pwc-archive/papers-with-abstracts huggingface dataset:
Collective Mind: cleaning up the research and experimentation mess in computer engineering using crowdsourcing, big data and machine learning
Authors: Grigori Fursin
Date: 2013-08-11 00:00:00
Tasks: Problem Decomposition

Software and hardware co-design and optimization of HPC systems has become
intolerably complex, ad-hoc, time consuming and error prone due to enormous
number of available design and optimization choices, complex interactions
between all software and hardware components, and multiple strict requirements
placed on performance, power consumption, size, reliability and cost. We
present our novel long-term holistic and practical solution to this problem
based on customizable, plugin-based, schema-free, heterogeneous, open-source
Collective Mind repository and infrastructure with unified web interfaces and
on-line advise system. This collaborative framework distributes analysis and
multi-objective off-line and on-line auto-tuning of computer systems among many
participants while utilizing any available smart phone, tablet, laptop, cluster
or data center, and continuously observing, classifying and modeling their
realistic behavior. Any unexpected behavior is analyzed using shared data
mining and predictive modeling plugins or exposed to the community at
cTuning.org for collaborative explanation, top-down complexity reduction,
incremental problem decomposition and detection of correlating program,
architecture or run-time properties (features). Gradually increasing
optimization knowledge helps to continuously improve optimization heuristics of
any compiler, predict optimizations for new programs or suggest efficient
run-time (online) tuning and adaptation strategies depending on end-user
requirements. We decided to share all our past research artifacts including
hundreds of codelets, numerical applications, data sets, models, universal
experimental analysis and auto-tuning pipelines, self-tuning machine learning
based meta compiler, and unified statistical analysis and machine learning
plugins in a public repository to initiate systematic, reproducible and
collaborative research, development and experimentation with a new publication
model where experiments and techniques are validated, ranked and improved by
the community.
CDfdr: A Comparison Density Approach to Local False Discovery Rate Estimation
Authors: Subhadeep Mukhopadhyay
Date: 2013-08-11 00:00:00

Efron et al. (2001) proposed empirical Bayes formulation of the frequentist
Benjamini and Hochbergs False Discovery Rate method (Benjamini and
Hochberg,1995). This article attempts to unify the `two cultures' using
concepts of comparison density and distribution function. We have also shown
how almost all of the existing local fdr methods can be viewed as proposing
various model specification for comparison density - unifies the vast
literature of false discovery methods under one concept and notation.
A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module
Authors: Francesco Bonanno, Giacomo Capizzi, Christian Napoli, Giorgio Graditi, Giuseppe Marco Tina
Date: 2013-08-11 00:00:00

The design process of photovoltaic (PV) modules can be greatly enhanced by
using advanced and accurate models in order to predict accurately their
electrical output behavior. The main aim of this paper is to investigate the
application of an advanced neural network based model of a module to improve
the accuracy of the predicted output I--V and P--V curves and to keep in
account the change of all the parameters at different operating conditions.
Radial basis function neural networks (RBFNN) are here utilized to predict the
output characteristic of a commercial PV module, by reading only the data of
solar irradiation and temperature. A lot of available experimental data were
used for the training of the RBFNN, and a backpropagation algorithm was
employed. Simulation and experimental validation is reported.
Exploratory Analysis of Highly Heterogeneous Document Collections
Authors: Arun S. Maiya, John P. Thompson, Francisco Loaiza-Lemos, Robert M. Rolfe
Date: 2013-08-11 00:00:00
Tasks: Articles, Keyword Extraction

