pwc-archive/papers-with-abstracts huggingface dataset:
Using novelty-biased GA to sample diversity in graphs satisfying constraints
Authors: Peter Overbury, Luc Berthouze
Date: 2015-03-21 00:00:00
Tasks: Diversity

The structure of the network underlying many complex systems, whether
artificial or natural, plays a significant role in how these systems operate.
As a result, much emphasis has been placed on accurately describing networks
using network theoretic metrics. When it comes to generating networks with
similar properties, however, the set of available techniques and properties
that can be controlled for remains limited. Further, whilst it is becoming
clear that some of the metrics currently used to control the generation of such
networks are not very prescriptive so that networks could potentially exhibit
very different higher-order structure within those constraints, network
generating algorithms typically produce fairly contrived networks and lack
mechanisms by which to systematically explore the space of network solutions.
In this paper, we explore the potential of a multi-objective novelty-biased GA
to provide a viable alternative to these algorithms. We believe our results
provide the first proof of principle that (i) it is possible to use GAs to
generate graphs satisfying set levels of key classical graph theoretic
properties and (ii) it is possible to generate diverse solutions within these
constraints. The paper is only a preliminary step, however, and we identify key
avenues for further development.
$\ell_p$ Testing and Learning of Discrete Distributions
Authors: Bo Waggoner
Date: 2014-12-07 00:00:00
Tasks: Fairness

The classic problems of testing uniformity of and learning a discrete
distribution, given access to independent samples from it, are examined under
general $\ell_p$ metrics. The intuitions and results often contrast with the
classic $\ell_1$ case. For $p > 1$, we can learn and test with a number of
samples that is independent of the support size of the distribution: With an
$\ell_p$ tolerance $\epsilon$, $O(\max\{ \sqrt{1/\epsilon^q}, 1/\epsilon^2 \})$
samples suffice for testing uniformity and $O(\max\{ 1/\epsilon^q,
1/\epsilon^2\})$ samples suffice for learning, where $q=p/(p-1)$ is the
conjugate of $p$. As this parallels the intuition that $O(\sqrt{n})$ and $O(n)$
samples suffice for the $\ell_1$ case, it seems that $1/\epsilon^q$ acts as an
upper bound on the "apparent" support size.
  For some $\ell_p$ metrics, uniformity testing becomes easier over larger
supports: a 6-sided die requires fewer trials to test for fairness than a
2-sided coin, and a card-shuffler requires fewer trials than the die. In fact,
this inverse dependence on support size holds if and only if $p > \frac{4}{3}$.
The uniformity testing algorithm simply thresholds the number of "collisions"
or "coincidences" and has an optimal sample complexity up to constant factors
for all $1 \leq p \leq 2$. Another algorithm gives order-optimal sample
complexity for $\ell_{\infty}$ uniformity testing. Meanwhile, the most natural
learning algorithm is shown to have order-optimal sample complexity for all
$\ell_p$ metrics.
  The author thanks Cl\'{e}ment Canonne for discussions and contributions to
this work.
Wavelet based approach for tissue fractal parameter measurement: Pre cancer detection
Authors: Sabyasachi Mukhopadhyay, Nandan K. Das, Soham Mandal, Sawon Pratiher, Asish Mitra, Asima Pradhan, Nirmalya Ghosh, Prasanta K. Panigrahi
Date: 2015-03-21 00:00:00

In this paper, we have carried out the detail studies of pre-cancer by
wavelet coherency and multifractal based detrended fluctuation analysis (MFDFA)
on differential interference contrast (DIC) images of stromal region among
different grades of pre-cancer tissues. Discrete wavelet transform (DWT)
through Daubechies basis has been performed for identifying fluctuations over
polynomial trends for clear characterization and differentiation of tissues.
Wavelet coherence plots are performed for identifying the level of correlation
in time scale plane between normal and various grades of DIC samples. Applying
MFDFA on refractive index variations of cervical tissues, we have observed that
the values of Hurst exponent (correlation) decreases from healthy (normal) to
pre-cancer tissues. The width of singularity spectrum has a sudden degradation
at grade-I in comparison of healthy (normal) tissue but later on it increases
as cancer progresses from grade-II to grade-III.
Identifying Similar Patients Using Self-Organising Maps: A Case Study on Type-1 Diabetes Self-care Survey Responses
Authors: Santosh Tirunagari, Norman Poh, Guosheng Hu, David Windridge
Date: 2015-03-21 00:00:00

