pwc-archive/papers-with-abstracts huggingface dataset:
Deep convolutional networks for automated detection of posterior-element fractures on spine CT
Authors: Holger R. Roth, Yinong Wang, Jianhua Yao, Le Lu, Joseph E. Burns, Ronald M. Summers
Date: 2016-01-29 00:00:00

Injuries of the spine, and its posterior elements in particular, are a common
occurrence in trauma patients, with potentially devastating consequences.
Computer-aided detection (CADe) could assist in the detection and
classification of spine fractures. Furthermore, CAD could help assess the
stability and chronicity of fractures, as well as facilitate research into
optimization of treatment paradigms.
  In this work, we apply deep convolutional networks (ConvNets) for the
automated detection of posterior element fractures of the spine. First, the
vertebra bodies of the spine with its posterior elements are segmented in spine
CT using multi-atlas label fusion. Then, edge maps of the posterior elements
are computed. These edge maps serve as candidate regions for predicting a set
of probabilities for fractures along the image edges using ConvNets in a 2.5D
fashion (three orthogonal patches in axial, coronal and sagittal planes). We
explore three different methods for training the ConvNet using 2.5D patches
along the edge maps of 'positive', i.e. fractured posterior-elements and
'negative', i.e. non-fractured elements.
  An experienced radiologist retrospectively marked the location of 55
displaced posterior-element fractures in 18 trauma patients. We randomly split
the data into training and testing cases. In testing, we achieve an
area-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at
5 or 10 false-positives per patient, respectively. Analysis of our set of
trauma patients demonstrates the feasibility of detecting posterior-element
fractures in spine CT images using computer vision techniques such as deep
convolutional networks.
High Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies
Authors: Yi, Wang, Guangliang Chen, Mauro Maggioni
Date: 2015-09-24 00:00:00
Tasks: Anomaly Detection

We briefly review recent progress in techniques for modeling and analyzing
hyperspectral images and movies, in particular for detecting plumes of both
known and unknown chemicals. For detecting chemicals of known spectrum, we
extend the technique of using a single subspace for modeling the background to
a "mixture of subspaces" model to tackle more complicated background.
Furthermore, we use partial least squares regression on a resampled training
set to boost performance. For the detection of unknown chemicals we view the
problem as an anomaly detection problem, and use novel estimators with
low-sampled complexity for intrinsically low-dimensional data in
high-dimensions that enable us to model the "normal" spectra and detect
anomalies. We apply these algorithms to benchmark data sets made available by
the Automated Target Detection program co-funded by NSF, DTRA and NGA, and
compare, when applicable, to current state-of-the-art algorithms, with
favorable results.
Early Predictions of Movie Success: the Who, What, and When of Profitability
Authors: Michael T. Lash, Kang Zhao
Date: 2015-06-17 00:00:00

This paper proposes a decision support system to aid movie investment
decisions at the early stage of movie productions. The system predicts the
success of a movie based on its profitability by leveraging historical data
from various sources. Using social network analysis and text mining techniques,
the system automatically extracts several groups of features, including "who"
are on the cast, "what" a movie is about, "when" a movie will be released, as
well as "hybrid" features that match "who" with "what", and "when" with "what".
Experiment results with movies during an 11-year period showed that the system
outperforms benchmark methods by a large margin in predicting movie
profitability. Novel features we proposed also made great contributions to the
prediction. In addition to designing a decision support system with practical
utilities, our analysis of key factors for movie profitability may also have
implications for theoretical research on team performance and the success of
creative work.
Probabilistic Bag-Of-Hyperlinks Model for Entity Linking
Authors: Octavian-Eugen Ganea, Marina Ganea, Aurelien Lucchi, Carsten Eickhoff, Thomas Hofmann
Date: 2015-09-08 00:00:00
Tasks: Entity Disambiguation, Entity Linking, Feature Engineering, Machine Translation, model, Translation

