pwc-archive/papers-with-abstracts huggingface dataset:
Theoretical formulation and analysis of the deterministic dendritic cell algorithm
Authors: Feng Gu, Julie Greensmith, Uwe Aickelin
Date: 2013-05-31 00:00:00

As one of the emerging algorithms in the field of Artificial Immune Systems
(AIS), the Dendritic Cell Algorithm (DCA) has been successfully applied to a
number of challenging real-world problems. However, one criticism is the lack
of a formal definition, which could result in ambiguity for understanding the
algorithm. Moreover, previous investigations have mainly focused on its
empirical aspects. Therefore, it is necessary to provide a formal definition of
the algorithm, as well as to perform runtime analyses to revealits theoretical
aspects. In this paper, we define the deterministic version of the DCA, named
the dDCA, using set theory and mathematical functions. Runtime analyses of the
standard algorithm and the one with additional segmentation are performed. Our
analysis suggests that the standard dDCA has a runtime complexity of O(n2) for
the worst-case scenario, where n is the number of input data instances. The
introduction of segmentation changes the algorithm's worst case runtime
complexity to O(max(nN; nz)), for DC population size N with size of each
segment z. Finally, two runtime variables of the algorithm are formulated based
on the input data, to understand its runtime behaviour as guidelines for
further development.
Investigating Mathematical Models of Immuno-Interactions with Early-Stage Cancer under an Agent-Based Modelling Perspective
Authors: Grazziela P. Figueredo, Peer-Olaf Siebers, Uwe Aickelin
Date: 2013-05-31 00:00:00

Many advances in research regarding immuno-interactions with cancer were
developed with the help of ordinary differential equation (ODE) models. These
models, however, are not effectively capable of representing problems involving
individual localisation, memory and emerging properties, which are common
characteristics of cells and molecules of the immune system. Agent-based
modelling and simulation is an alternative paradigm to ODE models that
overcomes these limitations. In this paper we investigate the potential
contribution of agent-based modelling and simulation when compared to ODE
modelling and simulation. We seek answers to the following questions: Is it
possible to obtain an equivalent agent-based model from the ODE formulation? Do
the outcomes differ? Are there any benefits of using one method compared to the
other? To answer these questions, we have considered three case studies using
established mathematical models of immune interactions with early-stage cancer.
These case studies were re-conceptualised under an agent-based perspective and
the simulation results were then compared with those from the ODE models. Our
results show that it is possible to obtain equivalent agent-based models (i.e.
implementing the same mechanisms); the simulation output of both types of
models however might differ depending on the attributes of the system to be
modelled. In some cases, additional insight from using agent-based modelling
was obtained. Overall, we can confirm that agent-based modelling is a useful
addition to the tool set of immunologists, as it has extra features that allow
for simulations with characteristics that are closer to the biological
phenomena.
Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data
Authors: Yihui Liu, Uwe Aickelin, Jan Feyereisl, Lindy G. Durrant
Date: 2013-05-31 00:00:00
Tasks: Medical Diagnosis, Survival Analysis

Biomarkers which predict patient's survival can play an important role in
medical diagnosis and treatment. How to select the significant biomarkers from
hundreds of protein markers is a key step in survival analysis. In this paper a
novel method is proposed to detect the prognostic biomarkers of survival in
colorectal cancer patients using wavelet analysis, genetic algorithm, and Bayes
classifier. One dimensional discrete wavelet transform (DWT) is normally used
to reduce the dimensionality of biomedical data. In this study one dimensional
continuous wavelet transform (CWT) was proposed to extract the features of
colorectal cancer data. One dimensional CWT has no ability to reduce
dimensionality of data, but captures the missing features of DWT, and is
complementary part of DWT. Genetic algorithm was performed on extracted wavelet
coefficients to select the optimized features, using Bayes classifier to build
its fitness function. The corresponding protein markers were located based on
the position of optimized features. Kaplan-Meier curve and Cox regression model
were used to evaluate the performance of selected biomarkers. Experiments were
conducted on colorectal cancer dataset and several significant biomarkers were
detected. A new protein biomarker CD46 was found to significantly associate
with survival time.
Validation of a Microsimulation of the Port of Dover
Authors: Chris Roadknight, Uwe Aickelin, Galina Sherman
Date: 2013-05-31 00:00:00

