pwc-archive/papers-with-abstracts huggingface dataset:
How essential are unstructured clinical narratives and information fusion to clinical trial recruitment?
Authors: Preethi Raghavan, James L. Chen, Eric Fosler-Lussier, Albert M. Lai
Date: 2015-02-13 00:00:00
Tasks: Prognosis

Electronic health records capture patient information using structured
controlled vocabularies and unstructured narrative text. While structured data
typically encodes lab values, encounters and medication lists, unstructured
data captures the physician's interpretation of the patient's condition,
prognosis, and response to therapeutic intervention. In this paper, we
demonstrate that information extraction from unstructured clinical narratives
is essential to most clinical applications. We perform an empirical study to
validate the argument and show that structured data alone is insufficient in
resolving eligibility criteria for recruiting patients onto clinical trials for
chronic lymphocytic leukemia (CLL) and prostate cancer. Unstructured data is
essential to solving 59% of the CLL trial criteria and 77% of the prostate
cancer trial criteria. More specifically, for resolving eligibility criteria
with temporal constraints, we show the need for temporal reasoning and
information integration with medical events within and across unstructured
clinical narratives and structured data.
Abstract Learning via Demodulation in a Deep Neural Network
Authors: Andrew J. R. Simpson
Date: 2015-02-13 00:00:00

Inspired by the brain, deep neural networks (DNN) are thought to learn
abstract representations through their hierarchical architecture. However, at
present, how this happens is not well understood. Here, we demonstrate that DNN
learn abstract representations by a process of demodulation. We introduce a
biased sigmoid activation function and use it to show that DNN learn and
perform better when optimized for demodulation. Our findings constitute the
first unambiguous evidence that DNN perform abstract learning in practical use.
Our findings may also explain abstract learning in the human brain.
On Redundant Topological Constraints
Authors: Sanjiang Li, Zhiguo Long, Weiming Liu, Matt Duckham, Alan Both
Date: 2014-03-03 00:00:00
Tasks: Spatial Reasoning

The Region Connection Calculus (RCC) is a well-known calculus for
representing part-whole and topological relations. It plays an important role
in qualitative spatial reasoning, geographical information science, and
ontology. The computational complexity of reasoning with RCC5 and RCC8 (two
fragments of RCC) as well as other qualitative spatial/temporal calculi has
been investigated in depth in the literature. Most of these works focus on the
consistency of qualitative constraint networks. In this paper, we consider the
important problem of redundant qualitative constraints. For a set $\Gamma$ of
qualitative constraints, we say a constraint $(x R y)$ in $\Gamma$ is redundant
if it is entailed by the rest of $\Gamma$. A prime subnetwork of $\Gamma$ is a
subset of $\Gamma$ which contains no redundant constraints and has the same
solution set as $\Gamma$. It is natural to ask how to compute such a prime
subnetwork, and when it is unique.
  In this paper, we show that this problem is in general intractable, but
becomes tractable if $\Gamma$ is over a tractable subalgebra $\mathcal{S}$ of a
qualitative calculus. Furthermore, if $\mathcal{S}$ is a subalgebra of RCC5 or
RCC8 in which weak composition distributes over nonempty intersections, then
$\Gamma$ has a unique prime subnetwork, which can be obtained in cubic time by
removing all redundant constraints simultaneously from $\Gamma$. As a
byproduct, we show that any path-consistent network over such a distributive
subalgebra is weakly globally consistent and minimal. A thorough empirical
analysis of the prime subnetwork upon real geographical data sets demonstrates
the approach is able to identify significantly more redundant constraints than
previously proposed algorithms, especially in constraint networks with larger
proportions of partial overlap relations.
Deep Neural Programs for Adaptive Control in Cyber-Physical Systems
Authors: Konstantin Selyunin, Denise Ratasich, Ezio Bartocci, Radu Grosu
Date: 2015-02-13 00:00:00

