pwc-archive/papers-with-abstracts huggingface dataset:
Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation
Authors: Mohamed Omran, Christoph Lassner, Gerard Pons-Moll, Peter V. Gehler, Bernt Schiele
Date: 2018-08-17 00:00:00
Tasks: 3D Human Pose Estimation, Monocular 3D Human Pose Estimation, Pose Estimation

Direct prediction of 3D body pose and shape remains a challenge even for
highly parameterized deep learning models. Mapping from the 2D image space to
the prediction space is difficult: perspective ambiguities make the loss
function noisy and training data is scarce. In this paper, we propose a novel
approach (Neural Body Fitting (NBF)). It integrates a statistical body model
within a CNN, leveraging reliable bottom-up semantic body part segmentation and
robust top-down body model constraints. NBF is fully differentiable and can be
trained using 2D and 3D annotations. In detailed experiments, we analyze how
the components of our model affect performance, especially the use of part
segmentations as an explicit intermediate representation, and present a robust,
efficiently trainable framework for 3D human pose estimation from 2D images
with competitive results on standard benchmarks. Code will be made available at
http://github.com/mohomran/neural_body_fitting
First Steps Toward CNN based Source Classification of Document Images Shared Over Messaging App
Authors: Sharad Joshi, Suraj Saxena, Nitin Khanna
Date: 2018-08-17 00:00:00
Tasks: General Classification

Knowledge of source smartphone corresponding to a document image can be
helpful in a variety of applications including copyright infringement,
ownership attribution, leak identification and usage restriction. In this
letter, we investigate a convolutional neural network-based approach to solve
source smartphone identification problem for printed text documents which have
been captured by smartphone cameras and shared over messaging platform. In
absence of any publicly available dataset addressing this problem, we introduce
a new image dataset consisting of 315 images of documents printed in three
different fonts, captured using 21 smartphones and shared over WhatsApp.
Experiments conducted on this dataset demonstrate that, in all scenarios, the
proposed system performs as well as or better than the state-of-the-art system
based on handcrafted features and classification of letters extracted from
document images. The new dataset and code of the proposed system will be made
publicly available along with this letter's publication, presently they are
submitted for review.
Decentralized Dictionary Learning Over Time-Varying Digraphs
Authors: Amir Daneshmand, Ying Sun, Gesualdo Scutari, Francisco Facchinei, Brian M. Sadler
Date: 2018-08-17 00:00:00
Tasks: Dictionary Learning

This paper studies Dictionary Learning problems wherein the learning task is
distributed over a multi-agent network, modeled as a time-varying directed
graph. This formulation is relevant, for instance, in Big Data scenarios where
massive amounts of data are collected/stored in different locations (e.g.,
sensors, clouds) and aggregating and/or processing all data in a fusion center
might be inefficient or unfeasible, due to resource limitations, communication
overheads or privacy issues. We develop a unified decentralized algorithmic
framework for this class of nonconvex problems, which is proved to converge to
stationary solutions at a sublinear rate. The new method hinges on Successive
Convex Approximation techniques, coupled with a decentralized tracking
mechanism aiming at locally estimating the gradient of the smooth part of the
sum-utility. To the best of our knowledge, this is the first provably
convergent decentralized algorithm for Dictionary Learning and, more generally,
bi-convex problems over (time-varying) (di)graphs.
ActiveRemediation: The Search for Lead Pipes in Flint, Michigan
Authors: Jacob Abernethy, Alex Chojnacki, Arya Farahi, Eric Schwartz, Jared Webb
Date: 2018-06-10 00:00:00

We detail our ongoing work in Flint, Michigan to detect pipes made of lead
and other hazardous metals. After elevated levels of lead were detected in
residents' drinking water, followed by an increase in blood lead levels in area
children, the state and federal governments directed over $125 million to
replace water service lines, the pipes connecting each home to the water
system. In the absence of accurate records, and with the high cost of
determining buried pipe materials, we put forth a number of predictive and
procedural tools to aid in the search and removal of lead infrastructure.
Alongside these statistical and machine learning approaches, we describe our
interactions with government officials in recommending homes for both
inspection and replacement, with a focus on the statistical model that adapts
to incoming information. Finally, in light of discussions about increased
spending on infrastructure development by the federal government, we explore
how our approach generalizes beyond Flint to other municipalities nationwide.
Characterizing the public perception of WhatsApp through the lens of media
Authors: Josemar Alves Caetano, Gabriel Magno, Evandro Cunha, Wagner Meira Jr., Humberto T. Marques-Neto, Virgilio Almeida
Date: 2018-08-17 00:00:00
Tasks: Articles, Misinformation