Many fundamental problems in natural language processing rely on determining
what entities appear in a given text. Commonly referenced as entity linking,
this step is a fundamental component of many NLP tasks such as text
understanding, automatic summarization, semantic search or machine translation.
Name ambiguity, word polysemy, context dependencies and a heavy-tailed
distribution of entities contribute to the complexity of this problem.
  We here propose a probabilistic approach that makes use of an effective
graphical model to perform collective entity disambiguation. Input mentions
(i.e.,~linkable token spans) are disambiguated jointly across an entire
document by combining a document-level prior of entity co-occurrences with
local information captured from mentions and their surrounding context. The
model is based on simple sufficient statistics extracted from data, thus
relying on few parameters to be learned.
  Our method does not require extensive feature engineering, nor an expensive
training procedure. We use loopy belief propagation to perform approximate
inference. The low complexity of our model makes this step sufficiently fast
for real-time usage. We demonstrate the accuracy of our approach on a wide
range of benchmark datasets, showing that it matches, and in many cases
outperforms, existing state-of-the-art methods.
Spectrally Grouped Total Variation Reconstruction for Scatter Imaging Using ADMM
Authors: Ikenna Odinaka, Yan Kaganovsky, Joel A. Greenberg, Mehadi Hassan, David G. Politte, Joseph A. O'Sullivan, Lawrence Carin, David J. Brady
Date: 2016-01-29 00:00:00
Tasks: Image Reconstruction

We consider X-ray coherent scatter imaging, where the goal is to reconstruct
momentum transfer profiles (spectral distributions) at each spatial location
from multiplexed measurements of scatter. Each material is characterized by a
unique momentum transfer profile (MTP) which can be used to discriminate
between different materials. We propose an iterative image reconstruction
algorithm based on a Poisson noise model that can account for photon-limited
measurements as well as various second order statistics of the data. To improve
image quality, previous approaches use edge-preserving regularizers to promote
piecewise constancy of the image in the spatial domain while treating each
spectral bin separately. Instead, we propose spectrally grouped regularization
that promotes piecewise constant images along the spatial directions but also
ensures that the MTPs of neighboring spatial bins are similar, if they contain
the same material. We demonstrate that this group regularization results in
improvement of both spectral and spatial image quality. We pursue an
optimization transfer approach where convex decompositions are used to lift the
problem such that all hyper-voxels can be updated in parallel and in
closed-form. The group penalty introduces a challenge since it is not directly
amendable to these decompositions. We use the alternating directions method of
multipliers (ADMM) to replace the original problem with an equivalent sequence
of sub-problems that are amendable to convex decompositions, leading to a
highly parallel algorithm. We demonstrate the performance on real data.
Lipreading with Long Short-Term Memory
Authors: Michael Wand, Jan Koutník, Jürgen Schmidhuber
Date: 2016-01-29 00:00:00
Tasks: Lipreading, speech-recognition, Speech Recognition

Lipreading, i.e. speech recognition from visual-only recordings of a
speaker's face, can be achieved with a processing pipeline based solely on
neural networks, yielding significantly better accuracy than conventional
methods. Feed-forward and recurrent neural network layers (namely Long
Short-Term Memory; LSTM) are stacked to form a single structure which is
trained by back-propagating error gradients through all the layers. The
performance of such a stacked network was experimentally evaluated and compared
to a standard Support Vector Machine classifier using conventional computer
vision features (Eigenlips and Histograms of Oriented Gradients). The
evaluation was performed on data from 19 speakers of the publicly available
GRID corpus. With 51 different words to classify, we report a best word
accuracy on held-out evaluation speakers of 79.6% using the end-to-end neural
network-based solution (11.6% improvement over the best feature-based solution
evaluated).
Joint System and Algorithm Design for Computationally Efficient Fan Beam Coded Aperture X-ray Coherent Scatter Imaging
Authors: Ikenna Odinaka, Joseph A. O'Sullivan, David G. Politte, Kenneth P. MacCabe, Yan Kaganovsky, Joel A. Greenberg, Manu Lakshmanan, Kalyani Krishnamurthy, Anuj Kapadia, Lawrence Carin, David J. Brady
Date: 2016-01-29 00:00:00