We introduce Deep Neural Programs (DNP), a novel programming paradigm for
writing adaptive controllers for cy-ber-physical systems (CPS). DNP replace if
and while statements, whose discontinuity is responsible for undecidability in
CPS analysis, intractability in CPS design, and frailness in CPS
implementation, with their smooth, neural nif and nwhile counterparts. This not
only makes CPS analysis decidable and CPS design tractable, but also allows to
write robust and adaptive CPS code. In DNP the connection between the sigmoidal
guards of the nif and nwhile statements has to be given as a Gaussian Bayesian
network, which reflects the partial knowledge, the CPS program has about its
environment. To the best of our knowledge, DNP are the first approach linking
neural networks to programs, in a way that makes explicit the meaning of the
network. In order to prove and validate the usefulness of DNP, we use them to
write and learn an adaptive CPS controller for the parallel parking of the
Pioneer rovers available in our CPS lab.
Incoherence-Optimal Matrix Completion
Authors: Yudong Chen
Date: 2013-10-01 00:00:00
Tasks: Clustering, Matrix Completion

This paper considers the matrix completion problem. We show that it is not
necessary to assume joint incoherence, which is a standard but unintuitive and
restrictive condition that is imposed by previous studies. This leads to a
sample complexity bound that is order-wise optimal with respect to the
incoherence parameter (as well as to the rank $r$ and the matrix dimension $n$
up to a log factor). As a consequence, we improve the sample complexity of
recovering a semidefinite matrix from $O(nr^{2}\log^{2}n)$ to $O(nr\log^{2}n)$,
and the highest allowable rank from $\Theta(\sqrt{n}/\log n)$ to
$\Theta(n/\log^{2}n)$. The key step in proof is to obtain new bounds on the
$\ell_{\infty,2}$-norm, defined as the maximum of the row and column norms of a
matrix. To illustrate the applicability of our techniques, we discuss
extensions to SVD projection, structured matrix completion and semi-supervised
clustering, for which we provide order-wise improvements over existing results.
Finally, we turn to the closely-related problem of low-rank-plus-sparse matrix
decomposition. We show that the joint incoherence condition is unavoidable here
for polynomial-time algorithms conditioned on the Planted Clique conjecture.
This means it is intractable in general to separate a rank-$\omega(\sqrt{n})$
positive semidefinite matrix and a sparse matrix. Interestingly, our results
show that the standard and joint incoherence conditions are associated
respectively with the information (statistical) and computational aspects of
the matrix decomposition problem.
Polynomial-Chaos-based Kriging
Authors: R. Schoebi, B. Sudret, J. Wiart
Date: 2015-02-13 00:00:00
Tasks: Experimental Design

Computer simulation has become the standard tool in many engineering fields
for designing and optimizing systems, as well as for assessing their
reliability. To cope with demanding analysis such as optimization and
reliability, surrogate models (a.k.a meta-models) have been increasingly
investigated in the last decade. Polynomial Chaos Expansions (PCE) and Kriging
are two popular non-intrusive meta-modelling techniques. PCE surrogates the
computational model with a series of orthonormal polynomials in the input
variables where polynomials are chosen in coherency with the probability
distributions of those input variables. On the other hand, Kriging assumes that
the computer model behaves as a realization of a Gaussian random process whose
parameters are estimated from the available computer runs, i.e. input vectors
and response values. These two techniques have been developed more or less in
parallel so far with little interaction between the researchers in the two
fields. In this paper, PC-Kriging is derived as a new non-intrusive
meta-modeling approach combining PCE and Kriging. A sparse set of orthonormal
polynomials (PCE) approximates the global behavior of the computational model
whereas Kriging manages the local variability of the model output. An adaptive
algorithm similar to the least angle regression algorithm determines the
optimal sparse set of polynomials. PC-Kriging is validated on various benchmark
analytical functions which are easy to sample for reference results. From the
numerical investigations it is concluded that PC-Kriging performs better than
or at least as good as the two distinct meta-modeling techniques. A larger gain
in accuracy is obtained when the experimental design has a limited size, which
is an asset when dealing with demanding computational models.
A Convex Formulation for Mixed Regression with Two Components: Minimax Optimal Rates
Authors: Yudong Chen, Xinyang Yi, Constantine Caramanis
Date: 2013-12-25 00:00:00
Tasks: regression

