pwc-archive/papers-with-abstracts huggingface dataset:
Interactive Volumetry Of Liver Ablation Zones
Authors: Jan Egger, Harald Busse, Philipp Brandmaier, Daniel Seider, Matthias Gawlitza, Steffen Strocka, Philip Voglreiter, Mark Dokter, Michael Hofmann, Bernhard Kainz, Alexander Hann, Xiaojun Chen, Tuomas Alhonnoro, Mika Pollari, Dieter Schmalstieg, Michael Moche
Date: 2015-10-21 00:00:00
Tasks: Lesion Segmentation

Percutaneous radiofrequency ablation (RFA) is a minimally invasive technique
that destroys cancer cells by heat. The heat results from focusing energy in
the radiofrequency spectrum through a needle. Amongst others, this can enable
the treatment of patients who are not eligible for an open surgery. However,
the possibility of recurrent liver cancer due to incomplete ablation of the
tumor makes post-interventional monitoring via regular follow-up scans
mandatory. These scans have to be carefully inspected for any conspicuousness.
Within this study, the RF ablation zones from twelve post-interventional CT
acquisitions have been segmented semi-automatically to support the visual
inspection. An interactive, graph-based contouring approach, which prefers
spherically shaped regions, has been applied. For the quantitative and
qualitative analysis of the algorithm's results, manual slice-by-slice
segmentations produced by clinical experts have been used as the gold standard
(which have also been compared among each other). As evaluation metric for the
statistical validation, the Dice Similarity Coefficient (DSC) has been
calculated. The results show that the proposed tool provides lesion
segmentation with sufficient accuracy much faster than manual segmentation. The
visual feedback and interactivity make the proposed tool well suitable for the
clinical workflow.
Creating Scalable and Interactive Web Applications Using High Performance Latent Variable Models
Authors: Aaron Q. Li, Yuntian Deng, Kublai Jing, Joseph W Robinson
Date: 2015-10-21 00:00:00

In this project we outline a modularized, scalable system for comparing
Amazon products in an interactive and informative way using efficient latent
variable models and dynamic visualization. We demonstrate how our system can
build on the structure and rich review information of Amazon products in order
to provide a fast, multifaceted, and intuitive comparison. By providing a
condensed per-topic comparison visualization to the user, we are able to
display aggregate information from the entire set of reviews while providing an
interface that is at least as compact as the "most helpful reviews" currently
displayed by Amazon, yet far more informative.
Constructing Dynamic Treatment Regimes in Infinite-Horizon Settings
Authors: Ashkan Ertefaie
Date: 2014-06-03 00:00:00
Tasks: Nutrition

The application of existing methods for constructing optimal dynamic
treatment regimes is limited to cases where investigators are interested in
optimizing a utility function over a fixed period of time (finite horizon). In
this manuscript, we develop an inferential procedure based on temporal
difference residuals for optimal dynamic treatment regimes in infinite-horizon
settings, where there is no a priori fixed end of follow-up point. The proposed
method can be used to determine the optimal regime in chronic diseases where
patients are monitored and treated throughout their life. We derive large
sample results necessary for conducting inference. We also simulate a cohort of
patients with diabetes to mimic the third wave of the National Health and
Nutrition Examination Survey, and we examine the performance of the proposed
method in controlling the level of hemoglobin A1c. Supplementary materials for
this article are available online.
Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions
Authors: Tomoki Tokuda, Junichiro Yoshimoto, Yu Shimizu, Shigeru Toki, Go Okada, Masahiro Takamura, Tetsuya Yamamoto, Shinpei Yoshimura, Yasumasa Okamoto, Shigeto Yamawaki, Kenji Doya
Date: 2015-10-21 00:00:00
Tasks: Clustering, Variational Inference

