pwc-archive/papers-with-abstracts huggingface dataset:
On the Solvability of Viewing Graphs
Authors: Matthew Trager, Brian Osserman, Jean Ponce
Date: 2018-08-08 00:00:00
Proceeding: ECCV 2018 9

A set of fundamental matrices relating pairs of cameras in some configuration
can be represented as edges of a "viewing graph". Whether or not these
fundamental matrices are generically sufficient to recover the global camera
configuration depends on the structure of this graph. We study
characterizations of "solvable" viewing graphs and present several new results
that can be applied to determine which pairs of views may be used to recover
all camera parameters. We also discuss strategies for verifying the solvability
of a graph computationally.
Relaxing and Restraining Queries for OBDA
Authors: Medina Andreşel, Yazmin Ibáñez-García, Magdalena Ortiz, Mantas Šimkus
Date: 2018-08-08 00:00:00

In ontology-based data access (OBDA), ontologies have been successfully
employed for querying possibly unstructured and incomplete data. In this paper,
we advocate using ontologies not only to formulate queries and compute their
answers, but also for modifying queries by relaxing or restraining them, so
that they can retrieve either more or less answers over a given dataset.
Towards this goal, we first illustrate that some domain knowledge that could be
naturally leveraged in OBDA can be expressed using complex role inclusions
(CRI). Queries over ontologies with CRI are not first-order (FO) rewritable in
general. We propose an extension of DL-Lite with CRI, and show that conjunctive
queries over ontologies in this extension are FO rewritable. Our main
contribution is a set of rules to relax and restrain conjunctive queries (CQs).
Firstly, we define rules that use the ontology to produce CQs that are
relaxations/restrictions over any dataset. Secondly, we introduce a set of
data-driven rules, that leverage patterns in the current dataset, to obtain
more fine-grained relaxations and restrictions.
Pattern Recognition Approach to Violin Shapes of MIMO database
Authors: Thomas Peron, Francisco A. Rodrigues, Luciano da F. Costa
Date: 2018-08-08 00:00:00

Since the landmarks established by the Cremonese school in the 16th century,
the history of violin design has been marked by experimentation. While great
effort has been invested since the early 19th century by the scientific
community on researching violin acoustics, substantially less attention has
been given to the statistical characterization of how the violin shape evolved
over time. In this paper we study the morphology of violins retrieved from the
Musical Instrument Museums Online (MIMO) database -- the largest freely
accessible platform providing information about instruments held in public
museums. From the violin images, we derive a set of measurements that reflect
relevant geometrical features of the instruments. The application of Principal
Component Analysis (PCA) uncovered similarities between violin makers and their
respective copyists, as well as among luthiers belonging to the same family
lineage, in the context of historical narrative. Combined with a time-windowed
approach, thin plate splines visualizations revealed that the average violin
outline has remained mostly stable over time, not adhering to any particular
trends of design across different periods in music history.
On the Effect of Task-to-Worker Assignment in Distributed Computing Systems with Stragglers
Authors: Amir Behrouzi-Far, Emina Soljanin
Date: 2018-08-08 00:00:00
Tasks: Distributed Computing

We study the expected completion time of some recently proposed algorithms
for distributed computing which redundantly assign computing tasks to multiple
machines in order to tolerate a certain number of machine failures. We
analytically show that not only the amount of redundancy but also the
task-to-machine assignments affect the latency in a distributed system. We
study systems with a fixed number of computing tasks that are split in possibly
overlapping batches, and independent exponentially distributed machine service
times. We show that, for such systems, the uniform replication of non-
overlapping (disjoint) batches of computing tasks achieves the minimum expected
computing time.
A Novel Disparity Transformation Algorithm for Road Segmentation
Authors: Rui Fan, Mohammud Junaid Bocus, Naim Dahnoun
Date: 2018-08-08 00:00:00
Tasks: Road Segmentation