We present an effective multifaceted system for exploratory analysis of
highly heterogeneous document collections. Our system is based on intelligently
tagging individual documents in a purely automated fashion and exploiting these
tags in a powerful faceted browsing framework. Tagging strategies employed
include both unsupervised and supervised approaches based on machine learning
and natural language processing. As one of our key tagging strategies, we
introduce the KERA algorithm (Keyword Extraction for Reports and Articles).
KERA extracts topic-representative terms from individual documents in a purely
unsupervised fashion and is revealed to be significantly more effective than
state-of-the-art methods. Finally, we evaluate our system in its ability to
help users locate documents pertaining to military critical technologies buried
deep in a large heterogeneous sea of information.
Learning Features and their Transformations by Spatial and Temporal Spherical Clustering
Authors: Jayanta K. Dutta, Bonny Banerjee
Date: 2013-08-10 00:00:00
Tasks: Clustering

Learning features invariant to arbitrary transformations in the data is a
requirement for any recognition system, biological or artificial. It is now
widely accepted that simple cells in the primary visual cortex respond to
features while the complex cells respond to features invariant to different
transformations. We present a novel two-layered feedforward neural model that
learns features in the first layer by spatial spherical clustering and
invariance to transformations in the second layer by temporal spherical
clustering. Learning occurs in an online and unsupervised manner following the
Hebbian rule. When exposed to natural videos acquired by a camera mounted on a
cat's head, the first and second layer neurons in our model develop simple and
complex cell-like receptive field properties. The model can predict by learning
lateral connections among the first layer neurons. A topographic map to their
spatial features emerges by exponentially decaying the flow of activation with
distance from one neuron to another in the first layer that fire in close
temporal proximity, thereby minimizing the pooling length in an online manner
simultaneously with feature learning.
Applying the Negative Selection Algorithm for Merger and Acquisition Target Identification
Authors: Satyakama Paul, Andreas Janecek, Fernando Buarque de Lima Neto, Tshilidzi Marwala
Date: 2013-08-10 00:00:00

In this paper, we propose a new methodology based on the Negative Selection
Algorithm that belongs to the field of Computational Intelligence,
specifically, Artificial Immune Systems to identify takeover targets. Although
considerable research based on customary statistical techniques and some
contemporary Computational Intelligence techniques have been devoted to
identify takeover targets, most of the existing studies are based upon multiple
previous mergers and acquisitions. Contrary to previous research, the novelty
of this proposal lies in its ability to suggest takeover targets for novice
firms that are at the beginning of their merger and acquisition spree. We first
discuss the theoretical perspective and then provide a case study with details
for practical implementation, both capitalizing from unique generalization
capabilities of artificial immune systems algorithms.
Finite Element Model Updating Using Fish School Search Optimization Method
Authors: I. Boulkabeit, L. Mthembu, T. Marwala, F. De Lima Neto
Date: 2013-08-10 00:00:00
Tasks: Nature-Inspired Optimization Algorithm

A recent nature inspired optimization algorithm, Fish School Search (FSS) is
applied to the finite element model (FEM) updating problem. This method is
tested on a GARTEUR SM-AG19 aeroplane structure. The results of this algorithm
are compared with two other metaheuristic algorithms; Genetic Algorithm (GA)
and Particle Swarm Optimization (PSO). It is observed that on average, the FSS
and PSO algorithms give more accurate results than the GA. A minor modification
to the FSS is proposed. This modification improves the performance of FSS on
the FEM updating problem which has a constrained search space.
Second order scattering descriptors predict fMRI activity due to visual textures
Authors: Michael Eickenberg, Fabian Pedregosa, Senoussi Mehdi, Alexandre Gramfort, Bertrand Thirion
Date: 2013-08-10 00:00:00
Tasks: General Classification, Sensitivity

Second layer scattering descriptors are known to provide good classification
performance on natural quasi-stationary processes such as visual textures due
to their sensitivity to higher order moments and continuity with respect to
small deformations. In a functional Magnetic Resonance Imaging (fMRI)
experiment we present visual textures to subjects and evaluate the predictive
power of these descriptors with respect to the predictive power of simple
contour energy - the first scattering layer. We are able to conclude not only
that invariant second layer scattering coefficients better encode voxel
activity, but also that well predicted voxels need not necessarily lie in known
retinotopic regions.
Fast image segmentation and restoration using parametric curve evolution with junctions and topology changes
Authors: Heike Benninghoff, Harald Garcke
Date: 2013-08-10 00:00:00
Tasks: Denoising, Image Segmentation, Segmentation, Semantic Segmentation

