This proposed tool is very helpful
for researchers and act as an odd-on improve the quality of existing
approaches.
Asymptotically Exact, Embarrassingly Parallel MCMC
Authors: Willie Neiswanger, Chong Wang, Eric Xing
Date: 2013-11-19 00:00:00

Communication costs, resulting from synchronization requirements during
learning, can greatly slow down many parallel machine learning algorithms. In
this paper, we present a parallel Markov chain Monte Carlo (MCMC) algorithm in
which subsets of data are processed independently, with very little
communication. First, we arbitrarily partition data onto multiple machines.
Then, on each machine, any classical MCMC method (e.g., Gibbs sampling) may be
used to draw samples from a posterior distribution given the data subset.
Finally, the samples from each machine are combined to form samples from the
full posterior. This embarrassingly parallel algorithm allows each machine to
act independently on a subset of the data (without communication) until the
final combination stage. We prove that our algorithm generates asymptotically
exact samples and empirically demonstrate its ability to parallelize burn-in
and sampling in several models.
SMML estimators for exponential families with continuous sufficient statistics
Authors: James G. Dowty
Date: 2013-02-04 00:00:00
Tasks: Data Compression

The minimum message length principle is an information theoretic criterion
that links data compression with statistical inference. This paper studies the
strict minimum message length (SMML) estimator for $d$-dimensional exponential
families with continuous sufficient statistics, for all $d \ge 1$. The
partition of an SMML estimator is shown to consist of convex polytopes (i.e.
convex polygons when $d=2$) which can be described explicitly in terms of the
assertions and coding probabilities. While this result is known, we give a new
proof based on the calculus of variations, and this approach gives some
interesting new inequalities for SMML estimators. We also use this result to
construct an SMML estimator for a $2$-dimensional normal random variable with
known variance and a normal prior on its mean.
A Physarum-Inspired Approach to Optimal Supply Chain Network Design at Minimum Total Cost with Demand Satisfaction
Authors: Xiaoge Zhang, Andrew Adamatzky, Xin-She Yang, Hai Yang, Sankaran Mahadevan, Yong Deng
Date: 2014-03-21 00:00:00

A supply chain is a system which moves products from a supplier to customers.
The supply chains are ubiquitous. They play a key role in all economic
activities. Inspired by biological principles of nutrients' distribution in
protoplasmic networks of slime mould Physarum polycephalum we propose a novel
algorithm for a supply chain design. The algorithm handles the supply networks
where capacity investments and product flows are variables. The networks are
constrained by a need to satisfy product demands. Two features of the slime
mould are adopted in our algorithm. The first is the continuity of a flux
during the iterative process, which is used in real-time update of the costs
associated with the supply links. The second feature is adaptivity. The supply
chain can converge to an equilibrium state when costs are changed. Practicality
and flexibility of our algorithm is illustrated on numerical examples.
Online Local Learning via Semidefinite Programming
Authors: Paul Christiano
Date: 2014-03-20 00:00:00
Tasks: Clustering, global-optimization

In many online learning problems we are interested in predicting local
information about some universe of items. For example, we may want to know
whether two items are in the same cluster rather than computing an assignment
of items to clusters; we may want to know which of two teams will win a game
rather than computing a ranking of teams. Although finding the optimal
clustering or ranking is typically intractable, it may be possible to predict
the relationships between items as well as if you could solve the global
optimization problem exactly.
  Formally, we consider an online learning problem in which a learner
repeatedly guesses a pair of labels (l(x), l(y)) and receives an adversarial
payoff depending on those labels. The learner's goal is to receive a payoff
nearly as good as the best fixed labeling of the items. We show that a simple
algorithm based on semidefinite programming can obtain asymptotically optimal
regret in the case where the number of possible labels is O(1), resolving an
open problem posed by Hazan, Kale, and Shalev-Schwartz. Our main technical
contribution is a novel use and analysis of the log determinant regularizer,
exploiting the observation that log det(A + I) upper bounds the entropy of any
distribution with covariance matrix A.
Review of Face Detection Systems Based Artificial Neural Networks Algorithms
Authors: Omaima N. A. AL-Allaf
Date: 2014-03-20 00:00:00
Tasks: Face Detection

Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.
Smoothing Dynamic Systems with State-Dependent Covariance Matrices
Authors: Aleksandr Y. Aravkin, James V. Burke
Date: 2012-11-19 00:00:00
Tasks: Computational Efficiency

Kalman filtering and smoothing algorithms are used in many areas, including
tracking and navigation, medical applications, and financial trend filtering.
One of the basic assumptions required to apply the Kalman smoothing framework
is that error covariance matrices are known and given. In this paper, we study
a general class of inference problems where covariance matrices can depend
functionally on unknown parameters. In the Kalman framework, this allows
modeling situations where covariance matrices may depend functionally on the
state sequence being estimated. We present an extended formulation and
generalized Gauss-Newton (GGN) algorithm for inference in this context. When
applied to dynamic systems inference, we show the algorithm can be implemented
to preserve the computational efficiency of the classic Kalman smoother. The
new approach is illustrated with a synthetic numerical example.
Sparse Learning over Infinite Subgraph Features
Authors: Ichigaku Takigawa, Hiroshi Mamitsuka
Date: 2014-03-20 00:00:00
Tasks: All, Sparse Learning