WhatsApp is, as of 2018, a significant component of the global information
and communication infrastructure, especially in developing countries. However,
probably due to its strong end-to-end encryption, WhatsApp became an attractive
place for the dissemination of misinformation, extremism and other forms of
undesirable behavior. In this paper, we investigate the public perception of
WhatsApp through the lens of media. We analyze two large datasets of news and
show the kind of content that is being associated with WhatsApp in different
regions of the world and over time. Our analyses include the examination of
named entities, general vocabulary, and topics addressed in news articles that
mention WhatsApp, as well as the polarity of these texts. Among other results,
we demonstrate that the vocabulary and topics around the term "whatsapp" in the
media have been changing over the years and in 2018 concentrate on matters
related to misinformation, politics and criminal scams. More generally, our
findings are useful to understand the impact that tools like WhatsApp play in
the contemporary society and how they are seen by the communities themselves.
A Projector-Based Approach to Quantifying Total and Excess Uncertainties for Sketched Linear Regression
Authors: Jocelyn T. Chi, Ilse C. F. Ipsen
Date: 2018-08-17 00:00:00
Tasks: Dimensionality Reduction, regression

Linear regression is a classic method of data analysis. In recent years, sketching -- a method of dimension reduction using random sampling, random projections, or both -- has gained popularity as an effective computational approximation when the number of observations greatly exceeds the number of variables. In this paper, we address the following question: How does sketching affect the statistical properties of the solution and key quantities derived from it? To answer this question, we present a projector-based approach to sketched linear regression that is exact and that requires minimal assumptions on the sketching matrix. Therefore, downstream analyses hold exactly and generally for all sketching schemes. Additionally, a projector-based approach enables derivation of key quantities from classic linear regression that account for the combined model- and algorithm-induced uncertainties. We demonstrate the usefulness of a projector-based approach in quantifying and enabling insight on excess uncertainties and bias-variance decompositions for sketched linear regression. Finally, we demonstrate how the insights from our projector-based analyses can be used to produce practical sketching diagnostics to aid the design of judicious sketching schemes.
A bagging and importance sampling approach to Support Vector Machines
Authors: R. Bárcenas, M. D. Gónzalez--Lima, A. J. Quiroz
Date: 2018-08-17 00:00:00

An importance sampling and bagging approach to solving the support vector
machine (SVM) problem in the context of large databases is presented and
evaluated. Our algorithm builds on the nearest neighbors ideas presented in
Camelo at al. (2015). As in that reference, the goal of the present proposal is
to achieve a faster solution of the SVM problem without a significance loss in
the prediction error. The performance of the methodology is evaluated in
benchmark examples and theoretical aspects of subsample methods are discussed.
Correlated Multi-armed Bandits with a Latent Random Source
Authors: Samarth Gupta, Gauri Joshi, Osman Yağan
Date: 2018-08-17 00:00:00
Tasks: Multi-Armed Bandits

We consider a novel multi-armed bandit framework where the rewards obtained
by pulling the arms are functions of a common latent random variable. The
correlation between arms due to the common random source can be used to design
a generalized upper-confidence-bound (UCB) algorithm that identifies certain
arms as $non-competitive$, and avoids exploring them. As a result, we reduce a
$K$-armed bandit problem to a $C+1$-armed problem, where $C+1$ includes the
best arm and $C$ $competitive$ arms. Our regret analysis shows that the
competitive arms need to be pulled $\mathcal{O}(\log T)$ times, while the
non-competitive arms are pulled only $\mathcal{O}(1)$ times. As a result, there
are regimes where our algorithm achieves a $\mathcal{O}(1)$ regret as opposed
to the typical logarithmic regret scaling of multi-armed bandit algorithms. We
also evaluate lower bounds on the expected regret and prove that our
correlated-UCB algorithm achieves $\mathcal{O}(1)$ regret whenever possible.
Learning Supervised Topic Models for Classification and Regression from Crowds
Authors: Filipe Rodrigues, Mariana Lourenço, Bernardete Ribeiro, Francisco Pereira
Date: 2018-08-17 00:00:00
Tasks: Classification, General Classification, regression, Topic Models, Variational Inference