Curve evolution schemes for image segmentation based on a region based
contour model allowing for junctions, vector-valued images and topology changes
are introduced. Together with an a posteriori denoising in the segmented
homogeneous regions this leads to a fast and efficient method for image
segmentation and restoration. An uneven spread of mesh points is avoided by
using the tangential degrees of freedom. Several numerical simulations on
artificial test problems and on real images illustrate the performance of the
method.
Supplement to "Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs"
Authors: Yangbo He, Jinzhu Jia, Bin Yu
Date: 2013-03-04 00:00:00

This supplementary material includes three parts: some preliminary results,
four examples, an experiment, three new algorithms, and all proofs of the
results in the paper "Reversible MCMC on Markov equivalence classes of sparse
directed acyclic graphs".
Precisely Verifying the Null Space Conditions in Compressed Sensing: A Sandwiching Algorithm
Authors: Myung Cho, Weiyu Xu
Date: 2013-06-11 00:00:00
Tasks: compressed sensing

In this paper, we propose new efficient algorithms to verify the null space
condition in compressed sensing (CS). Given an $(n-m) \times n$ ($m>0$) CS
matrix $A$ and a positive $k$, we are interested in computing $\displaystyle
\alpha_k = \max_{\{z: Az=0,z\neq 0\}}\max_{\{K: |K|\leq k\}}$ ${\|z_K
\|_{1}}{\|z\|_{1}}$, where $K$ represents subsets of $\{1,2,...,n\}$, and $|K|$
is the cardinality of $K$. In particular, we are interested in finding the
maximum $k$ such that $\alpha_k < {1}{2}$. However, computing $\alpha_k$ is
known to be extremely challenging. In this paper, we first propose a series of
new polynomial-time algorithms to compute upper bounds on $\alpha_k$. Based on
these new polynomial-time algorithms, we further design a new sandwiching
algorithm, to compute the \emph{exact} $\alpha_k$ with greatly reduced
complexity. When needed, this new sandwiching algorithm also achieves a smooth
tradeoff between computational complexity and result accuracy. Empirical
results show the performance improvements of our algorithm over existing known
methods; and our algorithm outputs precise values of $\alpha_k$, with much
lower complexity than exhaustive search.
Cognitive residues of similarity
Authors: Stephanie OToole, Mark T. Keane
Date: 2013-08-09 00:00:00
Tasks: Object

What are the cognitive after-effects of making a similarity judgement? What,
cognitively, is left behind and what effect might these residues have on
subsequent processing? In this paper, we probe for such after-effects using a
visual search task, performed after a task in which pictures of real-world
objects were compared. So, target objects were first presented in a comparison
task (e.g., rate the similarity of this object to another) thus, presumably,
modifying some of their features before asking people to visually search for
the same object in complex scenes (with distractors and camouflaged
backgrounds). As visual search is known to be influenced by the features of
target objects, then any after-effects of the comparison task should be
revealed in subsequent visual searches. Results showed that when people
previously rated an object as being high on a scale (e.g., colour similarity or
general similarity) then visual search is inhibited (slower RTs and more
saccades in eye-tracking) relative to an object being rated as low in the same
scale. There was also some evidence that different comparison tasks (e.g.,
compare on colour or compare on general similarity) have differential effects
on visual search.
Surprise: Youve got some explaining to do
Authors: Meadhbh Foster, Mark T. Keane
Date: 2013-08-09 00:00:00