We propose a novel method for multiple clustering that assumes a
co-clustering structure (partitions in both rows and columns of the data
matrix) in each view. The new method is applicable to high-dimensional data. It
is based on a nonparametric Bayesian approach in which the number of views and
the number of feature-/subject clusters are inferred in a data-driven manner.
We simultaneously model different distribution families, such as Gaussian,
Poisson, and multinomial distributions in each cluster block. This makes our
method applicable to datasets consisting of both numerical and categorical
variables, which biomedical data typically do. Clustering solutions are based
on variational inference with mean field approximation. We apply the proposed
method to synthetic and real data, and show that our method outperforms other
multiple clustering methods both in recovering true cluster structures and in
computation time. Finally, we apply our method to a depression dataset with no
true cluster structure available, from which useful inferences are drawn about
possible clustering structures of the data.
Dimensionality Reduction for Binary Data through the Projection of Natural Parameters
Authors: Andrew J. Landgraf, Yoonkyung Lee
Date: 2015-10-21 00:00:00
Tasks: Dimensionality Reduction

Principal component analysis (PCA) for binary data, known as logistic PCA,
has become a popular alternative to dimensionality reduction of binary data. It
is motivated as an extension of ordinary PCA by means of a matrix
factorization, akin to the singular value decomposition, that maximizes the
Bernoulli log-likelihood. We propose a new formulation of logistic PCA which
extends Pearson's formulation of a low dimensional data representation with
minimum error to binary data. Our formulation does not require a matrix
factorization, as previous methods do, but instead looks for projections of the
natural parameters from the saturated model. Due to this difference, the number
of parameters does not grow with the number of observations and the principal
component scores on new data can be computed with simple matrix multiplication.
We derive explicit solutions for data matrices of special structure and provide
computationally efficient algorithms for solving for the principal component
loadings. Through simulation experiments and an analysis of medical diagnoses
data, we compare our formulation of logistic PCA to the previous formulation as
well as ordinary PCA to demonstrate its benefits.
Content adaptive screen image scaling
Authors: Yao Zhai, Qifei Wang, Yan Lu, Shipeng Li
Date: 2015-10-21 00:00:00
Tasks: General Classification

This paper proposes an efficient content adaptive screen image scaling scheme
for the real-time screen applications like remote desktop and screen sharing.
In the proposed screen scaling scheme, a screen content classification step is
first introduced to classify the screen image into text and pictorial regions.
Afterward, we propose an adaptive shift linear interpolation algorithm to
predict the new pixel values with the shift offset adapted to the content type
of each pixel. The shift offset for each screen content type is offline
optimized by minimizing the theoretical interpolation error based on the
training samples respectively. The proposed content adaptive screen image
scaling scheme can achieve good visual quality and also keep the low complexity
for real-time applications.
Regularization vs. Relaxation: A conic optimization perspective of statistical variable selection
Authors: Hongbo Dong, Kun Chen, Jeff Linderoth
Date: 2015-10-20 00:00:00
Tasks: Combinatorial Optimization, Variable Selection

Variable selection is a fundamental task in statistical data analysis.
Sparsity-inducing regularization methods are a popular class of methods that
simultaneously perform variable selection and model estimation. The central
problem is a quadratic optimization problem with an l0-norm penalty. Exactly
enforcing the l0-norm penalty is computationally intractable for larger scale
problems, so dif- ferent sparsity-inducing penalty functions that approximate
the l0-norm have been introduced. In this paper, we show that viewing the
problem from a convex relaxation perspective offers new insights. In
particular, we show that a popular sparsity-inducing concave penalty function
known as the Minimax Concave Penalty (MCP), and the reverse Huber penalty
derived in a recent work by Pilanci, Wainwright and Ghaoui, can both be derived
as special cases of a lifted convex relaxation called the perspective
relaxation. The optimal perspective relaxation is a related minimax problem
that balances the overall convexity and tightness of approximation to the l0
norm. We show it can be solved by a semidefinite relaxation. Moreover, a
probabilistic interpretation of the semidefinite relaxation reveals connections
with the boolean quadric polytope in combinatorial optimization. Finally by
reformulating the l0-norm pe- nalized problem as a two-level problem, with the
inner level being a Max-Cut problem, our proposed semidefinite relaxation can
be realized by replacing the inner level problem with its semidefinite
relaxation studied by Goemans and Williamson. This interpretation suggests
using the Goemans-Williamson rounding procedure to find approximate solutions
to the l0-norm penalized problem. Numerical experiments demonstrate the
tightness of our proposed semidefinite relaxation, and the effectiveness of
finding approximate solutions by Goemans-Williamson rounding.
A latent shared-component generative model for real-time disease surveillance using Twitter data
Authors: Roberto C. S. N. P. Souza, Denise E. F de Brito, Renato M. Assunção, Wagner Meira Jr
Date: 2015-10-20 00:00:00