In x-ray coherent scatter tomography, tomographic measurements of the forward
scatter distribution are used to infer scatter densities within a volume. A
radiopaque 2D pattern placed between the object and the detector array enables
the disambiguation between different scatter events. The use of a fan beam
source illumination to speed up data acquisition relative to a pencil beam
presents computational challenges. To facilitate the use of iterative
algorithms based on a penalized Poisson log-likelihood function, efficient
computational implementation of the forward and backward models are needed. Our
proposed implementation exploits physical symmetries and structural properties
of the system and suggests a joint system-algorithm design, where the system
design choices are influenced by computational considerations, and in turn lead
to reduced reconstruction time. Computational-time speedups of approximately
146 and 32 are achieved in the computation of the forward and backward models,
respectively. Results validating the forward model and reconstruction algorithm
are presented on simulated analytic and Monte Carlo data.
Kernels for sequentially ordered data
Authors: Franz J. Király, Harald Oberhauser
Date: 2016-01-29 00:00:00
Tasks: Time Series, Time Series Analysis

We present a novel framework for kernel learning with sequential data of any
kind, such as time series, sequences of graphs, or strings. Our approach is
based on signature features which can be seen as an ordered variant of sample
(cross-)moments; it allows to obtain a "sequentialized" version of any static
kernel. The sequential kernels are efficiently computable for discrete
sequences and are shown to approximate a continuous moment form in a sampling
sense.
  A number of known kernels for sequences arise as "sequentializations" of
suitable static kernels: string kernels may be obtained as a special case, and
alignment kernels are closely related up to a modification that resolves their
open non-definiteness issue. Our experiments indicate that our signature-based
sequential kernel framework may be a promising approach to learning with
sequential data, such as time series, that allows to avoid extensive manual
pre-processing.
Mapping Tractography Across Subjects
Authors: Thien Bao Nguyen, Emanuele Olivetti, Paolo Avesani
Date: 2016-01-29 00:00:00
Tasks: Combinatorial Optimization, Graph Matching

Diffusion magnetic resonance imaging (dMRI) and tractography provide means to
study the anatomical structures within the white matter of the brain. When
studying tractography data across subjects, it is usually necessary to align,
i.e. to register, tractographies together. This registration step is most often
performed by applying the transformation resulting from the registration of
other volumetric images (T1, FA). In contrast with registration methods that
"transform" tractographies, in this work, we try to find which streamline in
one tractography correspond to which streamline in the other tractography,
without any transformation. In other words, we try to find a "mapping" between
the tractographies. We propose a graph-based solution for the tractography
mapping problem and we explain similarities and differences with the related
well-known graph matching problem. Specifically, we define a loss function
based on the pairwise streamline distance and reformulate the mapping problem
as combinatorial optimization of that loss function. We show preliminary
promising results where we compare the proposed method, implemented with
simulated annealing, against a standard registration techniques in a task of
segmentation of the corticospinal tract.
Quantum perceptron over a field and neural network architecture selection in a quantum computer
Authors: Adenilton J. da Silva, Teresa B. Ludermir, Wilson R. de Oliveira
Date: 2016-01-29 00:00:00

In this work, we propose a quantum neural network named quantum perceptron
over a field (QPF). Quantum computers are not yet a reality and the models and
algorithms proposed in this work cannot be simulated in actual (or classical)
computers. QPF is a direct generalization of a classical perceptron and solves
some drawbacks found in previous models of quantum perceptrons. We also present
a learning algorithm named Superposition based Architecture Learning algorithm
(SAL) that optimizes the neural network weights and architectures. SAL searches
for the best architecture in a finite set of neural network architectures with
linear time over the number of patterns in the training set. SAL is the first
learning algorithm to determine neural network architectures in polynomial
time. This speedup is obtained by the use of quantum parallelism and a
non-linear quantum operator.
A Robust UCB Scheme for Active Learning in Regression from Strategic Crowds
Authors: Divya Padmanabhan, Satyanath Bhat, Dinesh Garg, Shirish Shevade, Y. Narahari
Date: 2016-01-25 00:00:00
Tasks: Active Learning, parameter estimation, regression, Variational Inference

