pwc-archive/papers-with-abstracts huggingface dataset:
Image processing
Authors: Franco Rino
Date: 2014-08-25 00:00:00
Tasks: Face Recognition, General Classification, subspace methods

Gabor filters can extract multi-orientation and multiscale features from face
images. Researchers have designed different ways to use the magnitude of the
filtered results for face recognition: Gabor Fisher classifier exploited only
the magnitude information of Gabor magnitude pictures (GMPs); Local Gabor
Binary Pattern uses only the gradient information. In this paper, we regard
GMPs as smooth surfaces. By completely describing the shape of GMPs, we get a
face representation method called Gabor Surface Feature (GSF). First, we
compute the magnitude, 1st and 2nd derivatives of GMPs, then binarize them and
transform them into decimal values. Finally we construct joint histograms and
use subspace methods for classification. Experiments on FERET, ORL and FRGC
1.0.4 database show the effectiveness of GSF.
An application of topological graph clustering to protein function prediction
Authors: R. Sean Bowman, Douglas Heisterkamp, Jesse Johnson, Danielle O'Donnol
Date: 2014-08-24 00:00:00
Tasks: Clustering, Graph Clustering, Protein Function Prediction, Topological Data Analysis

We use a semisupervised learning algorithm based on a topological data
analysis approach to assign functional categories to yeast proteins using
similarity graphs. This new approach to analyzing biological networks yields
results that are as good as or better than state of the art existing
approaches.
Fuzzy and entropy facial recognition
Authors: Jaejun Lee, Taeseon Yun
Date: 2014-08-24 00:00:00

This paper suggests an effective method for facial recognition using fuzzy
theory and Shannon entropy. Combination of fuzzy theory and Shannon entropy
eliminates the complication of other methods. Shannon entropy calculates the
ratio of an element between faces, and fuzzy theory calculates the member ship
of the entropy with 1. More details will be mentioned in Section 3. The
learning performance is better than others as it is very simple, and only need
two data per learning. By using factors that don't usually change during the
life, the method will have a high accuracy.
Learning a Hierarchical Compositional Shape Vocabulary for Multi-class Object Representation
Authors: Sanja Fidler, Marko Boben, Ales Leonardis
Date: 2014-08-23 00:00:00
Tasks: Object

Hierarchies allow feature sharing between objects at multiple levels of
representation, can code exponential variability in a very compact way and
enable fast inference. This makes them potentially suitable for learning and
recognizing a higher number of object classes. However, the success of the
hierarchical approaches so far has been hindered by the use of hand-crafted
features or predetermined grouping rules. This paper presents a novel framework
for learning a hierarchical compositional shape vocabulary for representing
multiple object classes. The approach takes simple contour fragments and learns
their frequent spatial configurations. These are recursively combined into
increasingly more complex and class-specific shape compositions, each exerting
a high degree of shape variability. At the top-level of the vocabulary, the
compositions are sufficiently large and complex to represent the whole shapes
of the objects. We learn the vocabulary layer after layer, by gradually
increasing the size of the window of analysis and reducing the spatial
resolution at which the shape configurations are learned. The lower layers are
learned jointly on images of all classes, whereas the higher layers of the
vocabulary are learned incrementally, by presenting the algorithm with one
object class after another. The experimental results show that the learned
multi-class object representation scales favorably with the number of object
classes and achieves a state-of-the-art detection performance at both, faster
inference as well as shorter training times.
Soft Neutrosophic Algebraic Structures and Their Generalization
Authors: Florentin Smarandache, Mumtaz Ali, Muhammad Shabir
Date: 2014-08-23 00:00:00

Study of soft sets was first proposed by Molodtsov in 1999 to deal with
uncertainty in a non-parametric manner. The researchers did not pay attention
to soft set theory at that time but now the soft set theory has been developed
in many areas of mathematics. Algebraic structures using soft set theory are
very rapidly developed. In this book we developed soft neutrosophic algebraic
structures by using soft sets and neutrosophic algebraic structures. In this
book we study soft neutrosophic groups, soft neutrosophic semigroups, soft
neutrosophic loops, soft neutrosophic LA-semigroups, and their generalizations
respectively.
Towards Decision Support Technology Platform for Modular Systems
Authors: Mark Sh. Levin
Date: 2014-08-23 00:00:00
Tasks: Clustering, Combinatorial Optimization, Multiple-choice