Modelling and simulating the traffic of heavily used but secure environments
such as seaports and airports is of increasing importance. Errors made when
simulating these environments can have long standing economic, social and
environmental implications. This paper discusses issues and problems that may
arise when designing a simulation strategy. Data for the Port is presented,
methods for lightweight vehicle assessment that can be used to calibrate and
validate simulations are also discussed along with a diagnosis of
overcalibration issues. We show that decisions about where the intelligence
lies in a system has important repercussions for the reliability of system
statistics. Finally, conclusions are drawn about how microsimulations can be
moved forward as a robust planning tool for the 21st century.
Privileged Information for Data Clustering
Authors: Jan Feyereisl, Uwe Aickelin
Date: 2013-05-31 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

Many machine learning algorithms assume that all input samples are
independently and identically distributed from some common distribution on
either the input space X, in the case of unsupervised learning, or the input
and output space X x Y in the case of supervised and semi-supervised learning.
In the last number of years the relaxation of this assumption has been explored
and the importance of incorporation of additional information within machine
learning algorithms became more apparent. Traditionally such fusion of
information was the domain of semi-supervised learning. More recently the
inclusion of knowledge from separate hypothetical spaces has been proposed by
Vapnik as part of the supervised setting. In this work we are interested in
exploring Vapnik's idea of master-class learning and the associated learning
using privileged information, however within the unsupervised setting. Adoption
of the advanced supervised learning paradigm for the unsupervised setting
instigates investigation into the difference between privileged and technical
data. By means of our proposed aRi-MAX method stability of the KMeans algorithm
is improved and identification of the best clustering solution is achieved on
an artificial dataset. Subsequently an information theoretic dot product based
algorithm called P-Dot is proposed. This method has the ability to utilize a
wide variety of clustering techniques, individually or in combination, while
fusing privileged and technical data for improved clustering. Application of
the P-Dot method to the task of digit recognition confirms our findings in a
real-world scenario.
Robustness Analysis of Hottopixx, a Linear Programming Model for Factoring Nonnegative Matrices
Authors: Nicolas Gillis
Date: 2012-11-28 00:00:00

Although nonnegative matrix factorization (NMF) is NP-hard in general, it has
been shown very recently that it is tractable under the assumption that the
input nonnegative data matrix is close to being separable (separability
requires that all columns of the input matrix belongs to the cone spanned by a
small subset of these columns). Since then, several algorithms have been
designed to handle this subclass of NMF problems. In particular, Bittorf,
Recht, R\'e and Tropp (`Factoring nonnegative matrices with linear programs',
NIPS 2012) proposed a linear programming model, referred to as Hottopixx. In
this paper, we provide a new and more general robustness analysis of their
method. In particular, we design a provably more robust variant using a
post-processing strategy which allows us to deal with duplicates and near
duplicates in the dataset.
Modelling Electricity Consumption in Office Buildings: An Agent Based Approach
Authors: Tao Zhang, Peer-Olaf Siebers, Uwe Aickelin
Date: 2013-05-31 00:00:00
Tasks: energy management, Management

In this paper, we develop an agent-based model which integrates four
important elements, i.e. organisational energy management policies/regulations,
energy management technologies, electric appliances and equipment, and human
behaviour, to simulate the electricity consumption in office buildings. Based
on a case study, we use this model to test the effectiveness of different
electricity management strategies, and solve practical office electricity
consumption problems. This paper theoretically contributes to an integration of
the four elements involved in the complex organisational issue of office
electricity consumption, and practically contributes to an application of an
agent-based approach for office building electricity consumption study.
Motif Detection Inspired by Immune Memory (JORS)
Authors: William Wilson, Phil Birkin, Uwe Aickelin
Date: 2013-05-31 00:00:00
Tasks: Time Series, Time Series Analysis