We consider the mixed regression problem with two components, under
adversarial and stochastic noise. We give a convex optimization formulation
that provably recovers the true solution, and provide upper bounds on the
recovery errors for both arbitrary noise and stochastic noise settings. We also
give matching minimax lower bounds (up to log factors), showing that under
certain assumptions, our algorithm is information-theoretically optimal. Our
results represent the first tractable algorithm guaranteeing successful
recovery with tight bounds on recovery errors and sample complexity.
Gradient Difference based approach for Text Localization in Compressed domain
Authors: B. H. Shekar, Smitha M. L
Date: 2015-02-13 00:00:00

In this paper, we propose a gradient difference based approach to text
localization in videos and scene images. The input video frame/ image is first
compressed using multilevel 2-D wavelet transform. The edge information of the
reconstructed image is found which is further used for finding the maximum
gradient difference between the pixels and then the boundaries of the detected
text blocks are computed using zero crossing technique. We perform logical AND
operation of the text blocks obtained by gradient difference and the zero
crossing technique followed by connected component analysis to eliminate the
false positives. Finally, the morphological dilation operation is employed on
the detected text blocks for scene text localization. The experimental results
obtained on publicly available standard datasets illustrate that the proposed
method can detect and localize the texts of various sizes, fonts and colors.
Skeleton Matching based approach for Text Localization in Scene Images
Authors: B. H. Shekar, Smitha M. L
Date: 2015-02-13 00:00:00

In this paper, we propose a skeleton matching based approach which aids in
text localization in scene images. The input image is preprocessed and
segmented into blocks using connected component analysis. We obtain the
skeleton of the segmented block using morphology based approach. The
skeletonized images are compared with the trained templates in the database to
categorize into text and non-text blocks. Further, the newly designed
geometrical rules and morphological operations are employed on the detected
text blocks for scene text localization. The experimental results obtained on
publicly available standard datasets illustrate that the proposed method can
detect and localize the texts of various sizes, fonts and colors.
Decision Maker using Coupled Incompressible-Fluid Cylinders
Authors: Song-Ju Kim, Masashi Aono
Date: 2015-02-13 00:00:00

The multi-armed bandit problem (MBP) is the problem of finding, as accurately
and quickly as possible, the most profitable option from a set of options that
gives stochastic rewards by referring to past experiences. Inspired by
fluctuated movements of a rigid body in a tug-of-war game, we formulated a
unique search algorithm that we call the `tug-of-war (TOW) dynamics' for
solving the MBP efficiently. The cognitive medium access, which refers to
multi-user channel allocations in cognitive radio, can be interpreted as the
competitive multi-armed bandit problem (CMBP); the problem is to determine the
optimal strategy for allocating channels to users which yields maximum total
rewards gained by all users. Here we show that it is possible to construct a
physical device for solving the CMBP, which we call the `TOW Bombe', by
exploiting the TOW dynamics existed in coupled incompressible-fluid cylinders.
This analog computing device achieves the `socially-maximum' resource
allocation that maximizes the total rewards in cognitive medium access without
paying a huge computational cost that grows exponentially as a function of the
problem size.
Semi-supervised Data Representation via Affinity Graph Learning
Authors: Wei-Ya Ren
Date: 2015-02-13 00:00:00
Tasks: Graph Learning