The survey methodological paper addresses a glance to a general decision
support platform technology for modular systems (modular/composite
alterantives/solutions) in various applied domains. The decision support
platform consists of seven basic combinatorial engineering frameworks (system
synthesis, system modeling, evaluation, detection of bottleneck,
improvement/extension, multistage design, combinatorial evolution and
forecasting). The decision support platform is based on decision support
procedures (e.g., multicriteria selection/sorting, clustering), combinatorial
optimization problems (e.g., knapsack, multiple choice problem, clique,
assignment/allocation, covering, spanning trees), and their combinations. The
following is described: (1) general scheme of the decision support platform
technology; (2) brief descriptions of modular (composite) systems (or composite
alternatives); (3) trends in moving from chocie/selection of alternatives to
processing of composite alternatives which correspond to hierarchical modular
products/systems; (4) scheme of resource requirements (i.e., human,
information-computer); and (5) basic combinatorial engineering frameworks and
their applications in various domains.
Interpreting Tree Ensembles with inTrees
Authors: Houtao Deng
Date: 2014-08-23 00:00:00
Tasks: Future prediction, General Classification, regression

Tree ensembles such as random forests and boosted trees are accurate but
difficult to understand, debug and deploy. In this work, we provide the inTrees
(interpretable trees) framework that extracts, measures, prunes and selects
rules from a tree ensemble, and calculates frequent variable interactions. An
rule-based learner, referred to as the simplified tree ensemble learner (STEL),
can also be formed and used for future prediction. The inTrees framework can
applied to both classification and regression problems, and is applicable to
many types of tree ensembles, e.g., random forests, regularized random forests,
and boosted trees. We implemented the inTrees algorithms in the "inTrees" R
package.
Stretchy Polynomial Regression
Authors: Kar-Ann Toh
Date: 2014-08-23 00:00:00
Tasks: regression

This article proposes a novel solution for stretchy polynomial regression
learning. The solution comes in primal and dual closed-forms similar to that of
ridge regression. Essentially, the proposed solution stretches the covariance
computation via a power term thereby compresses or amplifies the estimation.
Our experiments on both synthetic data and real-world data show effectiveness
of the proposed method for compressive learning.
Hierarchical Adaptive Structural SVM for Domain Adaptation
Authors: Jiaolong Xu, Sebastian Ramos, David Vazquez, Antonio M. Lopez
Date: 2014-08-22 00:00:00
Tasks: Domain Adaptation, General Classification, image-classification, Image Classification, Object, object-detection, Object Detection, Pedestrian Detection

A key topic in classification is the accuracy loss produced when the data
distribution in the training (source) domain differs from that in the testing
(target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and
object category recognition. In this paper, we present a novel domain
adaptation method that leverages multiple target domains (or sub-domains) in a
hierarchical adaptation tree. The core idea is to exploit the commonalities and
differences of the jointly considered target domains.
  Given the relevance of structural SVM (SSVM) classifiers, we apply our idea
to the adaptive SSVM (A-SSVM), which only requires the target domain samples
together with the existing source-domain classifier for performing the desired
adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM).
  As proof of concept we use HA-SSVM for pedestrian detection and object
category recognition. In the former we apply HA-SSVM to the deformable
part-based model (DPM) while in the latter HA-SSVM is applied to multi-category
classifiers. In both cases, we show how HA-SSVM is effective in increasing the
detection/recognition accuracy with respect to adaptation strategies that
ignore the structure of the target data. Since, the sub-domains of the target
data are not always known a priori, we shown how HA-SSVM can incorporate
sub-domain structure discovery for object category recognition.
Computing Multi-Relational Sufficient Statistics for Large Databases
Authors: Zhensong Qian, Oliver Schulte, Yan Sun
Date: 2014-08-22 00:00:00
Tasks: feature selection