Exploiting the large amount of available data for addressing relevant social
problems has been one of the key challenges in data mining. Such efforts have
been recently named "data science for social good" and attracted the attention
of several researchers and institutions. We give a contribution in this
objective in this paper considering a difficult public health problem, the
timely monitoring of dengue epidemics in small geographical areas. We develop a
generative simple yet effective model to connect the fluctuations of disease
cases and disease-related Twitter posts. We considered a hidden Markov process
driving both, the fluctuations in dengue reported cases and the tweets issued
in each region. We add a stable but random source of tweets to represent the
posts when no disease cases are recorded. The model is learned through a Markov
chain Monte Carlo algorithm that produces the posterior distribution of the
relevant parameters. Using data from a significant number of large Brazilian
towns, we demonstrate empirically that our model is able to predict well the
next weeks of the disease counts using the tweets and disease cases jointly.
Transductive Optimization of Top k Precision
Authors: Li-Ping Liu, Thomas G. Dietterich, Nan Li, Zhi-Hua Zhou
Date: 2015-10-20 00:00:00
Tasks: Binary Classification, Information Retrieval, Retrieval

Consider a binary classification problem in which the learner is given a
labeled training set, an unlabeled test set, and is restricted to choosing
exactly $k$ test points to output as positive predictions. Problems of this
kind---{\it transductive precision@$k$}---arise in information retrieval,
digital advertising, and reserve design for endangered species. Previous
methods separate the training of the model from its use in scoring the test
points. This paper introduces a new approach, Transductive Top K (TTK), that
seeks to minimize the hinge loss over all training instances under the
constraint that exactly $k$ test instances are predicted as positive. The paper
presents two optimization methods for this challenging problem. Experiments and
analysis confirm the importance of incorporating the knowledge of $k$ into the
learning process. Experimental evaluations of the TTK approach show that the
performance of TTK matches or exceeds existing state-of-the-art methods on 7
UCI datasets and 3 reserve design problem instances.
Semantic, Cognitive, and Perceptual Computing: Advances toward Computing for Human Experience
Authors: Amit Sheth, Pramod Anantharam, Cory Henson
Date: 2015-10-20 00:00:00
Tasks: Management

The World Wide Web continues to evolve and serve as the infrastructure for
carrying massive amounts of multimodal and multisensory observations. These
observations capture various situations pertinent to people's needs and
interests along with all their idiosyncrasies. To support human-centered
computing that empower people in making better and timely decisions, we look
towards computation that is inspired by human perception and cognition. Toward
this goal, we discuss computing paradigms of semantic computing, cognitive
computing, and an emerging aspect of computing, which we call perceptual
computing. In our view, these offer a continuum to make the most out of vast,
growing, and diverse data pertinent to human needs and interests. We propose
details of perceptual computing characterized by interpretation and exploration
operations comparable to the interleaving of bottom and top brain processing.
  This article consists of two parts. First we describe semantic computing,
cognitive computing, and perceptual computing to lay out distinctions while
acknowledging their complementary capabilities. We then provide a conceptual
overview of the newest of these three paradigms--perceptual computing. For
further insights, we focus on an application scenario of asthma management
converting massive, heterogeneous and multimodal (big) data into actionable
information or smart data.
Computing the Stereo Matching Cost with a Convolutional Neural Network
Authors: Jure Žbontar, Yann Lecun
Date: 2014-09-15 00:00:00
Proceeding: CVPR 2015 6
Tasks: Stereo Matching, Stereo Matching Hand

