pwc-archive/papers-with-abstracts huggingface dataset:
PCG-Cut: Graph Driven Segmentation of the Prostate Central Gland
Authors: Jan Egger
Date: 2013-10-12 00:00:00
Tasks: graph construction, Segmentation

Prostate cancer is the most abundant cancer in men, with over 200,000
expected new cases and around 28,000 deaths in 2012 in the US alone. In this
study, the segmentation results for the prostate central gland (PCG) in MR
scans are presented. The aim of this research study is to apply a graph-based
algorithm to automated segmentation (i.e. delineation) of organ limits for the
prostate central gland. The ultimate goal is to apply automated segmentation
approach to facilitate efficient MR-guided biopsy and radiation treatment
planning. The automated segmentation algorithm used is graph-driven based on a
spherical template. Therefore, rays are sent through the surface points of a
polyhedron to sample the graph's nodes. After graph construction - which only
requires the center of the polyhedron defined by the user and located inside
the prostate center gland - the minimal cost closed set on the graph is
computed via a polynomial time s-t-cut, which results in the segmentation of
the prostate center gland's boundaries and volume. The algorithm has been
realized as a C++ modul within the medical research platform MeVisLab and the
ground truth of the central gland boundaries were manually extracted by
clinical experts (interventional radiologists) with several years of experience
in prostate treatment. For evaluation the automated segmentations of the
proposed scheme have been compared with the manual segmentations, yielding an
average Dice Similarity Coefficient (DSC) of 78.94 +/- 10.85%.
Visualizing Bags of Vectors
Authors: Sriramkumar Balasubramanian, Raghuram Reddy Nagireddy
Date: 2013-10-12 00:00:00

The motivation of this work is two-fold - a) to compare between two different
modes of visualizing data that exists in a bag of vectors format b) to propose
a theoretical model that supports a new mode of visualizing data. Visualizing
high dimensional data can be achieved using Minimum Volume Embedding, but the
data has to exist in a format suitable for computing similarities while
preserving local distances. This paper compares the visualization between two
methods of representing data and also proposes a new method providing sample
visualizations for that method.
Comunication-Efficient Algorithms for Statistical Optimization
Authors: Yuchen Zhang, John C. Duchi, Martin Wainwright
Date: 2012-09-19 00:00:00
Tasks: regression

We analyze two communication-efficient algorithms for distributed statistical
optimization on large-scale data sets. The first algorithm is a standard
averaging method that distributes the $N$ data samples evenly to $\nummac$
machines, performs separate minimization on each subset, and then averages the
estimates. We provide a sharp analysis of this average mixture algorithm,
showing that under a reasonable set of conditions, the combined parameter
achieves mean-squared error that decays as $\order(N^{-1}+(N/m)^{-2})$.
Whenever $m \le \sqrt{N}$, this guarantee matches the best possible rate
achievable by a centralized algorithm having access to all $\totalnumobs$
samples. The second algorithm is a novel method, based on an appropriate form
of bootstrap subsampling. Requiring only a single round of communication, it
has mean-squared error that decays as $\order(N^{-1} + (N/m)^{-3})$, and so is
more robust to the amount of parallelization. In addition, we show that a
stochastic gradient-based method attains mean-squared error decaying as
$O(N^{-1} + (N/ m)^{-3/2})$, easing computation at the expense of penalties in
the rate of convergence. We also provide experimental evaluation of our
methods, investigating their performance both on simulated data and on a
large-scale regression problem from the internet search domain. In particular,
we show that our methods can be used to efficiently solve an advertisement
prediction problem from the Chinese SoSo Search Engine, which involves logistic
regression with $N \approx 2.4 \times 10^8$ samples and $d \approx 740,000$
covariates.
A Turing test for free will
Authors: Seth Lloyd
Date: 2013-10-11 00:00:00
Tasks: Decision Making