We consider the general problem of utilizing both labeled and unlabeled data
to improve data representation performance. A new semi-supervised learning
framework is proposed by combing manifold regularization and data
representation methods such as Non negative matrix factorization and sparse
coding. We adopt unsupervised data representation methods as the learning
machines because they do not depend on the labeled data, which can improve
machine's generation ability as much as possible. The proposed framework forms
the Laplacian regularizer through learning the affinity graph. We incorporate
the new Laplacian regularizer into the unsupervised data representation to
smooth the low dimensional representation of data and make use of label
information. Experimental results on several real benchmark datasets indicate
that our semi-supervised learning framework achieves encouraging results
compared with state-of-art methods.
Far-Field Compression for Fast Kernel Summation Methods in High Dimensions
Authors: William B. March, George Biros
Date: 2014-09-09 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We consider fast kernel summations in high dimensions: given a large set of
points in $d$ dimensions (with $d \gg 3$) and a pair-potential function (the
{\em kernel} function), we compute a weighted sum of all pairwise kernel
interactions for each point in the set. Direct summation is equivalent to a
(dense) matrix-vector multiplication and scales quadratically with the number
of points. Fast kernel summation algorithms reduce this cost to log-linear or
linear complexity.
  Treecodes and Fast Multipole Methods (FMMs) deliver tremendous speedups by
constructing approximate representations of interactions of points that are far
from each other. In algebraic terms, these representations correspond to
low-rank approximations of blocks of the overall interaction matrix. Existing
approaches require an excessive number of kernel evaluations with increasing
$d$ and number of points in the dataset.
  To address this issue, we use a randomized algebraic approach in which we
first sample the rows of a block and then construct its approximate, low-rank
interpolative decomposition. We examine the feasibility of this approach
theoretically and experimentally. We provide a new theoretical result showing a
tighter bound on the reconstruction error from uniformly sampling rows than the
existing state-of-the-art. We demonstrate that our sampling approach is
competitive with existing (but prohibitively expensive) methods from the
literature. We also construct kernel matrices for the Laplacian, Gaussian, and
polynomial kernels -- all commonly used in physics and data analysis. We
explore the numerical properties of blocks of these matrices, and show that
they are amenable to our approach. Depending on the data set, our randomized
algorithm can successfully compute low rank approximations in high dimensions.
We report results for data sets with ambient dimensions from four to 1,000.
Discovering Human Interactions in Videos with Limited Data Labeling
Authors: Mehran Khodabandeh, Arash Vahdat, Guang-Tong Zhou, Hossein Hajimirsadeghi, Mehrsan Javan Roshtkhari, Greg Mori, Stephen Se
Date: 2015-02-12 00:00:00
Tasks: Clustering, Human-Object Interaction Detection

We present a novel approach for discovering human interactions in videos.
Activity understanding techniques usually require a large number of labeled
examples, which are not available in many practical cases. Here, we focus on
recovering semantically meaningful clusters of human-human and human-object
interaction in an unsupervised fashion. A new iterative solution is introduced
based on Maximum Margin Clustering (MMC), which also accepts user feedback to
refine clusters. This is achieved by formulating the whole process as a unified
constrained latent max-margin clustering problem. Extensive experiments have
been carried out over three challenging datasets, Collective Activity, VIRAT,
and UT-interaction. Empirical results demonstrate that the proposed algorithm
can efficiently discover perfect semantic clusters of human interactions with
only a small amount of labeling effort.
Managing large-scale scientific hypotheses as uncertain and probabilistic data
Authors: Bernardo Gonçalves
Date: 2015-01-21 00:00:00
Tasks: Management

In view of the paradigm shift that makes science ever more data-driven, in
this thesis we propose a synthesis method for encoding and managing large-scale
deterministic scientific hypotheses as uncertain and probabilistic data.
  In the form of mathematical equations, hypotheses symmetrically relate
aspects of the studied phenomena. For computing predictions, however,
deterministic hypotheses can be abstracted as functions. We build upon Simon's
notion of structural equations in order to efficiently extract the (so-called)
causal ordering between variables, implicit in a hypothesis structure (set of
mathematical equations).
  We show how to process the hypothesis predictive structure effectively
through original algorithms for encoding it into a set of functional
dependencies (fd's) and then performing causal reasoning in terms of acyclic
pseudo-transitive reasoning over fd's. Such reasoning reveals important causal
dependencies implicit in the hypothesis predictive data and guide our synthesis
of a probabilistic database. Like in the field of graphical models in AI, such
a probabilistic database should be normalized so that the uncertainty arisen
from competing hypotheses is decomposed into factors and propagated properly
onto predictive data by recovering its joint probability distribution through a
lossless join. That is motivated as a design-theoretic principle for
data-driven hypothesis management and predictive analytics.
  The method is applicable to both quantitative and qualitative deterministic
hypotheses and demonstrated in realistic use cases from computational science.
Variable and value elimination in binary constraint satisfaction via forbidden patterns
Authors: David A. Cohen, Martin C. Cooper, Guillaume Escamocher, Stanislav Zivny
Date: 2015-02-12 00:00:00
Tasks: ARC

