pwc-archive/papers-with-abstracts huggingface dataset:
Towards Automated Proof Strategy Generalisation
Authors: Gudmund Grov, Ewen Maclean
Date: 2013-03-12 00:00:00

The ability to automatically generalise (interactive) proofs and use such
generalisations to discharge related conjectures is a very hard problem which
remains unsolved. Here, we develop a notion of goal types to capture key
properties of goals, which enables abstractions over the specific order and
number of sub-goals arising when composing tactics. We show that the goal types
form a lattice, and utilise this property in the techniques we develop to
automatically generalise proof strategies in order to reuse it for proofs of
related conjectures. We illustrate our approach with an example.
Flexibly-bounded Rationality and Marginalization of Irrationality Theories for Decision Making
Authors: Tshilidzi Marwala
Date: 2013-06-09 00:00:00
Tasks: Decision Making

In this paper the theory of flexibly-bounded rationality which is an
extension to the theory of bounded rationality is revisited. Rational decision
making involves using information which is almost always imperfect and
incomplete together with some intelligent machine which if it is a human being
is inconsistent to make decisions. In bounded rationality, this decision is
made irrespective of the fact that the information to be used is incomplete and
imperfect and that the human brain is inconsistent and thus this decision that
is to be made is taken within the bounds of these limitations. In the theory of
flexibly-bounded rationality, advanced information analysis is used, the
correlation machine is applied to complete missing information and artificial
intelligence is used to make more consistent decisions. Therefore
flexibly-bounded rationality expands the bounds within which rationality is
exercised. Because human decision making is essentially irrational, this paper
proposes the theory of marginalization of irrationality in decision making to
deal with the problem of satisficing in the presence of irrationality.
Comparing Edge Detection Methods based on Stochastic Entropies and Distances for PolSAR Imagery
Authors: Abraão D. C. Nascimento, Michelle M. Horta, Alejandro C. Frery, Renato J. Cintra
Date: 2013-06-09 00:00:00
Tasks: Edge Detection

Polarimetric synthetic aperture radar (PolSAR) has achieved a prominent
position as a remote imaging method. However, PolSAR images are contaminated by
speckle noise due to the coherent illumination employed during the data
acquisition. This noise provides a granular aspect to the image, making its
processing and analysis (such as in edge detection) hard tasks. This paper
discusses seven methods for edge detection in multilook PolSAR images. In all
methods, the basic idea consists in detecting transition points in the finest
possible strip of data which spans two regions. The edge is contoured using the
transitions points and a B-spline curve. Four stochastic distances, two
differences of entropies, and the maximum likelihood criterion were used under
the scaled complex Wishart distribution; the first six stem from the h-phi
class of measures. The performance of the discussed detection methods was
quantified and analyzed by the computational time and probability of correct
edge detection, with respect to the number of looks, the backscatter matrix as
a whole, the SPAN, the covariance an the spatial resolution. The detection
procedures were applied to three real PolSAR images. Results provide evidence
that the methods based on the Bhattacharyya distance and the difference of
Shannon entropies outperform the other techniques.
Blind Signal Separation in the Presence of Gaussian Noise
Authors: Mikhail Belkin, Luis Rademacher, James Voss
Date: 2012-11-07 00:00:00

A prototypical blind signal separation problem is the so-called cocktail
party problem, with n people talking simultaneously and n different microphones
within a room. The goal is to recover each speech signal from the microphone
inputs. Mathematically this can be modeled by assuming that we are given
samples from an n-dimensional random variable X=AS, where S is a vector whose
coordinates are independent random variables corresponding to each speaker. The
objective is to recover the matrix A^{-1} given random samples from X. A range
of techniques collectively known as Independent Component Analysis (ICA) have
been proposed to address this problem in the signal processing and machine
learning literature. Many of these techniques are based on using the kurtosis
or other cumulants to recover the components.
  In this paper we propose a new algorithm for solving the blind signal
separation problem in the presence of additive Gaussian noise, when we are
given samples from X=AS+\eta, where \eta is drawn from an unknown, not
necessarily spherical n-dimensional Gaussian distribution. Our approach is
based on a method for decorrelating a sample with additive Gaussian noise under
the assumption that the underlying distribution is a linear transformation of a
distribution with independent components. Our decorrelation routine is based on
the properties of cumulant tensors and can be combined with any standard
cumulant-based method for ICA to get an algorithm that is provably robust in
the presence of Gaussian noise. We derive polynomial bounds for the sample
complexity and error propagation of our method.
Learning About Meetings
Authors: Been Kim, Cynthia Rudin
Date: 2013-06-08 00:00:00