The disparity information provided by stereo cameras has enabled advanced
driver assistance systems to estimate road area more accurately and
effectively. In this paper, a novel disparity transformation algorithm is
proposed to extract road areas from dense disparity maps by making the
disparity value of the road pixels become similar. The transformation is
achieved using two parameters: roll angle and fitted disparity value with
respect to each row. To achieve a better processing efficiency, golden section
search and dynamic programming are utilised to estimate the roll angle and the
fitted disparity value, respectively. By performing a rotation around the
estimated roll angle, the disparity distribution of each row becomes very
compact. This further improves the accuracy of the road model estimation, as
demonstrated by the various experimental results in this paper. Finally, the
Otsu's thresholding method is applied to the transformed disparity map and the
roads can be accurately segmented at pixel level.
Debunking Fake News One Feature at a Time
Authors: Melanie Tosik, Antonio Mallia, Kedar Gangopadhyay
Date: 2018-08-08 00:00:00
Tasks: Fake News Detection, Stance Detection

Identifying the stance of a news article body with respect to a certain
headline is the first step to automated fake news detection. In this paper, we
introduce a 2-stage ensemble model to solve the stance detection task. By using
only hand-crafted features as input to a gradient boosting classifier, we are
able to achieve a score of 9161.5 out of 11651.25 (78.63%) on the official Fake
News Challenge (Stage 1) dataset. We identify the most useful features for
detecting fake news and discuss how sampling techniques can be used to improve
recall accuracy on a highly imbalanced dataset.
Backprop Evolution
Authors: Maximilian Alber, Irwan Bello, Barret Zoph, Pieter-Jan Kindermans, Prajit Ramachandran, Quoc Le
Date: 2018-08-08 00:00:00

The back-propagation algorithm is the cornerstone of deep learning. Despite
its importance, few variations of the algorithm have been attempted. This work
presents an approach to discover new variations of the back-propagation
equation. We use a domain specific lan- guage to describe update equations as a
list of primitive functions. An evolution-based method is used to discover new
propagation rules that maximize the generalization per- formance after a few
epochs of training. We find several update equations that can train faster with
short training times than standard back-propagation, and perform similar as
standard back-propagation at convergence.
Highly Accelerated Multishot EPI through Synergistic Machine Learning and Joint Reconstruction
Authors: Berkin Bilgic, Itthi Chatnuntawech, Mary Kate Manhard, Qiyuan Tian, Congyu Liao, Stephen F. Cauley, Susie Y. Huang, Jonathan R. Polimeni, Lawrence L. Wald, Kawin Setsompop
Date: 2018-08-08 00:00:00
Tasks: BIG-bench Machine Learning, Image Reconstruction

Purpose: To introduce a combined machine learning (ML) and physics-based
image reconstruction framework that enables navigator-free, highly accelerated
multishot echo planar imaging (msEPI), and demonstrate its application in
high-resolution structural and diffusion imaging.
  Methods: Singleshot EPI is an efficient encoding technique, but does not lend
itself well to high-resolution imaging due to severe distortion artifacts and
blurring. While msEPI can mitigate these artifacts, high-quality msEPI has been
elusive because of phase mismatch arising from shot-to-shot variations which
preclude the combination of the multiple-shot data into a single image. We
employ deep learning to obtain an interim image with minimal artifacts, which
permits estimation of image phase variations due to shot-to-shot changes. These
variations are then included in a Joint Virtual Coil Sensitivity Encoding
(JVC-SENSE) reconstruction to utilize data from all shots and improve upon the
ML solution.
  Results: Our combined ML + physics approach enabled Rinplane x MultiBand (MB)
= 8x2-fold acceleration using 2 EPI-shots for multi-echo imaging, so that
whole-brain T2 and T2* parameter maps could be derived from an 8.3 sec
acquisition at 1x1x3mm3 resolution. This has also allowed high-resolution
diffusion imaging with high geometric fidelity using 5-shots at Rinplane x MB =
9x2-fold acceleration. To make these possible, we extended the state-of-the-art
MUSSELS reconstruction technique to Simultaneous MultiSlice (SMS) encoding and
used it as an input to our ML network.
  Conclusion: Combination of ML and JVC-SENSE enabled navigator-free msEPI at
higher accelerations than previously possible while using fewer shots, with
reduced vulnerability to poor generalizability and poor acceptance of
end-to-end ML approaches.
Multiband SAS Imagery
Authors: Isaac D Gerg
Date: 2018-08-08 00:00:00