Before Alan Turing made his crucial contributions to the theory of
computation, he studied the question of whether quantum mechanics could throw
light on the nature of free will. This article investigates the roles of
quantum mechanics and computation in free will. Although quantum mechanics
implies that events are intrinsically unpredictable, the `pure stochasticity'
of quantum mechanics adds only randomness to decision making processes, not
freedom. By contrast, the theory of computation implies that even when our
decisions arise from a completely deterministic decision-making process, the
outcomes of that process can be intrinsically unpredictable, even to --
especially to -- ourselves. I argue that this intrinsic computational
unpredictability of the decision making process is what give rise to our
impression that we possess free will. Finally, I propose a `Turing test' for
free will: a decision maker who passes this test will tend to believe that he,
she, or it possesses free will, whether the world is deterministic or not.
A quantum teleportation inspired algorithm produces sentence meaning from word meaning and grammatical structure
Authors: Stephen Clark, Bob Coecke, Edward Grefenstette, Stephen Pulman, Mehrnoosh Sadrzadeh
Date: 2013-05-02 00:00:00
Tasks: Sentence

We discuss an algorithm which produces the meaning of a sentence given
meanings of its words, and its resemblance to quantum teleportation. In fact,
this protocol was the main source of inspiration for this algorithm which has
many applications in the area of Natural Language Processing.
Guarantees of Total Variation Minimization for Signal Recovery
Authors: Jian-Feng Cai, Weiyu Xu
Date: 2013-01-28 00:00:00

In this paper, we consider using total variation minimization to recover
signals whose gradients have a sparse support, from a small number of
measurements. We establish the proof for the performance guarantee of total
variation (TV) minimization in recovering \emph{one-dimensional} signal with
sparse gradient support. This partially answers the open problem of proving the
fidelity of total variation minimization in such a setting \cite{TVMulti}. In
particular, we have shown that the recoverable gradient sparsity can grow
linearly with the signal dimension when TV minimization is used. Recoverable
sparsity thresholds of TV minimization are explicitly computed for
1-dimensional signal by using the Grassmann angle framework. We also extend our
results to TV minimization for multidimensional signals. Stability of
recovering signal itself using 1-D TV minimization has also been established
through a property called "almost Euclidean property for 1-dimensional TV
norm". We further give a lower bound on the number of random Gaussian
measurements for recovering 1-dimensional signal vectors with $N$ elements and
$K$-sparse gradients. Interestingly, the number of needed measurements is lower
bounded by $\Omega((NK)^{\frac{1}{2}})$, rather than the $O(K\log(N/K))$ bound
frequently appearing in recovering $K$-sparse signal vectors.
PACE: Pattern Accurate Computationally Efficient Bootstrapping for Timely Discovery of Cyber-Security Concepts
Authors: Nikki McNeil, Robert A. Bridges, Michael D. Iannacone, Bogdan Czejdo, Nicolas Perez, John R. Goodall
Date: 2013-08-21 00:00:00
Tasks: Entity Extraction using GAN, General Classification

Public disclosure of important security information, such as knowledge of
vulnerabilities or exploits, often occurs in blogs, tweets, mailing lists, and
other online sources months before proper classification into structured
databases. In order to facilitate timely discovery of such knowledge, we
propose a novel semi-supervised learning algorithm, PACE, for identifying and
classifying relevant entities in text sources. The main contribution of this
paper is an enhancement of the traditional bootstrapping method for entity
extraction by employing a time-memory trade-off that simultaneously circumvents
a costly corpus search while strengthening pattern nomination, which should
increase accuracy. An implementation in the cyber-security domain is discussed
as well as challenges to Natural Language Processing imposed by the security
domain.
Deep Multiple Kernel Learning
Authors: Eric Strobl, Shyam Visweswaran
Date: 2013-10-11 00:00:00

Deep learning methods have predominantly been applied to large artificial
neural networks. Despite their state-of-the-art performance, these large
networks typically do not generalize well to datasets with limited sample
sizes. In this paper, we take a different approach by learning multiple layers
of kernels. We combine kernels at each layer and then optimize over an estimate
of the support vector machine leave-one-out error rather than the dual
objective function. Our experiments on a variety of datasets show that each
layer successively increases performance with only a few base kernels.
Two discussions of the paper "Bayesian measures of model complexity and fit" by D. Spiegelhalter et al., Read before The Royal Statistical Society at a meeting organized by the Research Section on Wednesday, March 13th, 2002
Authors: E. Moreno, F. -J. Vazquez-Polo, C. P. Robert
Date: 2013-10-10 00:00:00