Diabetes is considered a lifestyle disease and a well managed self-care plays
an important role in the treatment. Clinicians often conduct surveys to
understand the self-care behaviors in their patients. In this context, we
propose to use Self-Organising Maps (SOM) to explore the survey data for
assessing the self-care behaviors in Type-1 diabetic patients. Specifically,
SOM is used to visualize high dimensional similar patient profiles, which is
rarely discussed. Experiments demonstrate that our findings through SOM
analysis corresponds well to the expectations of the clinicians. In addition,
our findings inspire the experts to improve their understanding of the
self-care behaviors for their patients. The principle findings in our study
show: 1) patients who take correct dose of insulin, inject insulin at the right
time, 2) patients who take correct food portions undertake regular physical
activity and 3) patients who eat on time take correct food portions.
Incorporating Both Distributional and Relational Semantics in Word Representations
Authors: Daniel Fried, Kevin Duh
Date: 2014-12-18 00:00:00
Tasks: Knowledge Base Completion

We investigate the hypothesis that word representations ought to incorporate
both distributional and relational semantics. To this end, we employ the
Alternating Direction Method of Multipliers (ADMM), which flexibly optimizes a
distributional objective on raw text and a relational objective on WordNet.
Preliminary results on knowledge base completion, analogy tests, and parsing
show that word representations trained on both objectives can give improvements
in some cases.
Incorporating Both Distributional and Relational Semantics in Word Representations
Authors: Daniel Fried, Kevin Duh
Date: 2014-12-14 00:00:00
Tasks: Knowledge Base Completion

We investigate the hypothesis that word representations ought to incorporate
both distributional and relational semantics. To this end, we employ the
Alternating Direction Method of Multipliers (ADMM), which flexibly optimizes a
distributional objective on raw text and a relational objective on WordNet.
Preliminary results on knowledge base completion, analogy tests, and parsing
show that word representations trained on both objectives can give improvements
in some cases.
Skin Detection of Animation Characters
Authors: Kazi Tanvir Ahmed Siddiqui, Abu Wasif
Date: 2015-03-21 00:00:00

The increasing popularity of animes makes it vulnerable to unwanted usages
like copyright violations and pornography. That is why, we need to develop a
method to detect and recognize animation characters. Skin detection is one of
the most important steps in this way. Though there are some methods to detect
human skin color, but those methods do not work properly for anime characters.
Anime skin varies greatly from human skin in color, texture, tone and in
different kinds of lighting. They also vary greatly among themselves. Moreover,
many other things (for example leather, shirt, hair etc.), which are not skin,
can have color similar to skin. In this paper, we have proposed three methods
that can identify an anime character skin more successfully as compared with
Kovac, Swift, Saleh and Osman methods, which are primarily designed for human
skin detection. Our methods are based on RGB values and their comparative
relations.
Hierarchical sparse Bayesian learning: theory and application for inferring structural damage from incomplete modal data
Authors: Yong Huang, James L. Beck
Date: 2015-03-21 00:00:00
Tasks: Learning Theory, Structural Health Monitoring