Variable or value elimination in a constraint satisfaction problem (CSP) can
be used in preprocessing or during search to reduce search space size. A
variable elimination rule (value elimination rule) allows the polynomial-time
identification of certain variables (domain elements) whose elimination,
without the introduction of extra compensatory constraints, does not affect the
satisfiability of an instance. We show that there are essentially just four
variable elimination rules and three value elimination rules defined by
forbidding generic sub-instances, known as irreducible existential patterns, in
arc-consistent CSP instances. One of the variable elimination rules is the
already-known Broken Triangle Property, whereas the other three are novel. The
three value elimination rules can all be seen as strict generalisations of
neighbourhood substitution.
A new hybrid metric for verifying parallel corpora of Arabic-English
Authors: Saad Alkahtani, Wei Liu, William J. Teahan
Date: 2015-02-12 00:00:00
Tasks: Sentence, Translation

This paper discusses a new metric that has been applied to verify the quality
in translation between sentence pairs in parallel corpora of Arabic-English.
This metric combines two techniques, one based on sentence length and the other
based on compression code length. Experiments on sample test parallel
Arabic-English corpora indicate the combination of these two techniques
improves accuracy of the identification of satisfactory and unsatisfactory
sentence pairs compared to sentence length and compression code length alone.
The new method proposed in this research is effective at filtering noise and
reducing mis-translations resulting in greatly improved quality.
Simulation of Color Blindness and a Proposal for Using Google Glass as Color-correcting Tool
Authors: H. M. de Oliveira, J. Ranhel, R. B. A. Alves
Date: 2015-02-12 00:00:00

The human visual color response is driven by specialized cells called cones,
which exist in three types, viz. R, G, and B. Software is developed to simulate
how color images are displayed for different types of color blindness.
Specified the default color deficiency associated with a user, it generates a
preview of the rainbow (in the visible range, from red to violet) and shows up,
side by side with a colorful image provided as input, the display correspondent
colorblind. The idea is to provide an image processing after image acquisition
to enable a better perception ofcolors by the color blind. Examples of
pseudo-correction are shown for the case of Protanopia (red blindness). The
system is adapted into a screen of an i-pad or a cellphone in which the
colorblind observe the camera, the image processed with color detail previously
imperceptible by his naked eye. As prospecting, wearable computer glasses could
be manufactured to provide a corrected image playback. The approach can also
provide augmented reality for human vision by adding the UV or IR responses as
a new feature of Google Glass.
Generalized Non-orthogonal Joint Diagonalization with LU Decomposition and Successive Rotations
Authors: Xiao-Feng Gong, Xiu-Lin Wang, Qiu-Hua Lin
Date: 2013-12-03 00:00:00
Tasks: blind source separation

Non-orthogonal joint diagonalization (NJD) free of prewhitening has been
widely studied in the context of blind source separation (BSS) and array signal
processing, etc. However, NJD is used to retrieve the jointly diagonalizable
structure for a single set of target matrices which are mostly formulized with
a single dataset, and thus is insufficient to handle multiple datasets with
inter-set dependences, a scenario often encountered in joint BSS (J-BSS)
applications. As such, we present a generalized NJD (GNJD) algorithm to
simultaneously perform asymmetric NJD upon multiple sets of target matrices
with mutually linked loading matrices, by using LU decomposition and successive
rotations, to enable J-BSS over multiple datasets with indication/exploitation
of their mutual dependences. Experiments with synthetic and real-world datasets
are provided to illustrate the performance of the proposed algorithm.
Analysis of Solution Quality of a Multiobjective Optimization-based Evolutionary Algorithm for Knapsack Problem
Authors: Jun He, Yong Wang, Yuren Zhou
Date: 2015-02-12 00:00:00
Tasks: Multiobjective Optimization

Multi-objective optimisation is regarded as one of the most promising ways
for dealing with constrained optimisation problems in evolutionary
optimisation. This paper presents a theoretical investigation of a
multi-objective optimisation evolutionary algorithm for solving the 0-1
knapsack problem. Two initialisation methods are considered in the algorithm:
local search initialisation and greedy search initialisation. Then the solution
quality of the algorithm is analysed in terms of the approximation ratio.