We present a method for extracting depth information from a rectified image
pair. We train a convolutional neural network to predict how well two image
patches match and use it to compute the stereo matching cost. The cost is
refined by cross-based cost aggregation and semiglobal matching, followed by a
left-right consistency check to eliminate errors in the occluded regions. Our
stereo method achieves an error rate of 2.61 % on the KITTI stereo dataset and
is currently (August 2014) the top performing method on this dataset.
Fast and Guaranteed Tensor Decomposition via Sketching
Authors: Yining Wang, Hsiao-Yu Tung, Alexander Smola, Animashree Anandkumar
Date: 2015-06-14 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Tensor Decomposition

Tensor CANDECOMP/PARAFAC (CP) decomposition has wide applications in
statistical learning of latent variable models and in data mining. In this
paper, we propose fast and randomized tensor CP decomposition algorithms based
on sketching. We build on the idea of count sketches, but introduce many novel
ideas which are unique to tensors. We develop novel methods for randomized
computation of tensor contractions via FFTs, without explicitly forming the
tensors. Such tensor contractions are encountered in decomposition methods such
as tensor power iterations and alternating least squares. We also design novel
colliding hashes for symmetric tensors to further save time in computing the
sketches. We then combine these sketching ideas with existing whitening and
tensor power iterative techniques to obtain the fastest algorithm on both
sparse and dense tensors. The quality of approximation under our method does
not depend on properties such as sparsity, uniformity of elements, etc. We
apply the method for topic modeling and obtain competitive results.
Improved Answer-Set Programming Encodings for Abstract Argumentation
Authors: Sarah A. Gaggl, Norbert Manthey, Alessandro Ronca, Johannes P. Wallner, Stefan Woltran
Date: 2015-07-23 00:00:00
Tasks: Abstract Argumentation

The design of efficient solutions for abstract argumentation problems is a
crucial step towards advanced argumentation systems. One of the most prominent
approaches in the literature is to use Answer-Set Programming (ASP) for this
endeavor. In this paper, we present new encodings for three prominent
argumentation semantics using the concept of conditional literals in
disjunctions as provided by the ASP-system clingo. Our new encodings are not
only more succinct than previous versions, but also outperform them on standard
benchmarks.
Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation
Authors: Christian Bailer, Bertram Taetz, Didier Stricker
Date: 2015-08-21 00:00:00
Proceeding: ICCV 2015 12
Tasks: Optical Flow Estimation

Modern large displacement optical flow algorithms usually use an
initialization by either sparse descriptor matching techniques or dense
approximate nearest neighbor fields. While the latter have the advantage of
being dense, they have the major disadvantage of being very outlier prone as
they are not designed to find the optical flow, but the visually most similar
correspondence. In this paper we present a dense correspondence field approach
that is much less outlier prone and thus much better suited for optical flow
estimation than approximate nearest neighbor fields. Our approach is
conceptually novel as it does not require explicit regularization, smoothing
(like median filtering) or a new data term, but solely our novel purely data
based search strategy that finds most inliers (even for small objects), while
it effectively avoids finding outliers. Moreover, we present novel enhancements
for outlier filtering. We show that our approach is better suited for large
displacement optical flow estimation than state-of-the-art descriptor matching
techniques. We do so by initializing EpicFlow (so far the best method on
MPI-Sintel) with our Flow Fields instead of their originally used
state-of-the-art descriptor matching technique. We significantly outperform the
original EpicFlow on MPI-Sintel, KITTI and Middlebury.
What's the point? Frame-wise Pointing Gesture Recognition with Latent-Dynamic Conditional Random Fields
Authors: Christian Wittner, Boris Schauerte, Rainer Stiefelhagen
Date: 2015-10-20 00:00:00
Tasks: General Classification, Gesture Recognition