Structural damage due to excessive loading or environmental degradation
typically occurs in localized areas in the absence of collapse. This prior
information about the spatial sparseness of structural damage is exploited here
by a hierarchical sparse Bayesian learning framework with the goal of reducing
the source of ill-conditioning in the stiffness loss inversion problem for
damage detection. Sparse Bayesian learning methodologies automatically prune
away irrelevant or inactive features from a set of potential candidates, and so
they are effective probabilistic tools for producing sparse explanatory
subsets. We have previously proposed such an approach to establish the
probability of localized stiffness reductions that serve as a proxy for damage
by using noisy incomplete modal data from before and after possible damage. The
core idea centers on a specific hierarchical Bayesian model that promotes
spatial sparseness in the inferred stiffness reductions in a way that is
consistent with the Bayesian Ockham razor. In this paper, we improve the theory
of our previously proposed sparse Bayesian learning approach by eliminating an
approximation and, more importantly, incorporating a constraint on stiffness
increases. Our approach has many appealing features that are summarized at the
end of the paper. We validate the approach by applying it to the Phase II
simulated and experimental benchmark studies sponsored by the IASC-ASCE Task
Group on Structural Health Monitoring. The results show that it can reliably
detect, locate and assess damage by inferring substructure stiffness losses
from the identified modal parameters. The occurrence of missed and false damage
alerts is effectively suppressed.
A Randomized Nonmonotone Block Proximal Gradient Method for a Class of Structured Nonlinear Programming
Authors: Zhaosong Lu, Lin Xiao
Date: 2013-06-25 00:00:00

We propose a randomized nonmonotone block proximal gradient (RNBPG) method
for minimizing the sum of a smooth (possibly nonconvex) function and a
block-separable (possibly nonconvex nonsmooth) function. At each iteration,
this method randomly picks a block according to any prescribed probability
distribution and solves typically several associated proximal subproblems that
usually have a closed-form solution, until a certain progress on objective
value is achieved. In contrast to the usual randomized block coordinate descent
method [23,20], our method has a nonmonotone flavor and uses variable stepsizes
that can partially utilize the local curvature information of the smooth
component of objective function. We show that any accumulation point of the
solution sequence of the method is a stationary point of the problem {\it
almost surely} and the method is capable of finding an approximate stationary
point with high probability. We also establish a sublinear rate of convergence
for the method in terms of the minimal expected squared norm of certain
proximal gradients over the iterations. When the problem under consideration is
convex, we show that the expected objective values generated by RNBPG converge
to the optimal value of the problem. Under some assumptions, we further
establish a sublinear and linear rate of convergence on the expected objective
values generated by a monotone version of RNBPG. Finally, we conduct some
preliminary experiments to test the performance of RNBPG on the
$\ell_1$-regularized least-squares problem and a dual SVM problem in machine
learning. The computational results demonstrate that our method substantially
outperforms the randomized block coordinate {\it descent} method with fixed or
variable stepsizes.
Fast Imbalanced Classification of Healthcare Data with Missing Values
Authors: Talayeh Razzaghi, Oleg Roderick, Ilya Safro, Nick Marko
Date: 2015-03-21 00:00:00
Tasks: Classification, General Classification, imbalanced classification, Imputation, Missing Values, Robust classification

In medical domain, data features often contain missing values. This can
create serious bias in the predictive modeling. Typical standard data mining
methods often produce poor performance measures. In this paper, we propose a
new method to simultaneously classify large datasets and reduce the effects of
missing values. The proposed method is based on a multilevel framework of the
cost-sensitive SVM and the expected maximization imputation method for missing
values, which relies on iterated regression analyses. We compare classification
results of multilevel SVM-based algorithms on public benchmark datasets with
imbalanced classes and missing values as well as real data in health
applications, and show that our multilevel SVM-based method produces fast, and
more accurate and robust classification results.
Block-Wise MAP Inference for Determinantal Point Processes with Application to Change-Point Detection
Authors: Jinye Zhang, Zhijian Ou
Date: 2015-03-20 00:00:00
Tasks: Change Point Detection, Diversity, Point Processes

Existing MAP inference algorithms for determinantal point processes (DPPs)
need to calculate determinants or conduct eigenvalue decomposition generally at
the scale of the full kernel, which presents a great challenge for real-world
applications. In this paper, we introduce a class of DPPs, called BwDPPs, that
are characterized by an almost block diagonal kernel matrix and thus can allow
efficient block-wise MAP inference. Furthermore, BwDPPs are successfully
applied to address the difficulty of selecting change-points in the problem of
change-point detection (CPD), which results in a new BwDPP-based CPD method,
named BwDppCpd. In BwDppCpd, a preliminary set of change-point candidates is
first created based on existing well-studied metrics. Then, these change-point
candidates are treated as DPP items, and DPP-based subset selection is
conducted to give the final estimate of the change-points that favours both
quality and diversity. The effectiveness of BwDppCpd is demonstrated through
extensive experiments on five real-world datasets.
Explaining and Harnessing Adversarial Examples
Authors: Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy
Date: 2014-12-20 00:00:00
Tasks: Image Classification