We present a supervised-learning algorithm from graph data (a set of graphs)
for arbitrary twice-differentiable loss functions and sparse linear models over
all possible subgraph features. To date, it has been shown that under all
possible subgraph features, several types of sparse learning, such as Adaboost,
LPBoost, LARS/LASSO, and sparse PLS regression, can be performed. Particularly
emphasis is placed on simultaneous learning of relevant features from an
infinite set of candidates. We first generalize techniques used in all these
preceding studies to derive an unifying bounding technique for arbitrary
separable functions. We then carefully use this bounding to make block
coordinate gradient descent feasible over infinite subgraph features, resulting
in a fast converging algorithm that can solve a wider class of sparse learning
problems over graph data. We also empirically study the differences from the
existing approaches in convergence property, selected subgraph features, and
search-space sizes. We further discuss several unnoticed issues in sparse
learning over all possible subgraph features.
Defuzzify firstly or finally: Dose it matter in fuzzy DEMATEL under uncertain environment?
Authors: Yunpeng Li, Ya Li, Jie Liu, Yong Deng
Date: 2014-03-20 00:00:00
Tasks: Decision Making

Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is widely
used in many real applications. With the desirable property of efficient
handling with the uncertain information in decision making, the fuzzy DEMATEL
is heavily studied. Recently, Dytczak and Ginda suggested to defuzzify the
fuzzy numbers firstly and then use the classical DEMATEL to obtain the final
result. In this short paper, we show that it is not reasonable in some
situations. The results of defuzzification at the first step are not coincide
with the results of defuzzification at the final step.It seems that the
alternative is to defuzzification in the final step in fuzzy DEMATEL.
Multilingual Distributed Representations without Word Alignment
Authors: Karl Moritz Hermann, Phil Blunsom
Date: 2013-12-20 00:00:00
Tasks: Cross-Lingual Document Classification, Document Classification, Sentence, Sentiment Analysis, Word Alignment

Distributed representations of meaning are a natural way to encode covariance
relationships between words and phrases in NLP. By overcoming data sparsity
problems, as well as providing information about semantic relatedness which is
not available in discrete representations, distributed representations have
proven useful in many NLP tasks. Recent work has shown how compositional
semantic representations can successfully be applied to a number of monolingual
applications such as sentiment analysis. At the same time, there has been some
initial success in work on learning shared word-level representations across
languages. We combine these two approaches by proposing a method for learning
distributed representations in a multilingual setup. Our model learns to assign
similar embeddings to aligned sentences and dissimilar ones to sentence which
are not aligned while not requiring word alignments. We show that our
representations are semantically informative and apply them to a cross-lingual
document classification task where we outperform the previous state of the art.
Further, by employing parallel corpora of multiple language pairs we find that
our model learns representations that capture semantic relationships across
languages for which no parallel data was used.
Unconfused Ultraconservative Multiclass Algorithms
Authors: Ugo Louche, Liva Ralaivola
Date: 2014-03-20 00:00:00
Tasks: General Classification

We tackle the problem of learning linear classifiers from noisy datasets in a
multiclass setting. The two-class version of this problem was studied a few
years ago by, e.g. Bylander (1994) and Blum et al. (1996): in these
contributions, the proposed approaches to fight the noise revolve around a
Perceptron learning scheme fed with peculiar examples computed through a
weighted average of points from the noisy training set. We propose to build
upon these approaches and we introduce a new algorithm called UMA (for
Unconfused Multiclass additive Algorithm) which may be seen as a generalization
to the multiclass setting of the previous approaches. In order to characterize
the noise we use the confusion matrix as a multiclass extension of the
classification noise studied in the aforementioned literature. Theoretically
well-founded, UMA furthermore displays very good empirical noise robustness, as
evidenced by numerical simulations conducted on both synthetic and real data.
Keywords: Multiclass classification, Perceptron, Noisy labels, Confusion Matrix
On The Sample Complexity of Sparse Dictionary Learning
Authors: Matthias Seibert, Martin Kleinsteuber, Rémi Gribonval, Rodolphe Jenatton, Francis Bach
Date: 2014-03-20 00:00:00
Tasks: Dictionary Learning

In the synthesis model signals are represented as a sparse combinations of
atoms from a dictionary. Dictionary learning describes the acquisition process
of the underlying dictionary for a given set of training samples. While ideally
this would be achieved by optimizing the expectation of the factors over the
underlying distribution of the training data, in practice the necessary
information about the distribution is not available. Therefore, in real world
applications it is achieved by minimizing an empirical average over the
available samples. The main goal of this paper is to provide a sample
complexity estimate that controls to what extent the empirical average deviates
from the cost function. This estimate then provides a suitable estimate to the
accuracy of the representation of the learned dictionary. The presented
approach exemplifies the general results proposed by the authors in Sample
Complexity of Dictionary Learning and other Matrix Factorizations, Gribonval et
al. and gives more concrete bounds of the sample complexity of dictionary
learning. We cover a variety of sparsity measures employed in the learning
procedure.