Most people participate in meetings almost every day, multiple times a day.
The study of meetings is important, but also challenging, as it requires an
understanding of social signals and complex interpersonal dynamics. Our aim
this work is to use a data-driven approach to the science of meetings. We
provide tentative evidence that: i) it is possible to automatically detect when
during the meeting a key decision is taking place, from analyzing only the
local dialogue acts, ii) there are common patterns in the way social dialogue
acts are interspersed throughout a meeting, iii) at the time key decisions are
made, the amount of time left in the meeting can be predicted from the amount
of time that has passed, iv) it is often possible to predict whether a proposal
during a meeting will be accepted or rejected based entirely on the language
(the set of persuasive words) used by the speaker.
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
Authors: Tapio Pahikkala, Antti Airola, Michiel Stock, Bernard De Baets, Willem Waegeman
Date: 2012-09-21 00:00:00
Tasks: Computational Efficiency, Information Retrieval, regression, Retrieval

In domains like bioinformatics, information retrieval and social network
analysis, one can find learning tasks where the goal consists of inferring a
ranking of objects, conditioned on a particular target object. We present a
general kernel framework for learning conditional rankings from various types
of relational data, where rankings can be conditioned on unseen data objects.
We propose efficient algorithms for conditional ranking by optimizing squared
regression and ranking loss functions. We show theoretically, that learning
with the ranking loss is likely to generalize better than with the regression
loss. Further, we prove that symmetry or reciprocity properties of relations
can be efficiently enforced in the learned models. Experiments on synthetic and
real-world data illustrate that the proposed methods deliver state-of-the-art
performance in terms of predictive power and computational efficiency.
Moreover, we also show empirically that incorporating symmetry or reciprocity
properties can improve the generalization performance.
Emotional Expression Classification using Time-Series Kernels
Authors: Andras Lorincz, Laszlo Jeni, Zoltan Szabo, Jeffrey Cohn, Takeo Kanade
Date: 2013-06-08 00:00:00
Tasks: Classification, Dynamic Time Warping, General Classification, Time Series, Time Series Analysis

Estimation of facial expressions, as spatio-temporal processes, can take
advantage of kernel methods if one considers facial landmark positions and
their motion in 3D space. We applied support vector classification with kernels
derived from dynamic time-warping similarity measures. We achieved over 99%
accuracy - measured by area under ROC curve - using only the 'motion pattern'
of the PCA compressed representation of the marker point vector, the so-called
shape parameters. Beyond the classification of full motion patterns, several
expressions were recognized with over 90% accuracy in as few as 5-6 frames from
their onset, about 200 milliseconds.
Speckle Reduction with Adaptive Stack Filters
Authors: María Elena Buemi, Alejandro C. Frery, Heitor S. Ramos
Date: 2013-06-08 00:00:00
Tasks: General Classification

Stack filters are a special case of non-linear filters. They have a good
performance for filtering images with different types of noise while preserving
edges and details. A stack filter decomposes an input image into stacks of
binary images according to a set of thresholds. Each binary image is then
filtered by a Boolean function, which characterizes the filter. Adaptive stack
filters can be computed by training using a prototype (ideal) image and its
corrupted version, leading to optimized filters with respect to a loss
function. In this work we propose the use of training with selected samples for
the estimation of the optimal Boolean function. We study the performance of
adaptive stack filters when they are applied to speckled imagery, in particular
to Synthetic Aperture Radar (SAR) images. This is done by evaluating the
quality of the filtered images through the use of suitable image quality
indexes and by measuring the classification accuracy of the resulting images.
We used SAR images as input, since they are affected by speckle noise that
makes classification a difficult task.
A Factor Graph Approach to Joint OFDM Channel Estimation and Decoding in Impulsive Noise Environments
Authors: Marcel Nassar, Philip Schniter, Brian L. Evans
Date: 2013-06-07 00:00:00