Advances in unmanned synthetic aperture sonar (SAS) imaging platforms allow
for the simultaneous collection of multiband SAS imagery. The imagery is
collected over several octaves and the phenomenology's interactions with the
sea floor vary greatly over this range -- higher frequencies resolve proud and
fine structure of the seafloor while lower frequencies resolve subsurface
features and often induce internal resonance in man-made objects. Currently,
analysts examine multiband imagery by viewing a single band at a time. This
method makes it difficult to ascertain correlations between any pair of bands
collected over the same location. To mitigate this issue, we propose methods
which ingest high frequency (HF) and low frequency (LF) SAS imagery and
generates a color composite creating what we call a multiband SAS (MSAS) image.
The MSAS image contains the relevant portions of the HF and LF images required
by an analyst to interpret the scene and are defined using a spatial saliency
metric computed for each image. We then combine the saliency and acoustic
backscatter measures to form the final MSAS image.
Can Network Analysis Techniques help to Predict Design Dependencies? An Initial Study
Authors: J. Andrés Díaz-Pace, Antonela Tommasel, Daniela Godoy
Date: 2018-08-08 00:00:00
Tasks: Attribute, Link Prediction, Prediction

The degree of dependencies among the modules of a software system is a key
attribute to characterize its design structure and its ability to evolve over
time. Several design problems are often correlated with undesired dependencies
among modules. Being able to anticipate those problems is important for
developers, so they can plan early for maintenance and refactoring efforts.
However, existing tools are limited to detecting undesired dependencies once
they appeared in the system. In this work, we investigate whether module
dependencies can be predicted (before they actually appear). Since the module
structure can be regarded as a network, i.e, a dependency graph, we leverage on
network features to analyze the dynamics of such a structure. In particular, we
apply link prediction techniques for this task. We conducted an evaluation on
two Java projects across several versions, using link prediction and machine
learning techniques, and assessed their performance for identifying new
dependencies from a project version to the next one. The results, although
preliminary, show that the link prediction approach is feasible for package
dependencies. Also, this work opens opportunities for further development of
software-specific strategies for dependency prediction.
Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras
Authors: Hidenobu Matsuki, Lukas von Stumberg, Vladyslav Usenko, Jörg Stückler, Daniel Cremers
Date: 2018-08-08 00:00:00
Tasks: Monocular Visual Odometry, Position, Visual Odometry

We propose a novel real-time direct monocular visual odometry for
omnidirectional cameras. Our method extends direct sparse odometry (DSO) by
using the unified omnidirectional model as a projection function, which can be
applied to fisheye cameras with a field-of-view (FoV) well above 180 degrees.
This formulation allows for using the full area of the input image even with
strong distortion, while most existing visual odometry methods can only use a
rectified and cropped part of it. Model parameters within an active keyframe
window are jointly optimized, including the intrinsic/extrinsic camera
parameters, 3D position of points, and affine brightness parameters. Thanks to
the wide FoV, image overlap between frames becomes bigger and points are more
spatially distributed. Our results demonstrate that our method provides
increased accuracy and robustness over state-of-the-art visual odometry
algorithms.
Natural Language Generation by Hierarchical Decoding with Linguistic Patterns
Authors: Shang-Yu Su, Kai-Ling Lo, Yi-Ting Yeh, Yun-Nung Chen
Date: 2018-08-08 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decoder, Sentence, Spoken Dialogue Systems, Text Generation

Natural language generation (NLG) is a critical component in spoken dialogue
systems. Classic NLG can be divided into two phases: (1) sentence planning:
deciding on the overall sentence structure, (2) surface realization:
determining specific word forms and flattening the sentence structure into a
string. Many simple NLG models are based on recurrent neural networks (RNN) and
sequence-to-sequence (seq2seq) model, which basically contains an
encoder-decoder structure; these NLG models generate sentences from scratch by
jointly optimizing sentence planning and surface realization using a simple
cross entropy loss training criterion. However, the simple encoder-decoder
architecture usually suffers from generating complex and long sentences,
because the decoder has to learn all grammar and diction knowledge. This paper
introduces a hierarchical decoding NLG model based on linguistic patterns in
different levels, and shows that the proposed method outperforms the
traditional one with a smaller model size. Furthermore, the design of the
hierarchical decoding is flexible and easily-extensible in various NLG systems.
Fully Convolutional Networks for Automated Segmentation of Abdominal Adipose Tissue Depots in Multicenter Water-Fat MRI
Authors: Taro Langner, Anders Hedström, Katharina Mörwald, Daniel Weghuber, Anders Forslund, Peter Bergsten, Håkan Ahlström, Joel Kullberg
Date: 2018-06-26 00:00:00