Several machine learning models, including neural networks, consistently
misclassify adversarial examples---inputs formed by applying small but
intentionally worst-case perturbations to examples from the dataset, such that
the perturbed input results in the model outputting an incorrect answer with
high confidence. Early attempts at explaining this phenomenon focused on
nonlinearity and overfitting. We argue instead that the primary cause of neural
networks' vulnerability to adversarial perturbation is their linear nature.
This explanation is supported by new quantitative results while giving the
first explanation of the most intriguing fact about them: their generalization
across architectures and training sets. Moreover, this view yields a simple and
fast method of generating adversarial examples. Using this approach to provide
examples for adversarial training, we reduce the test set error of a maxout
network on the MNIST dataset.
Data Science as a New Frontier for Design
Authors: Akin Osman, Kazakçi Mines
Date: 2015-03-20 00:00:00

The purpose of this paper is to contribute to the challenge of transferring
know-how, theories and methods from design research to the design processes in
information science and technologies. More specifically, we shall consider a
domain, namely data-science, that is becoming rapidly a globally invested
research and development axis with strong imperatives for innovation given the
data deluge we are currently facing. We argue that, in order to rise to the
data-related challenges that the society is facing, data-science initiatives
should ensure a renewal of traditional research methodologies that are still
largely based on trial-error processes depending on the talent and insights of
a single (or a restricted group of) researchers. It is our claim that design
theories and methods can provide, at least to some extent, the much-needed
framework. We will use a worldwide data-science challenge organized to study a
technical problem in physics, namely the detection of Higgs boson, as a use
case to demonstrate some of the ways in which design theory and methods can
help in analyzing and shaping the innovation dynamics in such projects.
Networked Stochastic Multi-Armed Bandits with Combinatorial Strategies
Authors: Shaojie Tang, Yaqin Zhou
Date: 2015-03-20 00:00:00
Tasks: Multi-Armed Bandits

In this paper, we investigate a largely extended version of classical MAB
problem, called networked combinatorial bandit problems. In particular, we
consider the setting of a decision maker over a networked bandits as follows:
each time a combinatorial strategy, e.g., a group of arms, is chosen, and the
decision maker receives a reward resulting from her strategy and also receives
a side bonus resulting from that strategy for each arm's neighbor. This is
motivated by many real applications such as on-line social networks where
friends can provide their feedback on shared content, therefore if we promote a
product to a user, we can also collect feedback from her friends on that
product. To this end, we consider two types of side bonus in this study: side
observation and side reward. Upon the number of arms pulled at each time slot,
we study two cases: single-play and combinatorial-play. Consequently, this
leaves us four scenarios to investigate in the presence of side bonus:
Single-play with Side Observation, Combinatorial-play with Side Observation,
Single-play with Side Reward, and Combinatorial-play with Side Reward. For each
case, we present and analyze a series of \emph{zero regret} polices where the
expect of regret over time approaches zero as time goes to infinity. Extensive
simulations validate the effectiveness of our results.
Pragmatic Neural Language Modelling in Machine Translation
Authors: Paul Baltescu, Phil Blunsom
Date: 2014-12-22 00:00:00
Proceeding: HLT 2015 5
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

This paper presents an in-depth investigation on integrating neural language
models in translation systems. Scaling neural language models is a difficult
task, but crucial for real-world applications. This paper evaluates the impact
on end-to-end MT quality of both new and existing scaling techniques. We show
when explicitly normalising neural models is necessary and what optimisation
tricks one should use in such scenarios. We also focus on scalable training
algorithms and investigate noise contrastive estimation and diagonal contexts
as sources for further speed improvements. We explore the trade-offs between
neural models and back-off n-gram models and find that neural models make
strong candidates for natural language applications in memory constrained
environments, yet still lag behind traditional models in raw translation
quality. We conclude with a set of recommendations one should follow to build a
scalable neural language model for MT.
On measuring linguistic intelligence
Authors: Maxim Litvak
Date: 2015-03-20 00:00:00