We use Latent-Dynamic Conditional Random Fields to perform skeleton-based
pointing gesture classification at each time instance of a video sequence,
where we achieve a frame-wise pointing accuracy of roughly 83%. Subsequently,
we determine continuous time sequences of arbitrary length that form individual
pointing gestures and this way reliably detect pointing gestures at a false
positive detection rate of 0.63%.
Partition MCMC for inference on acyclic digraphs
Authors: Jack Kuipers, Giusi Moffa
Date: 2015-04-20 00:00:00

Acyclic digraphs are the underlying representation of Bayesian networks, a
widely used class of probabilistic graphical models. Learning the underlying
graph from data is a way of gaining insights about the structural properties of
a domain. Structure learning forms one of the inference challenges of
statistical graphical models.
  MCMC methods, notably structure MCMC, to sample graphs from the posterior
distribution given the data are probably the only viable option for Bayesian
model averaging. Score modularity and restrictions on the number of parents of
each node allow the graphs to be grouped into larger collections, which can be
scored as a whole to improve the chain's convergence. Current examples of
algorithms taking advantage of grouping are the biased order MCMC, which acts
on the alternative space of permuted triangular matrices, and non ergodic edge
reversal moves.
  Here we propose a novel algorithm, which employs the underlying combinatorial
structure of DAGs to define a new grouping. As a result convergence is improved
compared to structure MCMC, while still retaining the property of producing an
unbiased sample. Finally the method can be combined with edge reversal moves to
improve the sampler further.
Sequential Score Adaptation with Extreme Value Theory for Robust Railway Track Inspection
Authors: Xavier Gibert, Vishal M. Patel, Rama Chellappa
Date: 2015-10-20 00:00:00
Tasks: Defect Detection

Periodic inspections are necessary to keep railroad tracks in state of good
repair and prevent train accidents. Automatic track inspection using machine
vision technology has become a very effective inspection tool. Because of its
non-contact nature, this technology can be deployed on virtually any railway
vehicle to continuously survey the tracks and send exception reports to track
maintenance personnel. However, as appearance and imaging conditions vary,
false alarm rates can dramatically change, making it difficult to select a good
operating point. In this paper, we use extreme value theory (EVT) within a
Bayesian framework to optimally adjust the sensitivity of anomaly detectors. We
show that by approximating the lower tail of the probability density function
(PDF) of the scores with an Exponential distribution (a special case of the
Generalized Pareto distribution), and using the Gamma conjugate prior learned
from the training data, it is possible to reduce the variability in false alarm
rate and improve the overall performance. This method has shown an increase in
the defect detection rate of rail fasteners in the presence of clutter (at PFA
0.1%) from 95.40% to 99.26% on the 85-mile Northeast Corridor (NEC) 2012-2013
concrete tie dataset.
Two Phase $Q-$learning for Bidding-based Vehicle Sharing
Authors: Yin-Lam Chow, Jia Yuan Yu, Marco Pavone
Date: 2015-09-29 00:00:00
Tasks: Decision Making, Q-Learning, Sequential Decision Making, Vocal Bursts Valence Prediction

We consider one-way vehicle sharing systems where customers can rent a car at
one station and drop it off at another. The problem we address is to optimize
the distribution of cars, and quality of service, by pricing rentals
appropriately. We propose a bidding approach that is inspired from auctions and
takes into account the significant uncertainty inherent in the problem data
(e.g., pick-up and drop-off locations, time of requests, and duration of
trips). Specifically, in contrast to current vehicle sharing systems, the
operator does not set prices. Instead, customers submit bids and the operator
decides whether to rent or not. The operator can even accept negative bids to
motivate drivers to rebalance available cars to unpopular destinations within a
city. We model the operator's sequential decision-making problem as a
\emph{constrained Markov decision problem} (CMDP) and propose and rigorously
analyze a novel two phase $Q$-learning algorithm for its solution. Numerical
experiments are presented and discussed.