Databases contain information about which relationships do and do not hold
among entities. To make this information accessible for statistical analysis
requires computing sufficient statistics that combine information from
different database tables. Such statistics may involve any number of {\em
positive and negative} relationships. With a naive enumeration approach,
computing sufficient statistics for negative relationships is feasible only for
small databases. We solve this problem with a new dynamic programming algorithm
that performs a virtual join, where the requisite counts are computed without
materializing join tables. Contingency table algebra is a new extension of
relational algebra, that facilitates the efficient implementation of this
M\"obius virtual join operation. The M\"obius Join scales to large datasets
(over 1M tuples) with complex schemas. Empirical evaluation with seven
benchmark datasets showed that information about the presence and absence of
links can be exploited in feature selection, association rule mining, and
Bayesian network learning.
Dynamic Sweep Filtering Algorithm for FlexC
Authors: Alban Derrien, Thierry Petit, Stephane Zampelli
Date: 2014-08-22 00:00:00
Tasks: Scheduling

We investigate cumulative scheduling in uncertain environments, using
constraint programming. We detail in this paper the dynamic sweep filtering
algorithm of the FlexC global constraint.
Nonconvex Statistical Optimization: Minimax-Optimal Sparse PCA in Polynomial Time
Authors: Zhaoran Wang, Huanran Lu, Han Liu
Date: 2014-08-22 00:00:00

Sparse principal component analysis (PCA) involves nonconvex optimization for
which the global solution is hard to obtain. To address this issue, one popular
approach is convex relaxation. However, such an approach may produce suboptimal
estimators due to the relaxation effect. To optimally estimate sparse principal
subspaces, we propose a two-stage computational framework named "tighten after
relax": Within the 'relax' stage, we approximately solve a convex relaxation of
sparse PCA with early stopping to obtain a desired initial estimator; For the
'tighten' stage, we propose a novel algorithm called sparse orthogonal
iteration pursuit (SOAP), which iteratively refines the initial estimator by
directly solving the underlying nonconvex problem. A key concept of this
two-stage framework is the basin of attraction. It represents a local region
within which the `tighten' stage has desired computational and statistical
guarantees. We prove that, the initial estimator obtained from the 'relax'
stage falls into such a region, and hence SOAP geometrically converges to a
principal subspace estimator which is minimax-optimal within a certain model
class. Unlike most existing sparse PCA estimators, our approach applies to the
non-spiked covariance models, and adapts to non-Gaussianity as well as
dependent data settings. Moreover, through analyzing the computational
complexity of the two stages, we illustrate an interesting phenomenon that
larger sample size can reduce the total iteration complexity. Our framework
motivates a general paradigm for solving many complex statistical problems
which involve nonconvex optimization with provable guarantees.
Structural bias in population-based algorithms
Authors: Anna V. Kononova, David W. Corne, Philippe De Wilde, Vsevolod Shneer, Fabio Caraffini
Date: 2014-08-22 00:00:00
Tasks: Clustering

Challenging optimisation problems are abundant in all areas of science. Since
the 1950s, scientists have developed ever-diversifying families of black box
optimisation algorithms designed to address any optimisation problem, requiring
only that quality of a candidate solution is calculated via a fitness function
specific to the problem. For such algorithms to be successful, at least three
properties are required: an effective informed sampling strategy, that guides
generation of new candidates on the basis of fitnesses and locations of
previously visited candidates; mechanisms to ensure efficiency, so that same
candidates are not repeatedly visited; absence of structural bias, which, if
present, would predispose the algorithm towards limiting its search to some
regions of solution space. The first two of these properties have been
extensively investigated, however the third is little understood. In this
article we provide theoretical and empirical analyses that contribute to the
understanding of structural bias. We prove a theorem concerning dynamics of
population variance in the case of real-valued search spaces. This reveals how
structural bias can manifest as non-uniform clustering of population over time.
Theory predicts that structural bias is exacerbated with increasing population
size and problem difficulty. These predictions reveal two previously
unrecognised aspects of structural bias. Respectively, increasing population
size, though ostensibly promoting diversity, will magnify any inherent
structural bias, and effects of structural bias are more apparent when faced
with difficult problems. Our theoretical result also suggests that two commonly
used approaches to enhancing exploration, increasing population size and
increasing disruptiveness of search operators, have quite distinct implications
in terms of structural bias.
Bat Algorithm is Better Than Intermittent Search Strategy
Authors: Xin-She Yang, Suash Deb, Simon Fong
Date: 2014-08-22 00:00:00