These are the written discussions of the paper "Bayesian measures of model
complexity and fit" by D. Spiegelhalter et al. (2002), following the
discussions given at the Annual Meeting of the Royal Statistical Society in
Newcastle-upon-Tyne on September 3rd, 2013.
Flexible High-dimensional Classification Machines and Their Asymptotic Properties
Authors: Xingye Qiao, Lingsong Zhang
Date: 2013-10-11 00:00:00
Tasks: Classification, General Classification, Vocal Bursts Intensity Prediction

Classification is an important topic in statistics and machine learning with
great potential in many real applications. In this paper, we investigate two
popular large margin classification methods, Support Vector Machine (SVM) and
Distance Weighted Discrimination (DWD), under two contexts: the
high-dimensional, low-sample size data and the imbalanced data. A unified
family of classification machines, the FLexible Assortment MachinE (FLAME) is
proposed, within which DWD and SVM are special cases. The FLAME family helps to
identify the similarities and differences between SVM and DWD. It is well known
that many classifiers overfit the data in the high-dimensional setting; and
others are sensitive to the imbalanced data, that is, the class with a larger
sample size overly influences the classifier and pushes the decision boundary
towards the minority class. SVM is resistant to the imbalanced data issue, but
it overfits high-dimensional data sets by showing the undesired data-piling
phenomena. The DWD method was proposed to improve SVM in the high-dimensional
setting, but its decision boundary is sensitive to the imbalanced ratio of
sample sizes. Our FLAME family helps to understand an intrinsic connection
between SVM and DWD, and improves both methods by providing a better trade-off
between sensitivity to the imbalanced data and overfitting the high-dimensional
data. Several asymptotic properties of the FLAME classifiers are studied.
Simulations and real data applications are investigated to illustrate the
usefulness of the FLAME classifiers.
Spontaneous Analogy by Piggybacking on a Perceptual System
Authors: Marc Pickett, David W. Aha
Date: 2013-10-10 00:00:00
Tasks: Retrieval

Most computational models of analogy assume they are given a delineated
source domain and often a specified target domain. These systems do not address
how analogs can be isolated from large domains and spontaneously retrieved from
long-term memory, a process we call spontaneous analogy. We present a system
that represents relational structures as feature bags. Using this
representation, our system leverages perceptual algorithms to automatically
create an ontology of relational structures and to efficiently retrieve analogs
for new relational structures from long-term memory. We provide a demonstration
of our approach that takes a set of unsegmented stories, constructs an ontology
of analogical schemas (corresponding to plot devices), and uses this ontology
to efficiently find analogs within new stories, yielding significant
time-savings over linear analog retrieval at a small accuracy cost.
Privacy Aware Learning
Authors: John C. Duchi, Michael. I. Jordan, Martin J. Wainwright
Date: 2012-10-07 00:00:00
Proceeding: NeurIPS 2012 12

We study statistical risk minimization problems under a privacy model in
which the data is kept confidential even from the learner. In this local
privacy framework, we establish sharp upper and lower bounds on the convergence
rates of statistical estimation procedures. As a consequence, we exhibit a
precise tradeoff between the amount of privacy the data preserves and the
utility, as measured by convergence rate, of any statistical estimator or
learning procedure.
Robust Dequantized Compressive Sensing
Authors: Ji Liu, Stephen J. Wright
Date: 2012-07-03 00:00:00
Tasks: compressed sensing, Compressive Sensing, Quantization

We consider the reconstruction problem in compressed sensing in which the
observations are recorded in a finite number of bits. They may thus contain
quantization errors (from being rounded to the nearest representable value) and
saturation errors (from being outside the range of representable values). Our
formulation has an objective of weighted $\ell_2$-$\ell_1$ type, along with
constraints that account explicitly for quantization and saturation errors, and
is solved with an augmented Lagrangian method. We prove a consistency result
for the recovered solution, stronger than those that have appeared to date in
the literature, showing in particular that asymptotic consistency can be
obtained without oversampling. We present extensive computational comparisons
with formulations proposed previously, and variants thereof.
Wavelet methods for shape perception in electro-sensing
Authors: Habib Ammari, Stéphane Mallat, Irène Waldspurger, Han Wang
Date: 2013-10-10 00:00:00