We study the problem of training an accurate linear regression model by
procuring labels from multiple noisy crowd annotators, under a budget
constraint. We propose a Bayesian model for linear regression in crowdsourcing
and use variational inference for parameter estimation. To minimize the number
of labels crowdsourced from the annotators, we adopt an active learning
approach. In this specific context, we prove the equivalence of well-studied
criteria of active learning like entropy minimization and expected error
reduction. Interestingly, we observe that we can decouple the problems of
identifying an optimal unlabeled instance and identifying an annotator to label
it. We observe a useful connection between the multi-armed bandit framework and
the annotator selection in active learning. Due to the nature of the
distribution of the rewards on the arms, we use the Robust Upper Confidence
Bound (UCB) scheme with truncated empirical mean estimator to solve the
annotator selection problem. This yields provable guarantees on the regret. We
further apply our model to the scenario where annotators are strategic and
design suitable incentives to induce them to put in their best efforts.
Efficient Robust Mean Value Calculation of 1D Features
Authors: Erik Jonsson, Michael Felsberg
Date: 2016-01-29 00:00:00

A robust mean value is often a good alternative to the standard mean value
when dealing with data containing many outliers. An efficient method for
samples of one-dimensional features and the truncated quadratic error norm is
presented and compared to the method of channel averaging (soft histograms).
Towards A Deeper Geometric, Analytic and Algorithmic Understanding of Margins
Authors: Aaditya Ramdas, Javier Peña
Date: 2014-06-20 00:00:00

Given a matrix $A$, a linear feasibility problem (of which linear
classification is a special case) aims to find a solution to a primal problem
$w: A^Tw > \textbf{0}$ or a certificate for the dual problem which is a
probability distribution $p: Ap = \textbf{0}$. Inspired by the continued
importance of "large-margin classifiers" in machine learning, this paper
studies a condition measure of $A$ called its \textit{margin} that determines
the difficulty of both the above problems. To aid geometrical intuition, we
first establish new characterizations of the margin in terms of relevant balls,
cones and hulls. Our second contribution is analytical, where we present
generalizations of Gordan's theorem, and variants of Hoffman's theorems, both
using margins. We end by proving some new results on a classical iterative
scheme, the Perceptron, whose convergence rates famously depends on the margin.
Our results are relevant for a deeper understanding of margin-based learning
and proving convergence rates of iterative schemes, apart from providing a
unifying perspective on this vast topic.
Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation
Authors: Guo-Sen Xie, Xu-Yao Zhang, Shuicheng Yan, Cheng-Lin Liu
Date: 2016-01-29 00:00:00
Tasks: Clustering, Domain Adaptation, Scene Recognition

Convolutional neural network (CNN) has achieved state-of-the-art performance
in many different visual tasks. Learned from a large-scale training dataset,
CNN features are much more discriminative and accurate than the hand-crafted
features. Moreover, CNN features are also transferable among different domains.
On the other hand, traditional dictionarybased features (such as BoW and SPM)
contain much more local discriminative and structural information, which is
implicitly embedded in the images. To further improve the performance, in this
paper, we propose to combine CNN with dictionarybased models for scene
recognition and visual domain adaptation. Specifically, based on the well-tuned
CNN models (e.g., AlexNet and VGG Net), two dictionary-based representations
are further constructed, namely mid-level local representation (MLR) and
convolutional Fisher vector representation (CFV). In MLR, an efficient
two-stage clustering method, i.e., weighted spatial and feature space spectral
clustering on the parts of a single image followed by clustering all
representative parts of all images, is used to generate a class-mixture or a
classspecific part dictionary. After that, the part dictionary is used to
operate with the multi-scale image inputs for generating midlevel
representation. In CFV, a multi-scale and scale-proportional GMM training
strategy is utilized to generate Fisher vectors based on the last convolutional
layer of CNN. By integrating the complementary information of MLR, CFV and the
CNN features of the fully connected layer, the state-of-the-art performance can
be achieved on scene recognition and domain adaptation problems. An interested
finding is that our proposed hybrid representation (from VGG net trained on
ImageNet) is also complementary with GoogLeNet and/or VGG-11 (trained on
Place205) greatly.
Face Alignment by Local Deep Descriptor Regression
Authors: Amit Kumar, Rajeev Ranjan, Vishal Patel, Rama Chellappa
Date: 2016-01-29 00:00:00
Tasks: Face Alignment, regression