The growing need to analyze large collections of documents has led to great
developments in topic modeling. Since documents are frequently associated with
other related variables, such as labels or ratings, much interest has been
placed on supervised topic models. However, the nature of most annotation
tasks, prone to ambiguity and noise, often with high volumes of documents, deem
learning under a single-annotator assumption unrealistic or unpractical for
most real-world applications. In this article, we propose two supervised topic
models, one for classification and another for regression problems, which
account for the heterogeneity and biases among different annotators that are
encountered in practice when learning from crowds. We develop an efficient
stochastic variational inference algorithm that is able to scale to very large
datasets, and we empirically demonstrate the advantages of the proposed model
over state-of-the-art approaches.
Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks
Authors: David Tellez, Maschenka Balkenhol, Irene Otte-Holler, Rob van de Loo, Rob Vogels, Peter Bult, Carla Wauters, Willem Vreuls, Suzanne Mol, Nico Karssemeijer, Geert Litjens, Jeroen van der Laak, Francesco Ciompi
Date: 2018-08-17 00:00:00
Tasks: Data Augmentation, Knowledge Distillation, Mitosis Detection, whole slide images

Manual counting of mitotic tumor cells in tissue sections constitutes one of
the strongest prognostic markers for breast cancer. This procedure, however, is
time-consuming and error-prone. We developed a method to automatically detect
mitotic figures in breast cancer tissue sections based on convolutional neural
networks (CNNs). Application of CNNs to hematoxylin and eosin (H&E) stained
histological tissue sections is hampered by: (1) noisy and expensive reference
standards established by pathologists, (2) lack of generalization due to
staining variation across laboratories, and (3) high computational requirements
needed to process gigapixel whole-slide images (WSIs). In this paper, we
present a method to train and evaluate CNNs to specifically solve these issues
in the context of mitosis detection in breast cancer WSIs. First, by combining
image analysis of mitotic activity in phosphohistone-H3 (PHH3) restained slides
and registration, we built a reference standard for mitosis detection in entire
H&E WSIs requiring minimal manual annotation effort. Second, we designed a data
augmentation strategy that creates diverse and realistic H&E stain variations
by modifying the hematoxylin and eosin color channels directly. Using it during
training combined with network ensembling resulted in a stain invariant mitosis
detector. Third, we applied knowledge distillation to reduce the computational
requirements of the mitosis detection ensemble with a negligible loss of
performance. The system was trained in a single-center cohort and evaluated in
an independent multicenter cohort from The Cancer Genome Atlas on the three
tasks of the Tumor Proliferation Assessment Challenge (TUPAC). We obtained a
performance within the top-3 best methods for most of the tasks of the
challenge.
Data Consistency Approach to Model Validation
Authors: Andreas Svensson, Dave Zachariah, Petre Stoica, Thomas B. Schön
Date: 2018-08-17 00:00:00
Tasks: model, Time Series, Time Series Analysis

In scientific inference problems, the underlying statistical modeling assumptions have a crucial impact on the end results. There exist, however, only a few automatic means for validating these fundamental modelling assumptions. The contribution in this paper is a general criterion to evaluate the consistency of a set of statistical models with respect to observed data. This is achieved by automatically gauging the models' ability to generate data that is similar to the observed data. Importantly, the criterion follows from the model class itself and is therefore directly applicable to a broad range of inference problems with varying data types, ranging from independent univariate data to high-dimensional time-series. The proposed data consistency criterion is illustrated, evaluated and compared to several well-established methods using three synthetic and two real data sets.
Epithelium segmentation using deep learning in H&E-stained prostate specimens with immunohistochemistry as reference standard
Authors: Wouter Bulten, Péter Bándi, Jeffrey Hoven, Rob van de Loo, Johannes Lotz, Nick Weiss, Jeroen van der Laak, Bram van Ginneken, Christina Hulsbergen-van de Kaa, Geert Litjens
Date: 2018-08-17 00:00:00
Tasks: Segmentation