This paper aims at presenting a new approach to the electro-sensing problem
using wavelets. It provides an efficient algorithm for recognizing the shape of
a target from micro-electrical impedance measurements. Stability and resolution
capabilities of the proposed algorithm are quantified in numerical simulations.
Kronecker Sum Decompositions of Space-Time Data
Authors: Kristjan Greenewald, Theodoros Tsiligkaridis, Alfred O. Hero III
Date: 2013-07-27 00:00:00

In this paper we consider the use of the space vs. time Kronecker product
decomposition in the estimation of covariance matrices for spatio-temporal
data. This decomposition imposes lower dimensional structure on the estimated
covariance matrix, thus reducing the number of samples required for estimation.
To allow a smooth tradeoff between the reduction in the number of parameters
(to reduce estimation variance) and the accuracy of the covariance
approximation (affecting estimation bias), we introduce a diagonally loaded
modification of the sum of kronecker products representation [1]. We derive a
Cramer-Rao bound (CRB) on the minimum attainable mean squared predictor
coefficient estimation error for unbiased estimators of Kronecker structured
covariance matrices. We illustrate the accuracy of the diagonally loaded
Kronecker sum decomposition by applying it to video data of human activity.
Gibbs Max-margin Topic Models with Data Augmentation
Authors: Jun Zhu, Ning Chen, Hugh Perkins, Bo Zhang
Date: 2013-10-10 00:00:00
Tasks: Data Augmentation, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Task Learning, Topic Models

Max-margin learning is a powerful approach to building classifiers and
structured output predictors. Recent work on max-margin supervised topic models
has successfully integrated it with Bayesian topic models to discover
discriminative latent semantic structures and make accurate predictions for
unseen testing data. However, the resulting learning problems are usually hard
to solve because of the non-smoothness of the margin loss. Existing approaches
to building max-margin supervised topic models rely on an iterative procedure
to solve multiple latent SVM subproblems with additional mean-field assumptions
on the desired posterior distributions. This paper presents an alternative
approach by defining a new max-margin loss. Namely, we present Gibbs max-margin
supervised topic models, a latent variable Gibbs classifier to discover hidden
topic representations for various tasks, including classification, regression
and multi-task learning. Gibbs max-margin supervised topic models minimize an
expected margin loss, which is an upper bound of the existing margin loss
derived from an expected prediction rule. By introducing augmented variables
and integrating out the Dirichlet variables analytically by conjugacy, we
develop simple Gibbs sampling algorithms with no restricting assumptions and no
need to solve SVM subproblems. Furthermore, each step of the
"augment-and-collapse" Gibbs sampling algorithms has an analytical conditional
distribution, from which samples can be easily drawn. Experimental results
demonstrate significant improvements on time efficiency. The classification
performance is also significantly improved over competitors on binary,
multi-class and multi-label classification tasks.
MizAR 40 for Mizar 40
Authors: Cezary Kaliszyk, Josef Urban
Date: 2013-10-10 00:00:00
Tasks: CPU

As a present to Mizar on its 40th anniversary, we develop an AI/ATP system
that in 30 seconds of real time on a 14-CPU machine automatically proves 40% of
the theorems in the latest official version of the Mizar Mathematical Library
(MML). This is a considerable improvement over previous performance of large-
theory AI/ATP methods measured on the whole MML. To achieve that, a large suite
of AI/ATP methods is employed and further developed. We implement the most
useful methods efficiently, to scale them to the 150000 formulas in MML. This
reduces the training times over the corpus to 1-3 seconds, allowing a simple
practical deployment of the methods in the online automated reasoning service
for the Mizar users (MizAR).
Lemma Mining over HOL Light
Authors: Cezary Kaliszyk, Josef Urban
Date: 2013-10-10 00:00:00
Tasks: LEMMA