Purpose: An approach for the automated segmentation of visceral adipose
tissue (VAT) and subcutaneous adipose tissue (SAT) in multicenter water-fat MRI
scans of the abdomen was investigated, using two different neural network
architectures.
  Methods: The two fully convolutional network architectures U-Net and V-Net
were trained, evaluated and compared on the water-fat MRI data. Data of the
study Tellus with 90 scans from a single center was used for a 10-fold
cross-validation in which the most successful configuration for both networks
was determined. These configurations were then tested on 20 scans of the
multicenter study beta-cell function in JUvenile Diabetes and Obesity
(BetaJudo), which involved a different study population and scanning device.
  Results: The U-Net outperformed the used implementation of the V-Net in both
cross-validation and testing. In cross-validation, the U-Net reached average
dice scores of 0.988 (VAT) and 0.992 (SAT). The average of the absolute
quantification errors amount to 0.67% (VAT) and 0.39% (SAT). On the
multi-center test data, the U-Net performs only slightly worse, with average
dice scores of 0.970 (VAT) and 0.987 (SAT) and quantification errors of 2.80%
(VAT) and 1.65% (SAT).
  Conclusion: The segmentations generated by the U-Net allow for reliable
quantification and could therefore be viable for high-quality automated
measurements of VAT and SAT in large-scale studies with minimal need for human
intervention. The high performance on the multicenter test data furthermore
shows the robustness of this approach for data of different patient
demographics and imaging centers, as long as a consistent imaging protocol is
used.
Debugging Neural Machine Translations
Authors: Matīss Rikters
Date: 2018-08-08 00:00:00
Tasks: Machine Translation, NMT, Translation

In this paper, we describe a tool for debugging the output and attention
weights of neural machine translation (NMT) systems and for improved
estimations of confidence about the output based on the attention. The purpose
of the tool is to help researchers and developers find weak and faulty example
translations that their NMT systems produce without the need for reference
translations. Our tool also includes an option to directly compare translation
outputs from two different NMT engines or experiments. In addition, we present
a demo website of our tool with examples of good and bad translations:
http://attention.lielakeda.lv
Learning to Focus when Ranking Answers
Authors: Dana Sagi, Tzoof Avny, Kira Radinsky, Eugene Agichtein
Date: 2018-08-08 00:00:00
Tasks: Feature Engineering, Learning-To-Rank, Question Answering

One of the main challenges in ranking is embedding the query and document
pairs into a joint feature space, which can then be fed to a learning-to-rank
algorithm. To achieve this representation, the conventional state of the art
approaches perform extensive feature engineering that encode the similarity of
the query-answer pair. Recently, deep-learning solutions have shown that it is
possible to achieve comparable performance, in some settings, by learning the
similarity representation directly from data. Unfortunately, previous models
perform poorly on longer texts, or on texts with significant portion of
irrelevant information, or which are grammatically incorrect. To overcome these
limitations, we propose a novel ranking algorithm for question answering,
QARAT, which uses an attention mechanism to learn on which words and phrases to
focus when building the mutual representation. We demonstrate superior ranking
performance on several real-world question-answer ranking datasets, and provide
visualization of the attention mechanism to otter more insights into how our
models of attention could benefit ranking for difficult question answering
challenges.
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets
Authors: Valentin Vielzeuf, Corentin Kervadec, Stéphane Pateux, Alexis Lechervy, Frédéric Jurie
Date: 2018-08-08 00:00:00
Tasks: Emotion Recognition, Model Selection, Philosophy, Transfer Learning