This work addresses the problem of measuring how many languages a person
"effectively" speaks given that some of the languages are close to each other.
In other words, to assign a meaningful number to her language portfolio.
Intuition says that someone who speaks fluently Spanish and Portuguese is
linguistically less proficient compared to someone who speaks fluently Spanish
and Chinese since it takes more effort for a native Spanish speaker to learn
Chinese than Portuguese. As the number of languages grows and their proficiency
levels vary, it gets even more complicated to assign a score to a language
portfolio. In this article we propose such a measure ("linguistic quotient" -
LQ) that can account for these effects.
  We define the properties that such a measure should have. They are based on
the idea of coherent risk measures from the mathematical finance. Having laid
down the foundation, we propose one such a measure together with the algorithm
that works on languages classification tree as input.
  The algorithm together with the input is available online at lingvometer.com
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs
Authors: Yarin Gal, Richard Turner
Date: 2015-03-09 00:00:00
Tasks: Variational Inference

Standard sparse pseudo-input approximations to the Gaussian process (GP)
cannot handle complex functions well. Sparse spectrum alternatives attempt to
answer this but are known to over-fit. We suggest the use of variational
inference for the sparse spectrum approximation to avoid both issues. We model
the covariance function with a finite Fourier series approximation and treat it
as a random variable. The random covariance function has a posterior, on which
a variational distribution is placed. The variational distribution transforms
the random covariance function to fit the data. We study the properties of our
approximate inference, compare it to alternative ones, and extend it to the
distributed and stochastic domains. Our approximation captures complex
functions better than standard approaches and avoids over-fitting.
Country-scale Exploratory Analysis of Call Detail Records through the Lens of Data Grid Models
Authors: Romain Guigourès, Dominique Gay, Marc Boullé, Fabrice Clérot, Fabrice Rossi
Date: 2015-03-20 00:00:00

Call Detail Records (CDRs) are data recorded by telecommunications companies,
consisting of basic informations related to several dimensions of the calls
made through the network: the source, destination, date and time of calls. CDRs
data analysis has received much attention in the recent years since it might
reveal valuable information about human behavior. It has shown high added value
in many application domains like e.g., communities analysis or network
planning. In this paper, we suggest a generic methodology for summarizing
information contained in CDRs data. The method is based on a parameter-free
estimation of the joint distribution of the variables that describe the calls.
We also suggest several well-founded criteria that allows one to browse the
summary at various granularities and to explore the summary by means of
insightful visualizations. The method handles network graph data, temporal
sequence data as well as user mobility data stemming from original CDRs data.
We show the relevance of our methodology for various case studies on real-world
CDRs data from Ivory Coast.
Deep Transform: Cocktail Party Source Separation via Probabilistic Re-Synthesis
Authors: Andrew J. R. Simpson
Date: 2015-03-20 00:00:00

In cocktail party listening scenarios, the human brain is able to separate
competing speech signals. However, the signal processing implemented by the
brain to perform cocktail party listening is not well understood. Here, we
trained two separate convolutive autoencoder deep neural networks (DNN) to
separate monaural and binaural mixtures of two concurrent speech streams. We
then used these DNNs as convolutive deep transform (CDT) devices to perform
probabilistic re-synthesis. The CDTs operated directly in the time-domain. Our
simulations demonstrate that very simple neural networks are capable of
exploiting monaural and binaural information available in a cocktail party
listening scenario.
Feeder Load Balancing using Neural Network
Authors: A. Ukil, W. Siti, J. Jordaan
Date: 2015-03-20 00:00:00
Tasks: Combinatorial Optimization

The distribution system problems, such as planning, loss minimization, and
energy restoration, usually involve the phase balancing or network
reconfiguration procedures. The determination of an optimal phase balance is,
in general, a combinatorial optimization problem. This paper proposes optimal
reconfiguration of the phase balancing using the neural network, to switch on
and off the different switches, allowing the three phases supply by the
transformer to the end-users to be balanced. This paper presents the
application examples of the proposed method using the real and simulated test
data.