The search for patterns or motifs in data represents an area of key interest
to many researchers. In this paper we present the Motif Tracking Algorithm, a
novel immune inspired pattern identification tool that is able to identify
variable length unknown motifs which repeat within time series data. The
algorithm searches from a neutral perspective that is independent of the data
being analysed and the underlying motifs. In this paper we test the flexibility
of the motif tracking algorithm by applying it to the search for patterns in
two industrial data sets. The algorithm is able to identify a population of
meaningful motifs in both cases, and the value of these motifs is discussed.
Real-world Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms
Authors: Amanda Whitbrook, Uwe Aickelin, Jonathan M. Garibaldi
Date: 2013-05-31 00:00:00
Tasks: Reinforcement Learning

In mobile robotics, a solid test for adaptation is the ability of a control
system to function not only in a diverse number of physical environments, but
also on a number of different robotic platforms. This paper demonstrates that a
set of behaviours evolved in simulation on a miniature robot (epuck) can be
transferred to a much larger-scale platform (Pioneer), both in simulation and
in the real world. The chosen architecture uses artificial evolution of epuck
behaviours to obtain a genetic sequence, which is then employed to seed an
idiotypic, artificial immune system (AIS) on the Pioneers. Despite numerous
hardware and software differences between the platforms, navigation and
target-finding experiments show that the evolved behaviours transfer very well
to the larger robot when the idiotypic AIS technique is used. In contrast,
transferability is poor when reinforcement learning alone is used, which
validates the adaptability of the chosen architecture.
The Dendritic Cell Algorithm for Intrusion Detection
Authors: Feng Gu, Julie Greensmith, Uwe Aickelin
Date: 2013-05-31 00:00:00
Tasks: Intrusion Detection

As one of the solutions to intrusion detection problems, Artificial Immune
Systems (AIS) have shown their advantages. Unlike genetic algorithms, there is
no one archetypal AIS, instead there are four major paradigms. Among them, the
Dendritic Cell Algorithm (DCA) has produced promising results in various
applications. The aim of this chapter is to demonstrate the potential for the
DCA as a suitable candidate for intrusion detection problems. We review some of
the commonly used AIS paradigms for intrusion detection problems and
demonstrate the advantages of one particular algorithm, the DCA. In order to
clearly describe the algorithm, the background to its development and a formal
definition are given. In addition, improvements to the original DCA are
presented and their implications are discussed, including previous work done on
an online analysis component with segmentation and ongoing work on automated
data preprocessing. Based on preliminary results, both improvements appear to
be promising for online anomaly-based intrusion detection.
Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data
Authors: Saumyadipta Pyne, Kui Wang, Jonathan Irish, Pablo Tamayo, Marc-Danie Nazaire, Tarn Duong, Sharon Lee, Shu-Kay Ng, David Hafler, Ronald Levy, Garry Nolan, Jill Mesirov, Geoffrey J. McLachlan
Date: 2013-05-31 00:00:00
Tasks: Clustering

In systems biomedicine, an experimenter encounters different potential
sources of variation in data such as individual samples, multiple experimental
conditions, and multi-variable network-level responses. In multiparametric
cytometry, which is often used for analyzing patient samples, such issues are
critical. While computational methods can identify cell populations in
individual samples, without the ability to automatically match them across
samples, it is difficult to compare and characterize the populations in typical
experiments, such as those responding to various stimulations or distinctive of
particular patients or time-points, especially when there are many samples.
Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous
modeling and registration of populations across a cohort. JCM models every
population with a robust multivariate probability distribution. Simultaneously,
JCM fits a random-effects model to construct an overall batch template -- used
for registering populations across samples, and classifying new samples. By
tackling systems-level variation, JCM supports practical biomedical
applications involving large cohorts.
Stochastic dynamics of lexicon learning in an uncertain and nonuniform world
Authors: Rainer Reisenauer, Kenny Smith, Richard A. Blythe
Date: 2013-02-22 00:00:00