This paper presents a light-weight and accurate deep neural model for
audiovisual emotion recognition. To design this model, the authors followed a
philosophy of simplicity, drastically limiting the number of parameters to
learn from the target datasets, always choosing the simplest earning methods:
i) transfer learning and low-dimensional space embedding allows to reduce the
dimensionality of the representations. ii) The isual temporal information is
handled by a simple score-per-frame selection process, averaged across time.
iii) A simple frame selection echanism is also proposed to weight the images of
a sequence. iv) The fusion of the different modalities is performed at
prediction level (late usion). We also highlight the inherent challenges of the
AFEW dataset and the difficulty of model selection with as few as 383
validation equences. The proposed real-time emotion classifier achieved a
state-of-the-art accuracy of 60.64 % on the test set of AFEW, and ranked 4th at
he Emotion in the Wild 2018 challenge.
Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer
Authors: Hsueh-Ti Derek Liu, Michael Tao, Chun-Liang Li, Derek Nowrouzezahrai, Alec Jacobson
Date: 2018-08-08 00:00:00
Proceeding: ICLR 2019 5
Tasks: Data Augmentation

Many machine learning image classifiers are vulnerable to adversarial
attacks, inputs with perturbations designed to intentionally trigger
misclassification. Current adversarial methods directly alter pixel colors and
evaluate against pixel norm-balls: pixel perturbations smaller than a specified
magnitude, according to a measurement norm. This evaluation, however, has
limited practical utility since perturbations in the pixel space do not
correspond to underlying real-world phenomena of image formation that lead to
them and has no security motivation attached. Pixels in natural images are
measurements of light that has interacted with the geometry of a physical
scene. As such, we propose the direct perturbation of physical parameters that
underly image formation: lighting and geometry. As such, we propose a novel
evaluation measure, parametric norm-balls, by directly perturbing physical
parameters that underly image formation. One enabling contribution we present
is a physically-based differentiable renderer that allows us to propagate pixel
gradients to the parametric space of lighting and geometry. Our approach
enables physically-based adversarial attacks, and our differentiable renderer
leverages models from the interactive rendering literature to balance the
performance and accuracy trade-offs necessary for a memory-efficient and
scalable adversarial data augmentation workflow.
Cognitive system to achieve human-level accuracy in automated assignment of helpdesk email tickets
Authors: Atri Mandal, Nikhil Malhotra, Shivali Agarwal, Anupama Ray, Giriprasad Sridhara
Date: 2018-08-08 00:00:00

Ticket assignment/dispatch is a crucial part of service delivery business
with lot of scope for automation and optimization. In this paper, we present an
end-to-end automated helpdesk email ticket assignment system, which is also
offered as a service. The objective of the system is to determine the nature of
the problem mentioned in an incoming email ticket and then automatically
dispatch it to an appropriate resolver group (or team) for resolution.
  The proposed system uses an ensemble classifier augmented with a configurable
rule engine. While design of classifier that is accurate is one of the main
challenges, we also need to address the need of designing a system that is
robust and adaptive to changing business needs. We discuss some of the main
design challenges associated with email ticket assignment automation and how we
solve them. The design decisions for our system are driven by high accuracy,
coverage, business continuity, scalability and optimal usage of computational
resources.
  Our system has been deployed in production of three major service providers
and currently assigning over 40,000 emails per month, on an average, with an
accuracy close to 90% and covering at least 90% of email tickets. This
translates to achieving human-level accuracy and results in a net saving of
about 23000 man-hours of effort per annum.
Courteous Autonomous Cars
Authors: Liting Sun, Wei Zhan, Masayoshi Tomizuka, Anca D. Dragan
Date: 2018-08-08 00:00:00

Typically, autonomous cars optimize for a combination of safety, efficiency,
and driving quality. But as we get better at this optimization, we start seeing
behavior go from too conservative to too aggressive. The car's behavior exposes
the incentives we provide in its cost function. In this work, we argue for cars
that are not optimizing a purely selfish cost, but also try to be courteous to
other interactive drivers. We formalize courtesy as a term in the objective
that measures the increase in another driver's cost induced by the autonomous
car's behavior. Such a courtesy term enables the robot car to be aware of
possible irrationality of the human behavior, and plan accordingly. We analyze
the effect of courtesy in a variety of scenarios. We find, for example, that
courteous robot cars leave more space when merging in front of a human driver.
Moreover, we find that such a courtesy term can help explain real human driver
behavior on the NGSIM dataset.