We present an algorithm for extracting key-point descriptors using deep
convolutional neural networks (CNN). Unlike many existing deep CNNs, our model
computes local features around a given point in an image. We also present a
face alignment algorithm based on regression using these local descriptors. The
proposed method called Local Deep Descriptor Regression (LDDR) is able to
localize face landmarks of varying sizes, poses and occlusions with high
accuracy. Deep Descriptors presented in this paper are able to uniquely and
efficiently describe every pixel in the image and therefore can potentially
replace traditional descriptors such as SIFT and HOG. Extensive evaluations on
five publicly available unconstrained face alignment datasets show that our
deep descriptor network is able to capture strong local features around a given
landmark and performs significantly better than many competitive and
state-of-the-art face alignment algorithms.
Automating biomedical data science through tree-based pipeline optimization
Authors: Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, Jason H. Moore
Date: 2016-01-28 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Hyperparameter Optimization

Over the past decade, data science and machine learning has grown from a
mysterious art form to a staple tool across a variety of fields in academia,
business, and government. In this paper, we introduce the concept of tree-based
pipeline optimization for automating one of the most tedious parts of machine
learning---pipeline design. We implement a Tree-based Pipeline Optimization
Tool (TPOT) and demonstrate its effectiveness on a series of simulated and
real-world genetic data sets. In particular, we show that TPOT can build
machine learning pipelines that achieve competitive classification accuracy and
discover novel pipeline operators---such as synthetic feature
constructors---that significantly improve classification accuracy on these data
sets. We also highlight the current challenges to pipeline optimization, such
as the tendency to produce pipelines that overfit the data, and suggest future
research paths to overcome these challenges. As such, this work represents an
early step toward fully automating machine learning pipeline design.
COROLA: A Sequential Solution to Moving Object Detection Using Low-rank Approximation
Authors: Moein Shakeri, Hong Zhang
Date: 2015-05-13 00:00:00
Tasks: Moving Object Detection, object-detection, Object Detection

Extracting moving objects from a video sequence and estimating the background
of each individual image are fundamental issues in many practical applications
such as visual surveillance, intelligent vehicle navigation, and traffic
monitoring. Recently, some methods have been proposed to detect moving objects
in a video via low-rank approximation and sparse outliers where the background
is modeled with the computed low-rank component of the video and the foreground
objects are detected as the sparse outliers in the low-rank approximation. All
of these existing methods work in a batch manner, preventing them from being
applied in real time and long duration tasks. In this paper, we present an
online sequential framework, namely contiguous outliers representation via
online low-rank approximation (COROLA), to detect moving objects and learn the
background model at the same time. We also show that our model can detect
moving objects with a moving camera. Our experimental evaluation uses simulated
data and real public datasets and demonstrates the superior performance of
COROLA in terms of both accuracy and execution time.
Parameterized Machine Learning for High-Energy Physics
Authors: Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter Sadowski, Daniel Whiteson
Date: 2016-01-28 00:00:00
Tasks: BIG-bench Machine Learning, Vocal Bursts Intensity Prediction

We investigate a new structure for machine learning classifiers applied to
problems in high-energy physics by expanding the inputs to include not only
measured features but also physics parameters. The physics parameters represent
a smoothly varying learning task, and the resulting parameterized classifier
can smoothly interpolate between them and replace sets of classifiers trained
at individual values. This simplifies the training process and gives improved
performance at intermediate values, even for complex problems requiring deep
learning. Applications include tools parameterized in terms of theoretical
model parameters, such as the mass of a particle, which allow for a single
network to provide improved discrimination across a range of masses. This
concept is simple to implement and allows for optimized interpolatable results.