We propose a novel receiver for orthogonal frequency division multiplexing
(OFDM) transmissions in impulsive noise environments. Impulsive noise arises in
many modern wireless and wireline communication systems, such as Wi-Fi and
powerline communications, due to uncoordinated interference that is much
stronger than thermal noise. We first show that the bit-error-rate optimal
receiver jointly estimates the propagation channel coefficients, the noise
impulses, the finite-alphabet symbols, and the unknown bits. We then propose a
near-optimal yet computationally tractable approach to this joint estimation
problem using loopy belief propagation. In particular, we merge the recently
proposed "generalized approximate message passing" (GAMP) algorithm with the
forward-backward algorithm and soft-input soft-output decoding using a "turbo"
approach. Numerical results indicate that the proposed receiver drastically
outperforms existing receivers under impulsive noise and comes within 1 dB of
the matched-filter bound. Meanwhile, with N tones, the proposed
factor-graph-based receiver has only O(N log N) complexity, and it can be
parallelized.
Orbital-free Bond Breaking via Machine Learning
Authors: John C. Snyder, Matthias Rupp, Katja Hansen, Leo Blooston, Klaus-Robert Müller, Kieron Burke
Date: 2013-06-07 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning is used to approximate the kinetic energy of one dimensional
diatomics as a functional of the electron density. The functional can
accurately dissociate a diatomic, and can be systematically improved with
training. Highly accurate self-consistent densities and molecular forces are
found, indicating the possibility for ab-initio molecular dynamics simulations.
The DeLiVerMATH project - Text analysis in mathematics
Authors: Ulf Schöneberg, Wolfram Sperber
Date: 2013-06-07 00:00:00
Tasks: Classification, General Classification, Retrieval

A high-quality content analysis is essential for retrieval functionalities
but the manual extraction of key phrases and classification is expensive.
Natural language processing provides a framework to automatize the process.
Here, a machine-based approach for the content analysis of mathematical texts
is described. A prototype for key phrase extraction and classification of
mathematical texts is presented.
Fast greedy algorithm for subspace clustering from corrupted and incomplete data
Authors: Alexander Petukhov, Inna Kozlov
Date: 2013-06-07 00:00:00
Tasks: Clustering, Face Recognition

We describe the Fast Greedy Sparse Subspace Clustering (FGSSC) algorithm
providing an efficient method for clustering data belonging to a few
low-dimensional linear or affine subspaces. The main difference of our
algorithm from predecessors is its ability to work with noisy data having a
high rate of erasures (missed entries with the known coordinates) and errors
(corrupted entries with unknown coordinates). We discuss here how to implement
the fast version of the greedy algorithm with the maximum efficiency whose
greedy strategy is incorporated into iterations of the basic algorithm.
  We provide numerical evidences that, in the subspace clustering capability,
the fast greedy algorithm outperforms not only the existing state-of-the art
SSC algorithm taken by the authors as a basic algorithm but also the recent
GSSC algorithm. At the same time, its computational cost is only slightly
higher than the cost of SSC.
  The numerical evidence of the algorithm significant advantage is presented
for a few synthetic models as well as for the Extended Yale B dataset of facial
images. In particular, the face recognition misclassification rate turned out
to be 6-20 times lower than for the SSC algorithm. We provide also the
numerical evidence that the FGSSC algorithm is able to perform clustering of
corrupted data efficiently even when the sum of subspace dimensions
significantly exceeds the dimension of the ambient space.
Clifford Fourier-Mellin transform with two real square roots of -1 in Cl(p,q), p+q=2
Authors: Eckhard Hitzer
Date: 2013-06-07 00:00:00

We describe a non-commutative generalization of the complex Fourier-Mellin
transform to Clifford algebra valued signal functions over the domain
$\R^{p,q}$ taking values in Cl(p,q), p+q=2.
  Keywords: algebra, Fourier transforms; Logic, set theory, and algebra,
Fourier analysis, Integral transforms
Algebraic foundations of split hypercomplex nonlinear adaptive filtering
Authors: Eckhard Hitzer
Date: 2013-06-07 00:00:00