Prostate cancer (PCa) is graded by pathologists by examining the
architectural pattern of cancerous epithelial tissue on hematoxylin and eosin
(H&E) stained slides. Given the importance of gland morphology, automatically
differentiating between glandular epithelial tissue and other tissues is an
important prerequisite for the development of automated methods for detecting
PCa. We propose a new method, using deep learning, for automatically segmenting
epithelial tissue in digitized prostatectomy slides. We employed
immunohistochemistry (IHC) to render the ground truth less subjective and more
precise compared to manual outlining on H&E slides, especially in areas with
high-grade and poorly differentiated PCa. Our dataset consisted of 102 tissue
blocks, including both low and high grade PCa. From each block a single new
section was cut, stained with H&E, scanned, restained using P63 and CK8/18 to
highlight the epithelial structure, and scanned again. The H&E slides were
co-registered to the IHC slides. On a subset of the IHC slides we applied color
deconvolution, corrected stain errors manually, and trained a U-Net to perform
segmentation of epithelial structures. Whole-slide segmentation masks generated
by the IHC U-Net were used to train a second U-Net on H&E. Our system makes
precise cell-level segmentations and segments both intact glands as well as
individual (tumor) epithelial cells. We achieved an F1-score of 0.895 on a
hold-out test set and 0.827 on an external reference set from a different
center. We envision this segmentation as being the first part of a fully
automated prostate cancer detection and grading pipeline.
Lifted Wasserstein Matcher for Fast and Robust Topology Tracking
Authors: Maxime Soler, Mélanie Plainchault, Bruno Conche, Julien Tierny
Date: 2018-08-17 00:00:00

This paper presents a robust and efficient method for tracking topological
features in time-varying scalar data. Structures are tracked based on the
optimal matching between persistence diagrams with respect to the Wasserstein
metric. This fundamentally relies on solving the assignment problem, a special
case of optimal transport, for all consecutive timesteps. Our approach relies
on two main contributions. First, we revisit the seminal assignment algorithm
by Kuhn and Munkres which we specifically adapt to the problem of matching
persistence diagrams in an efficient way. Second, we propose an extension of
the Wasserstein metric that significantly improves the geometrical stability of
the matching of domain-embedded persistence pairs. We show that this
geometrical lifting has the additional positive side-effect of improving the
assignment matrix sparsity and therefore computing time. The global framework
implements a coarse-grained parallelism by computing persistence diagrams and
finding optimal matchings in parallel for every couple of consecutive
timesteps. Critical trajectories are constructed by associating successively
matched persistence pairs over time. Merging and splitting events are detected
with a geometrical threshold in a post-processing stage. Extensive experiments
on real-life datasets show that our matching approach is an order of magnitude
faster than the seminal Munkres algorithm. Moreover, compared to a modern
approximation method, our method provides competitive runtimes while yielding
exact results. We demonstrate the utility of our global framework by extracting
critical point trajectories from various simulated time-varying datasets and
compare it to the existing methods based on associated overlaps of volumes.
Robustness to noise and temporal resolution downsampling is empirically
demonstrated.
Robust Compressive Phase Retrieval via Deep Generative Priors
Authors: Fahad Shamshad, Ali Ahmed
Date: 2018-08-17 00:00:00
Tasks: Denoising, Retrieval

This paper proposes a new framework to regularize the highly ill-posed and
non-linear phase retrieval problem through deep generative priors using simple
gradient descent algorithm. We experimentally show effectiveness of proposed
algorithm for random Gaussian measurements (practically relevant in imaging
through scattering media) and Fourier friendly measurements (relevant in
optical set ups). We demonstrate that proposed approach achieves impressive
results when compared with traditional hand engineered priors including
sparsity and denoising frameworks for number of measurements and robustness
against noise. Finally, we show the effectiveness of the proposed approach on a
real transmission matrix dataset in an actual application of multiple
scattering media imaging.
Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling $(1+λ)$ EA Variants on OneMax and LeadingOnes
Authors: Carola Doerr, Furong Ye, Sander van Rijn, Hao Wang, Thomas Bäck
Date: 2018-08-17 00:00:00
Tasks: Benchmarking, Evolutionary Algorithms