Why are some events more surprising than others? We propose that events that
are more difficult to explain are those that are more surprising. The two
experiments reported here test the impact of different event outcomes
(Outcome-Type) and task demands (Task) on ratings of surprise for simple story
scenarios. For the Outcome-Type variable, participants saw outcomes that were
either known or less-known surprising outcomes for each scenario. For the Task
variable, participants either answered comprehension questions or provided an
explanation of the outcome. Outcome-Type reliably affected surprise judgments;
known outcomes were rated as less surprising than less-known outcomes. Task
also reliably affected surprise judgments; when people provided an explanation
it lowered surprise judgments relative to simply answering comprehension
questions. Both experiments thus provide evidence on this less-explored
explanation aspect of surprise, specifically showing that ease of explanation
is a key factor in determining the level of surprise experienced.
Innovation networks
Authors: Petra Ahrweiler, Mark T. Keane
Date: 2013-08-09 00:00:00

This paper advances a framework for modeling the component interactions
between cognitive and social aspects of scientific creativity and technological
innovation. Specifically, it aims to characterize Innovation Networks; those
networks that involve the interplay of people, ideas and organizations to
create new, technologically feasible, commercially-realizable products,
processes and organizational structures. The tri-partite framework captures
networks of ideas (Concept Level), people (Individual Level) and social
structures (Social-Organizational Level) and the interactions between these
levels. At the concept level, new ideas are the nodes that are created and
linked, kept open for further investigation or closed if solved by actors at
the individual or organizational levels. At the individual level, the nodes are
actors linked by shared worldviews (based on shared professional, educational,
experiential backgrounds) who are the builders of the concept level. At the
social-organizational level, the nodes are organizations linked by common
efforts on a given project (e.g., a company-university collaboration) that by
virtue of their intellectual property or rules of governance constrain the
actions of individuals (at the Individual Level) or ideas (at the Concept
Level). After describing this framework and its implications we paint a number
of scenarios to flesh out how it can be applied.
Coding for Random Projections
Authors: Ping Li, Michael Mitzenmacher, Anshumali Shrivastava
Date: 2013-08-09 00:00:00
Tasks: Information Retrieval, Quantization, Retrieval

The method of random projections has become very popular for large-scale
applications in statistical learning, information retrieval, bio-informatics
and other applications. Using a well-designed coding scheme for the projected
data, which determines the number of bits needed for each projected value and
how to allocate these bits, can significantly improve the effectiveness of the
algorithm, in storage cost as well as computational speed. In this paper, we
study a number of simple coding schemes, focusing on the task of similarity
estimation and on an application to training linear classifiers. We demonstrate
that uniform quantization outperforms the standard existing influential method
(Datar et. al. 2004). Indeed, we argue that in many cases coding with just a
small number of bits suffices. Furthermore, we also develop a non-uniform 2-bit
coding scheme that generally performs well in practice, as confirmed by our
experiments on training linear support vector machines (SVM).
Space as an invention of biological organisms
Authors: Alexander V. Terekhov, J. Kevin O'Regan
Date: 2013-08-09 00:00:00
Tasks: Philosophy

The question of the nature of space around us has occupied thinkers since the
dawn of humanity, with scientists and philosophers today implicitly assuming
that space is something that exists objectively. Here we show that this does
not have to be the case: the notion of space could emerge when biological
organisms seek an economic representation of their sensorimotor flow. The
emergence of spatial notions does not necessitate the existence of real
physical space, but only requires the presence of sensorimotor invariants
called `compensable' sensory changes. We show mathematically and then in
simulations that na\"ive agents making no assumptions about the existence of
space are able to learn these invariants and to build the abstract notion that
physicists call rigid displacement, which is independent of what is being
displaced. Rigid displacements may underly perception of space as an unchanging
medium within which objects are described by their relative positions. Our
findings suggest that the question of the nature of space, currently exclusive
to philosophy and physics, should also be addressed from the standpoint of
neuroscience and artificial intelligence.
Deconstructing analogy
Authors: Mark Keane
Date: 2013-08-09 00:00:00