We study the time taken by a language learner to correctly identify the
meaning of all words in a lexicon under conditions where many plausible
meanings can be inferred whenever a word is uttered. We show that the most
basic form of cross-situational learning - whereby information from multiple
episodes is combined to eliminate incorrect meanings - can perform badly when
words are learned independently and meanings are drawn from a nonuniform
distribution. If learners further assume that no two words share a common
meaning, we find a phase transition between a maximally-efficient learning
regime, where the learning time is reduced to the shortest it can possibly be,
and a partially-efficient regime where incorrect candidate meanings for words
persist at late times. We obtain exact results for the word-learning process
through an equivalence to a statistical mechanical problem of enumerating loops
in the space of word-meaning mappings.
Robust Hyperspectral Unmixing with Correntropy based Metric
Authors: Ying Wang, Chunhong Pan, Shiming Xiang, Feiyun Zhu
Date: 2013-05-31 00:00:00
Tasks: Hyperspectral Unmixing

Hyperspectral unmixing is one of the crucial steps for many hyperspectral
applications. The problem of hyperspectral unmixing has proven to be a
difficult task in unsupervised work settings where the endmembers and
abundances are both unknown. What is more, this task becomes more challenging
in the case that the spectral bands are degraded with noise. This paper
presents a robust model for unsupervised hyperspectral unmixing. Specifically,
our model is developed with the correntropy based metric where the non-negative
constraints on both endmembers and abundances are imposed to keep physical
significance. In addition, a sparsity prior is explicitly formulated to
constrain the distribution of the abundances of each endmember. To solve our
model, a half-quadratic optimization technique is developed to convert the
original complex optimization problem into an iteratively re-weighted NMF with
sparsity constraints. As a result, the optimization of our model can adaptively
assign small weights to noisy bands and give more emphasis on noise-free bands.
In addition, with sparsity constraints, our model can naturally generate sparse
abundances. Experiments on synthetic and real data demonstrate the
effectiveness of our model in comparison to the related state-of-the-art
unmixing models.
Non-linear dimensionality reduction: Riemannian metric estimation and the problem of geometric discovery
Authors: Dominique Perraul-Joncas, Marina Meila
Date: 2013-05-30 00:00:00
Tasks: Dimensionality Reduction

In recent years, manifold learning has become increasingly popular as a tool
for performing non-linear dimensionality reduction. This has led to the
development of numerous algorithms of varying degrees of complexity that aim to
recover man ifold geometry using either local or global features of the data.
  Building on the Laplacian Eigenmap and Diffusionmaps framework, we propose a
new paradigm that offers a guarantee, under reasonable assumptions, that any
manifo ld learning algorithm will preserve the geometry of a data set. Our
approach is based on augmenting the output of embedding algorithms with
geometric informatio n embodied in the Riemannian metric of the manifold. We
provide an algorithm for estimating the Riemannian metric from data and
demonstrate possible application s of our approach in a variety of examples.
Harmony search to solve the container storage problem with different container types
Authors: I. Ayachi, R. Kammarti, M. Ksouri, P. Borne LACS, ENIT, Tunis-Belvedere Tunisie LAGIS, ECL, Villeneuve d Ascq, France
Date: 2013-05-30 00:00:00

This paper presents an adaptation of the harmony search algorithm to solve
the storage allocation problem for inbound and outbound containers. This
problem is studied considering multiple container type (regular, open side,
open top, tank, empty and refrigerated) which lets the situation more
complicated, as various storage constraints appeared. The objective is to find
an optimal container arrangement which respects their departure dates, and
minimize the re-handle operations of containers. The performance of the
proposed approach is verified comparing to the results generated by genetic
algorithm and LIFO algorithm.
Image restoration using sparse approximations of spatially varying blur operators in the wavelet domain
Authors: Paul Escande, Pierre Weiss, Francois Malgouyres
Date: 2013-02-25 00:00:00
Tasks: Image Restoration