Theoretical and empirical research on evolutionary computation methods
complement each other by providing two fundamentally different approaches
towards a better understanding of black-box optimization heuristics. In
discrete optimization, both streams developed rather independently of each
other, but we observe today an increasing interest in reconciling these two
sub-branches. In continuous optimization, the COCO (COmparing Continuous
Optimisers) benchmarking suite has established itself as an important platform
that theoreticians and practitioners use to exchange research ideas and
questions. No widely accepted equivalent exists in the research domain of
discrete black-box optimization.
  Marking an important step towards filling this gap, we adjust the COCO
software to pseudo-Boolean optimization problems, and obtain from this a
benchmarking environment that allows a fine-grained empirical analysis of
discrete black-box heuristics. In this documentation we demonstrate how this
test bed can be used to profile the performance of evolutionary algorithms.
More concretely, we study the optimization behavior of several $(1+\lambda)$ EA
variants on the two benchmark problems OneMax and LeadingOnes. This comparison
motivates a refined analysis for the optimization time of the $(1+\lambda)$ EA
on LeadingOnes.
Performance Analysis and Robustification of Single-query 6-DoF Camera Pose Estimation
Authors: Junsheng Fu, Said Pertuz, Jiri Matas, Joni-Kristian Kämäräinen
Date: 2018-08-17 00:00:00
Tasks: Camera Pose Estimation, Pose Estimation

We consider a single-query 6-DoF camera pose estimation with reference images
and a point cloud, i.e. the problem of estimating the position and orientation
of a camera by using reference images and a point cloud. In this work, we
perform a systematic comparison of three state-of-the-art strategies for 6-DoF
camera pose estimation, i.e. feature-based, photometric-based and
mutual-information-based approaches. The performance of the studied methods is
evaluated on two standard datasets in terms of success rate, translation error
and max orientation error. Building on the results analysis, we propose a
hybrid approach that combines feature-based and mutual-information-based pose
estimation methods since it provides complementary properties for pose
estimation. Experiments show that (1) in cases with large environmental
variance, the hybrid approach outperforms feature-based and
mutual-information-based approaches by an average of 25.1% and 5.8% in terms of
success rate, respectively; (2) in cases where query and reference images are
captured at similar imaging conditions, the hybrid approach performs similarly
as the feature-based approach, but outperforms both photometric-based and
mutual-information-based approaches with a clear margin; (3) the feature-based
approach is consistently more accurate than mutual-information-based and
photometric-based approaches when at least 4 consistent matching points are
found between the query and reference images.
Neuromorphic Architecture for the Hierarchical Temporal Memory
Authors: Abdullah M. Zyarah, Dhireesha Kudithipudi
Date: 2018-08-17 00:00:00
Tasks: Anomaly Detection, Self-Learning

A biomimetic machine intelligence algorithm, that holds promise in creating
invariant representations of spatiotemporal input streams is the hierarchical
temporal memory (HTM). This unsupervised online algorithm has been demonstrated
on several machine-learning tasks, including anomaly detection. Significant
effort has been made in formalizing and applying the HTM algorithm to different
classes of problems. There are few early explorations of the HTM hardware
architecture, especially for the earlier version of the spatial pooler of HTM
algorithm. In this article, we present a full-scale HTM architecture for both
spatial pooler and temporal memory. Synthetic synapse design is proposed to
address the potential and dynamic interconnections occurring during learning.
The architecture is interweaved with parallel cells and columns that enable
high processing speed for the HTM. The proposed architecture is verified for
two different datasets: MNIST and the European number plate font (EUNF), with
and without the presence of noise. The spatial pooler architecture is
synthesized on Xilinx ZYNQ-7, with 91.16% classification accuracy for MNIST and
90\% accuracy for EUNF, with noise. For the temporal memory sequence
prediction, first and second order predictions are observed for a 5-number long
sequence generated from EUNF dataset and 95% accuracy is obtained. Moreover,
the proposed hardware architecture offers 1364X speedup over the software
realization. These results indicate that the proposed architecture can serve as
a digital core to build the HTM in hardware and eventually as a standalone
self-learning system.