Analogy has been shown to be important in many key cognitive abilities,
including learning, problem solving, creativity and language change. For
cognitive models of analogy, the fundamental computational question is how its
inherent complexity (its NP-hardness) is solved by the human cognitive system.
Indeed, different models of analogical processing can be categorized by the
simplification strategies they adopt to make this computational problem more
tractable. In this paper, I deconstruct several of these models in terms of the
simplification-strategies they use; a deconstruction that provides some
interesting perspectives on the relative differences between them. Later, I
consider whether any of these computational simplifications reflect the actual
strategies used by people and sketch a new cognitive model that tries to
present a closer fit to the psychological evidence.
Entropy landscape of solutions in the binary perceptron problem
Authors: Haiping Huang, K. Y. Michael Wong, Yoshiyuki Kabashima
Date: 2013-04-10 00:00:00
Tasks: Clustering

The statistical picture of the solution space for a binary perceptron is
studied. The binary perceptron learns a random classification of input random
patterns by a set of binary synaptic weights. The learning of this network is
difficult especially when the pattern (constraint) density is close to the
capacity, which is supposed to be intimately related to the structure of the
solution space. The geometrical organization is elucidated by the entropy
landscape from a reference configuration and of solution-pairs separated by a
given Hamming distance in the solution space. We evaluate the entropy at the
annealed level as well as replica symmetric level and the mean field result is
confirmed by the numerical simulations on single instances using the proposed
message passing algorithms. From the first landscape (a random configuration as
a reference), we see clearly how the solution space shrinks as more constraints
are added. From the second landscape of solution-pairs, we deduce the
coexistence of clustering and freezing in the solution space.
Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method
Authors: Cyril Voyant, Wani W. Tamas, Christophe Paoli, Aurélia Balu, Marc Muselli, Marie Laure Nivet, Gilles Notton
Date: 2013-08-08 00:00:00
Tasks: Time Series, Time Series Analysis

A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks
often used in TS modeling and forecasting. Because of its "black box" aspect,
many researchers refuse to use it. Moreover, the optimization (often based on
the exhaustive approach where "all" configurations are tested) and learning
phases of this artificial intelligence tool (often based on the
Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach
(exhaustively and local minima). These two tasks must be repeated depending on
the knowledge of each new problem studied, making the process, long, laborious
and not systematically robust. In this paper a pruning process is proposed.
This method allows, during the training phase, to carry out an inputs selecting
method activating (or not) inter-nodes connections in order to verify if
forecasting is improved. We propose to use iteratively the popular damped
least-squares method to activate inputs and neurons. A first pass is applied to
10% of the learning sample to determine weights significantly different from 0
and delete other. Then a classical batch process based on LMA is used with the
new MLP. The validation is done using 25 measured meteorological TS and
cross-comparing the prediction results of the classical LMA and the 2-stage
LMA.
Local Space-Time Smoothing for Version Controlled Documents
Authors: Seungyeon Kim, Guy Lebanon
Date: 2010-03-06 00:00:00

Unlike static documents, version controlled documents are continuously edited
by one or more authors. Such collaborative revision process makes traditional
modeling and visualization techniques inappropriate. In this paper we propose a
new representation based on local space-time smoothing that captures important
revision patterns. We demonstrate the applicability of our framework using
experiments on synthetic and real-world data.
Beyond Sentiment: The Manifold of Human Emotions
Authors: Seungyeon Kim, Fuxin Li, Guy Lebanon, Irfan Essa
Date: 2012-02-08 00:00:00
Tasks: Sentiment Analysis

Sentiment analysis predicts the presence of positive or negative emotions in
a text document. In this paper we consider higher dimensional extensions of the
sentiment concept, which represent a richer set of human emotions. Our approach
goes beyond previous work in that our model contains a continuous manifold
rather than a finite set of human emotions. We investigate the resulting model,
compare it to psychological observations, and explore its predictive
capabilities. Besides obtaining significant improvements over a baseline
without manifold, we are also able to visualize different notions of positive
sentiment in different domains.