Large formal mathematical libraries consist of millions of atomic inference
steps that give rise to a corresponding number of proved statements (lemmas).
Analogously to the informal mathematical practice, only a tiny fraction of such
statements is named and re-used in later proofs by formal mathematicians. In
this work, we suggest and implement criteria defining the estimated usefulness
of the HOL Light lemmas for proving further theorems. We use these criteria to
mine the large inference graph of all lemmas in the core HOL Light library,
adding thousands of the best lemmas to the pool of named statements that can be
re-used in later proofs. The usefulness of the new lemmas is then evaluated by
comparing the performance of automated proving of the core HOL Light theorems
with and without such added lemmas.
Case Adaptation with Qualitative Algebras
Authors: Valmi Dufour-Lussier, Florence Le Ber, Jean Lieber, Laura Martin
Date: 2013-10-10 00:00:00

This paper proposes an approach for the adaptation of spatial or temporal
cases in a case-based reasoning system. Qualitative algebras are used as
spatial and temporal knowledge representation languages. The intuition behind
this adaptation approach is to apply a substitution and then repair potential
inconsistencies, thanks to belief revision on qualitative algebras. A temporal
example from the cooking domain is given. (The paper on which this extended
abstract is based was the recipient of the best paper award of the 2012
International Conference on Case-Based Reasoning.)
Bayesian Estimation of White Matter Atlas from High Angular Resolution Diffusion Imaging
Authors: Jia Du, Alvina Goh, Anqi Qiu
Date: 2013-10-10 00:00:00
Tasks: Anatomy

We present a Bayesian probabilistic model to estimate the brain white matter
atlas from high angular resolution diffusion imaging (HARDI) data. This model
incorporates a shape prior of the white matter anatomy and the likelihood of
individual observed HARDI datasets. We first assume that the atlas is generated
from a known hyperatlas through a flow of diffeomorphisms and its shape prior
can be constructed based on the framework of large deformation diffeomorphic
metric mapping (LDDMM). LDDMM characterizes a nonlinear diffeomorphic shape
space in a linear space of initial momentum uniquely determining diffeomorphic
geodesic flows from the hyperatlas. Therefore, the shape prior of the HARDI
atlas can be modeled using a centered Gaussian random field (GRF) model of the
initial momentum. In order to construct the likelihood of observed HARDI
datasets, it is necessary to study the diffeomorphic transformation of
individual observations relative to the atlas and the probabilistic
distribution of orientation distribution functions (ODFs). To this end, we
construct the likelihood related to the transformation using the same
construction as discussed for the shape prior of the atlas. The probabilistic
distribution of ODFs is then constructed based on the ODF Riemannian manifold.
We assume that the observed ODFs are generated by an exponential map of random
tangent vectors at the deformed atlas ODF. Hence, the likelihood of the ODFs
can be modeled using a GRF of their tangent vectors in the ODF Riemannian
manifold. We solve for the maximum a posteriori using the
Expectation-Maximization algorithm and derive the corresponding update
equations. Finally, we illustrate the HARDI atlas constructed based on a
Chinese aging cohort of 94 adults and compare it with that generated by
averaging the coefficients of spherical harmonics of the ODF across subjects.
Localized Iterative Methods for Interpolation in Graph Structured Data
Authors: Sunil K. Narang, Akshay Gadde, Eduard Sanou, Antonio Ortega
Date: 2013-10-09 00:00:00

In this paper, we present two localized graph filtering based methods for
interpolating graph signals defined on the vertices of arbitrary graphs from
only a partial set of samples. The first method is an extension of previous
work on reconstructing bandlimited graph signals from partially observed
samples. The iterative graph filtering approach very closely approximates the
solution proposed in the that work, while being computationally more efficient.
As an alternative, we propose a regularization based framework in which we
define the cost of reconstruction to be a combination of smoothness of the
graph signal and the reconstruction error with respect to the known samples,
and find solutions that minimize this cost. We provide both a closed form
solution and a computationally efficient iterative solution of the optimization
problem. The experimental results on the recommendation system datasets
demonstrate effectiveness of the proposed methods.