The efficiency of any metaheuristic algorithm largely depends on the way of
balancing local intensive exploitation and global diverse exploration. Studies
show that bat algorithm can provide a good balance between these two key
components with superior efficiency. In this paper, we first review some
commonly used metaheuristic algorithms, and then compare the performance of bat
algorithm with the so-called intermittent search strategy. From simulations, we
found that bat algorithm is better than the optimal intermittent search
strategy. We also analyse the comparison results and their implications for
higher dimensional optimization problems. In addition, we also apply bat
algorithm in solving business optimization and engineering design problems.
Cuckoo Search: A Brief Literature Review
Authors: I. Fister Jr., X. S. Yang, D. Fister, I. Fister
Date: 2014-08-22 00:00:00

Cuckoo search (CS) was introduced in 2009, and it has attracted great
attention due to its promising efficiency in solving many optimization problems
and real-world applications. In the last few years, many papers have been
published regarding cuckoo search, and the relevant literature has expanded
significantly. This chapter summarizes briefly the majority of the literature
about cuckoo search in peer-reviewed journals and conferences found so far.
These references can be systematically classified into appropriate categories,
which can be used as a basis for further research.
Flower Pollination Algorithm: A Novel Approach for Multiobjective Optimization
Authors: Xin-She Yang, M. Karamanoglu, X. S. He
Date: 2014-08-22 00:00:00
Tasks: Multiobjective Optimization

Multiobjective design optimization problems require multiobjective
optimization techniques to solve, and it is often very challenging to obtain
high-quality Pareto fronts accurately. In this paper, the recently developed
flower pollination algorithm (FPA) is extended to solve multiobjective
optimization problems. The proposed method is used to solve a set of
multobjective test functions and two bi-objective design benchmarks, and a
comparison of the proposed algorithm with other algorithms has been made, which
shows that FPA is efficient with a good convergence rate. Finally, the
importance for further parametric studies and theoretical analysis are
highlighted and discussed.
Applications and Analysis of Bio-Inspired Eagle Strategy for Engineering Optimization
Authors: Xin-She Yang, M. Karamanoglu, T. O. Ting, Y. X. Zhao
Date: 2014-08-22 00:00:00
Tasks: Diversity

All swarm-intelligence-based optimization algorithms use some stochastic
components to increase the diversity of solutions during the search process.
Such randomization is often represented in terms of random walks. However, it
is not yet clear why some randomization techniques (and thus why some
algorithms) may perform better than others for a given set of problems. In this
work, we analyze these randomization methods in the context of nature-inspired
algorithms. We also use eagle strategy to provide basic observations and relate
step sizes and search efficiency using Markov theory. Then, we apply our
analysis and observations to solve four design benchmarks, including the
designs of a pressure vessel, a speed reducer, a PID controller and a heat
exchanger. Our results demonstrate that eagle strategy with L\'evy flights can
perform extremely well in reducing the overall computational efforts.
Cuckoo Search: Recent Advances and Applications
Authors: Xin-She Yang, Suash Deb
Date: 2014-08-22 00:00:00
Tasks: global-optimization

Cuckoo search (CS) is a relatively new algorithm, developed by Yang and Deb
in 2009, and CS is efficient in solving global optimization problems. In this
paper, we review the fundamental ideas of cuckoo search and the latest
developments as well as its applications. We analyze the algorithm and gain
insight into its search mechanisms and find out why it is efficient. We also
discuss the essence of algorithms and its link to self-organizing systems, and
finally we propose some important topics for further research.
Continuum armed bandit problem of few variables in high dimensions
Authors: Hemant Tyagi, Bernd Gärtner
Date: 2013-04-21 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We consider the stochastic and adversarial settings of continuum armed
bandits where the arms are indexed by [0,1]^d. The reward functions r:[0,1]^d
-> R are assumed to intrinsically depend on at most k coordinate variables
implying r(x_1,..,x_d) = g(x_{i_1},..,x_{i_k}) for distinct and unknown
i_1,..,i_k from {1,..,d} and some locally Holder continuous g:[0,1]^k -> R with
exponent 0 < alpha <= 1. Firstly, assuming (i_1,..,i_k) to be fixed across
time, we propose a simple modification of the CAB1 algorithm where we construct
the discrete set of sampling points to obtain a bound of
O(n^((alpha+k)/(2*alpha+k)) (log n)^((alpha)/(2*alpha+k)) C(k,d)) on the
regret, with C(k,d) depending at most polynomially in k and sub-logarithmically
in d. The construction is based on creating partitions of {1,..,d} into k
disjoint subsets and is probabilistic, hence our result holds with high
probability. Secondly we extend our results to also handle the more general
case where (i_1,...,i_k) can change over time and derive regret bounds for the
same.
Joint Hierarchical Gaussian Process Model with Application to Forecast in Medical Monitoring
Authors: Leo L. Duan, John P. Clancy, Rhonda D. Szczesniak
Date: 2014-08-20 00:00:00