Restoration of images degraded by spatially varying blurs is an issue of
increasing importance in the context of photography, satellite or microscopy
imaging. One of the main difficulty to solve this problem comes from the huge
dimensions of the blur matrix. It prevents the use of naive approaches for
performing matrix-vector multiplications. In this paper, we propose to
approximate the blur operator by a matrix sparse in the wavelet domain. We
justify this approach from a mathematical point of view and investigate the
approximation quality numerically. We finish by showing that the sparsity
pattern of the matrix can be pre-defined, which is central in tasks such as
blind deconvolution.
Organizing Linked Data Quality Related Methods
Authors: Philippe A. Martin
Date: 2013-05-30 00:00:00
Tasks: General Knowledge

This article presents the top-level of an ontology categorizing and
generalizing best practices and quality criteria or measures for Linked Data.
It permits to compare these techniques and have a synthetic organized view of
what can or should be done for knowledge sharing purposes. This ontology is
part of a general knowledge base that can be accessed and complemented by any
Web user. Thus, it can be seen as a cooperatively built library for the above
cited elements. Since they permit to evaluate information objects and create
better ones, these elements also permit knowledge-based tools and techniques -
as well as knowledge providers - to be evaluated and categorized based on their
input/output information objects. One top-level distinction permitting to
organize this ontology is the one between content, medium and containers of
descriptions. Various structural, ontological, syntactical and lexical
distinctions are then used.
For a Semantic Web based Peer-reviewing and Publication of Research Results
Authors: Philippe A. Martin
Date: 2013-05-30 00:00:00
Tasks: Articles

This article shows why the diffusion and peer-reviewing of research results
would be more efficient, precise and relevant if all or at least some parts of
the descriptions and peer-reviews of research results took the form of a
fine-grained semantic network, within articles or knowledge bases, as part of
the Semantic Web. This article also shows some ways this can be done and hence
how research journal/proceeding publishers could allow this. So far, the World
Wide Web Consortium (W3C) has not proposed simple notations and cooperation
protocols - similar to those illustrated or referred to in this article - but
it now seems likely that Wikipedia/Wikidata, Google or the W3C will propose
them sooner or later. Then, research journal/proceeding publishers and
researchers may or may not quickly use this approach.
Model Selection for Degree-corrected Block Models
Authors: Xiaoran Yan, Cosma Rohilla Shalizi, Jacob E. Jensen, Florent Krzakala, Cristopher Moore, Lenka Zdeborova, Pan Zhang, Yaojia Zhu
Date: 2012-07-17 00:00:00
Tasks: model, Model Selection, Stochastic Block Model

The proliferation of models for networks raises challenging problems of model
selection: the data are sparse and globally dependent, and models are typically
high-dimensional and have large numbers of latent variables. Together, these
issues mean that the usual model-selection criteria do not work properly for
networks. We illustrate these challenges, and show one way to resolve them, by
considering the key network-analysis problem of dividing a graph into
communities or blocks of nodes with homogeneous patterns of links to the rest
of the network. The standard tool for doing this is the stochastic block model,
under which the probability of a link between two nodes is a function solely of
the blocks to which they belong. This imposes a homogeneous degree distribution
within each block; this can be unrealistic, so degree-corrected block models
add a parameter for each node, modulating its over-all degree. The choice
between ordinary and degree-corrected block models matters because they make
very different inferences about communities. We present the first principled
and tractable approach to model selection between standard and degree-corrected
block models, based on new large-graph asymptotics for the distribution of
log-likelihood ratios under the stochastic block model, finding substantial
departures from classical results for sparse graphs. We also develop
linear-time approximations for log-likelihoods under both the stochastic block
model and the degree-corrected model, using belief propagation. Applications to
simulated and real networks show excellent agreement with our approximations.
Our results thus both solve the practical problem of deciding on degree
correction, and point to a general approach to model selection in network
analysis.