A split hypercomplex learning algorithm for the training of nonlinear finite
impulse response adaptive filters for the processing of hypercomplex signals of
any dimension is proposed. The derivation strictly takes into account the laws
of hypercomplex algebra and hypercomplex calculus, some of which have been
neglected in existing learning approaches (e.g. for quaternions). Already in
the case of quaternions we can predict improvements in performance of
hypercomplex processes. The convergence of the proposed algorithms is
rigorously analyzed.
  Keywords: Quaternionic adaptive filtering, Hypercomplex adaptive filtering,
Nonlinear adaptive filtering, Hypercomplex Multilayer Perceptron, Clifford
geometric algebra
Reducing statistical time-series problems to binary classification
Authors: Daniil Ryabko, Jérémie Mary
Date: 2012-10-22 00:00:00
Proceeding: NeurIPS 2012 12
Tasks: Binary Classification, Classification, Clustering, General Classification, Time Series, Time Series Analysis, Time Series Clustering

We show how binary classification methods developed to work on i.i.d. data
can be used for solving statistical problems that are seemingly unrelated to
classification and concern highly-dependent time series. Specifically, the
problems of time-series clustering, homogeneity testing and the three-sample
problem are addressed. The algorithms that we construct for solving these
problems are based on a new metric between time-series distributions, which can
be evaluated using binary classification methods. Universal consistency of the
proposed algorithms is proven under most general assumptions. The theoretical
results are illustrated with experiments on synthetic and real-world data.
Quaternionic Fourier-Mellin Transform
Authors: Eckhard Hitzer
Date: 2013-06-07 00:00:00
Tasks: Image Reconstruction

In this contribution we generalize the classical Fourier Mellin transform [S.
Dorrode and F. Ghorbel, Robust and efficient Fourier-Mellin transform
approximations for gray-level image reconstruction and complete invariant
description, Computer Vision and Image Understanding, 83(1) (2001), 57-78, DOI
10.1006/cviu.2001.0922.], which transforms functions $f$ representing, e.g., a
gray level image defined over a compact set of $\mathbb{R}^2$. The quaternionic
Fourier Mellin transform (QFMT) applies to functions $f: \mathbb{R}^2
\rightarrow \mathbb{H}$, for which $|f|$ is summable over $\mathbb{R}_+^*
\times \mathbb{S}^1$ under the measure $d\theta \frac{dr}{r}$. $\mathbb{R}_+^*$
is the multiplicative group of positive and non-zero real numbers. We
investigate the properties of the QFMT similar to the investigation of the
quaternionic Fourier Transform (QFT) in [E. Hitzer, Quaternion Fourier
Transform on Quaternion Fields and Generalizations, Advances in Applied
Clifford Algebras, 17(3) (2007), 497-517.; E. Hitzer, Directional Uncertainty
Principle for Quaternion Fourier Transforms, Advances in Applied Clifford
Algebras, 20(2) (2010), 271-284, online since 08 July 2009.].
Non-constant bounded holomorphic functions of hyperbolic numbers - Candidates for hyperbolic activation functions
Authors: Eckhard Hitzer
Date: 2013-06-07 00:00:00

The Liouville theorem states that bounded holomorphic complex functions are
necessarily constant. Holomorphic functions fulfill the socalled Cauchy-Riemann
(CR) conditions. The CR conditions mean that a complex $z$-derivative is
independent of the direction. Holomorphic functions are ideal for activation
functions of complex neural networks, but the Liouville theorem makes them
useless. Yet recently the use of hyperbolic numbers, lead to the construction
of hyperbolic number neural networks. We will describe the Cauchy-Riemann
conditions for hyperbolic numbers and show that there exists a new interesting
type of bounded holomorphic functions of hyperbolic numbers, which are not
constant. We give examples of such functions. They therefore substantially
expand the available candidates for holomorphic activation functions for
hyperbolic number neural networks.
  Keywords: Hyperbolic numbers, Liouville theorem, Cauchy-Riemann conditions,
bounded holomorphic functions
OPS-QFTs: A new type of quaternion Fourier transforms based on the orthogonal planes split with one or two general pure quaternions
Authors: Eckhard Hitzer
Date: 2013-06-07 00:00:00