A novel extrapolation method is proposed for longitudinal forecasting. A
hierarchical Gaussian process model is used to combine nonlinear population
change and individual memory of the past to make prediction. The prediction
error is minimized through the hierarchical design. The method is further
extended to joint modeling of continuous measurements and survival events. The
baseline hazard, covariate and joint effects are conveniently modeled in this
hierarchical structure. The estimation and inference are implemented in fully
Bayesian framework using the objective and shrinkage priors. In simulation
studies, this model shows robustness in latent estimation, correlation
detection and high accuracy in forecasting. The model is illustrated with
medical monitoring data from cystic fibrosis (CF) patients. Estimation and
forecasts are obtained in the measurement of lung function and records of acute
respiratory events.
  Keyword: Extrapolation, Joint Model, Longitudinal Model, Hierarchical
Gaussian Process, Cystic Fibrosis, Medical Monitoring
Automatic Extraction of Protein Interaction in Literature
Authors: Peilei Liu, Ting Wang
Date: 2014-06-08 00:00:00

Protein-protein interaction extraction is the key precondition of the
construction of protein knowledge network, and it is very important for the
research in the biomedicine. This paper extracted directional protein-protein
interaction from the biological text, using the SVM-based method. Experiments
were evaluated on the LLL05 corpus with good results. The results show that
dependency features are import for the protein-protein interaction extraction
and features related to the interaction word are effective for the interaction
direction judgment. At last, we analyzed the effects of different features and
planed for the next step.
Neural Mechanism of Language
Authors: Peilei Liu, Ting Wang
Date: 2014-08-22 00:00:00
Tasks: Position

This paper is based on our previous work on neural coding. It is a
self-organized model supported by existing evidences. Firstly, we briefly
introduce this model in this paper, and then we explain the neural mechanism of
language and reasoning with it. Moreover, we find that the position of an area
determines its importance. Specifically, language relevant areas are in the
capital position of the cortical kingdom. Therefore they are closely related
with autonomous consciousness and working memories. In essence, language is a
miniature of the real world. Briefly, this paper would like to bridge the gap
between molecule mechanism of neurons and advanced functions such as language
and reasoning.
Unsupervised Spike Sorting Based on Discriminative Subspace Learning
Authors: Mohammad Reza Keshtkaran, Zhi Yang
Date: 2014-08-22 00:00:00
Tasks: Clustering, Spike Sorting

Spike sorting is a fundamental preprocessing step for many neuroscience
studies which rely on the analysis of spike trains. In this paper, we present
two unsupervised spike sorting algorithms based on discriminative subspace
learning. The first algorithm simultaneously learns the discriminative feature
subspace and performs clustering. It uses histogram of features in the most
discriminative projection to detect the number of neurons. The second algorithm
performs hierarchical divisive clustering that learns a discriminative
1-dimensional subspace for clustering in each level of the hierarchy until
achieving almost unimodal distribution in the subspace. The algorithms are
tested on synthetic and in-vivo data, and are compared against two widely used
spike sorting methods. The comparative results demonstrate that our spike
sorting methods can achieve substantially higher accuracy in lower dimensional
feature space, and they are highly robust to noise. Moreover, they provide
significantly better cluster separability in the learned subspace than in the
subspace obtained by principal component analysis or wavelet transform.