We explain the orthogonal planes split (OPS) of quaternions based on the
arbitrary choice of one or two linearly independent pure unit quaternions
$f,g$. Next we systematically generalize the quaternionic Fourier transform
(QFT) applied to quaternion fields to conform with the OPS determined by $f,g$,
or by only one pure unit quaternion $f$, comment on their geometric meaning,
and establish inverse transformations.
  Keywords: Clifford geometric algebra, quaternion geometry, quaternion Fourier
transform, inverse Fourier transform, orthogonal planes split
Statistical Denoising for single molecule fluorescence microscopic images
Authors: Ji Won Yoon
Date: 2013-06-07 00:00:00
Tasks: Denoising

Single molecule fluorescence microscopy is a powerful technique for
uncovering detailed information about biological systems, both in vitro and in
vivo. In such experiments, the inherently low signal to noise ratios mean that
accurate algorithms to separate true signal and background noise are essential
to generate meaningful results. To this end, we have developed a new and robust
method to reduce noise in single molecule fluorescence images by using a
Gaussian Markov Random Field (GMRF) prior in a Bayesian framework. Two
different strategies are proposed to build the prior - an intrinsic GMRF, with
a stationary relationship between pixels and a heterogeneous intrinsic GMRF,
with a differently weighted relationship between pixels classified as molecules
and background. Testing with synthetic and real experimental fluorescence
images demonstrates that the heterogeneous intrinsic GMRF is superior to other
conventional de-noising approaches.
Vesselness features and the inverse compositional AAM for robust face recognition using thermal IR
Authors: Reza Shoja Ghiass, Ognjen Arandjelovic, Hakim Bendada, Xavier Maldague
Date: 2013-06-07 00:00:00
Tasks: Face Recognition, Robust Face Recognition

Over the course of the last decade, infrared (IR) and particularly thermal IR
imaging based face recognition has emerged as a promising complement to
conventional, visible spectrum based approaches which continue to struggle when
applied in the real world. While inherently insensitive to visible spectrum
illumination changes, IR images introduce specific challenges of their own,
most notably sensitivity to factors which affect facial heat emission patterns,
e.g. emotional state, ambient temperature, and alcohol intake. In addition,
facial expression and pose changes are more difficult to correct in IR images
because they are less rich in high frequency detail which is an important cue
for fitting any deformable model. We describe a novel method which addresses
these challenges. To normalize for pose and facial expression changes we
generate a synthetic frontal image of a face in a canonical, neutral facial
expression from an image of the face in an arbitrary pose and facial
expression. This is achieved by piecewise affine warping which follows active
appearance model (AAM) fitting. This is the first publication which explores
the use of an AAM on thermal IR images; we propose a pre-processing step which
enhances detail in thermal images, making AAM convergence faster and more
accurate. To overcome the problem of thermal IR image sensitivity to the
pattern of facial temperature emissions we describe a representation based on
reliable anatomical features. In contrast to previous approaches, our
representation is not binary; rather, our method accounts for the reliability
of the extracted features. This makes the proposed representation much more
robust both to pose and scale changes. The effectiveness of the proposed
approach is demonstrated on the largest public database of thermal IR images of
faces on which it achieved 100% identification, significantly outperforming
previous methods.
Accomplishable Tasks in Knowledge Representation
Authors: Keehang Kwon, Mi-Young Park
Date: 2013-06-07 00:00:00

Knowledge Representation (KR) is traditionally based on the logic of facts,
expressed in boolean logic. However, facts about an agent can also be seen as a
set of accomplished tasks by the agent. This paper proposes a new approach to
KR: the notion of task logical KR based on Computability Logic. This notion
allows the user to represent both accomplished tasks and accomplishable tasks
by the agent. This notion allows us to build sophisticated KRs about many
interesting agents, which have not been supported by previous logical
languages.