pwc-archive/papers-with-abstracts huggingface dataset:
Data-dependent Learning of Symmetric/Antisymmetric Relations for Knowledge Base Completion
Authors: Hitoshi Manabe, Katsuhiko Hayashi, Masashi Shimbo
Date: 2018-08-25 00:00:00
Tasks: Knowledge Base Completion, Relation

Embedding-based methods for knowledge base completion (KBC) learn
representations of entities and relations in a vector space, along with the
scoring function to estimate the likelihood of relations between entities. The
learnable class of scoring functions is designed to be expressive enough to
cover a variety of real-world relations, but this expressive comes at the cost
of an increased number of parameters. In particular, parameters in these
methods are superfluous for relations that are either symmetric or
antisymmetric. To mitigate this problem, we propose a new L1 regularizer for
Complex Embeddings, which is one of the state-of-the-art embedding-based
methods for KBC. This regularizer promotes symmetry or antisymmetry of the
scoring function on a relation-by-relation basis, in accordance with the
observed data. Our empirical evaluation shows that the proposed method
outperforms the original Complex Embeddings and other baseline methods on the
FB15k dataset.
Dr. Tux: A Question Answering System for Ubuntu users
Authors: Bijil Abraham Philip, Manas Jog, Apurv Milind Upasani
Date: 2018-08-25 00:00:00
Tasks: Question Answering

Various forums and question answering (Q&A) sites are available online that
allow Ubuntu users to find results similar to their queries. However, searching
for a result is often time consuming as it requires the user to find a specific
problem instance relevant to his/her query from a large set of questions. In
this paper, we present an automated question answering system for Ubuntu users
called Dr. Tux that is designed to answer user's queries by selecting the most
similar question from an online database. The prototype was implemented in
Python and uses NLTK and CoreNLP tools for Natural Language Processing. The
data for the prototype was taken from the AskUbuntu website which contains
about 150k questions. The results obtained from the manual evaluation of the
prototype were promising while also presenting some interesting opportunities
for improvement.
Multi-scale CNN stereo and pattern removal technique for underwater active stereo system
Authors: Kazuto Ichimaru, Ryo Furukawa, Hiroshi Kawasaki
Date: 2018-08-25 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

Demands on capturing dynamic scenes of underwater environments are rapidly
growing. Passive stereo is applicable to capture dynamic scenes, however the
shape with textureless surfaces or irregular reflections cannot be recovered by
the technique. In our system, we add a pattern projector to the stereo camera
pair so that artificial textures are augmented on the objects. To use the
system at underwater environments, several problems should be compensated,
i.e., refraction, disturbance by fluctuation and bubbles. Further, since
surface of the objects are interfered by the bubbles, projected patterns, etc.,
those noises and patterns should be removed from captured images to recover
original texture. To solve these problems, we propose three approaches; a
depth-dependent calibration, Convolutional Neural Network(CNN)-stereo method
and CNN-based texture recovery method. A depth-dependent calibration is our
analysis to find the acceptable depth range for approximation by center
projection to find the certain target depth for calibration. In terms of CNN
stereo, unlike common CNNbased stereo methods which do not consider strong
disturbances like refraction or bubbles, we designed a novel CNN architecture
for stereo matching using multi-scale information, which is intended to be
robust against such disturbances. Finally, we propose a multi-scale method for
bubble and a projected-pattern removal method using CNNs to recover original
textures. Experimental results are shown to prove the effectiveness of our
method compared with the state of the art techniques. Furthermore,
reconstruction of a live swimming fish is demonstrated to confirm the
feasibility of our techniques.
A Comparison of the Taguchi Method and Evolutionary Optimization in Multivariate Testing
Authors: Jingbo Jiang, Diego Legrand, Robert Severn, Risto Miikkulainen
Date: 2018-08-25 00:00:00

Multivariate testing has recently emerged as a promising technique in web interface design. In contrast to the standard A/B testing, multivariate approach aims at evaluating a large number of values in a few key variables systematically. The Taguchi method is a practical implementation of this idea, focusing on orthogonal combinations of values. This paper evaluates an alternative method: population-based search, i.e. evolutionary optimization. Its performance is compared to that of the Taguchi method in several simulated conditions, including an orthogonal one designed to favor the Taguchi method, and two realistic conditions with dependences between variables. Evolutionary optimization is found to perform significantly better especially in the realistic conditions, suggesting that it forms a good approach for web interface design in the future.
Multiobjective Optimization Training of PLDA for Speaker Verification
Authors: Liang He, Xianhong Chen, Can Xu, Jia Liu
Date: 2018-08-25 00:00:00
Tasks: Multiobjective Optimization, Speaker Verification, Text-Independent Speaker Verification

Most current state-of-the-art text-independent speaker verification systems
take probabilistic linear discriminant analysis (PLDA) as their backend
classifiers. The parameters of PLDA are often estimated by maximizing the
objective function, which focuses on increasing the value of log-likelihood
function, but ignoring the distinction between speakers. In order to better
distinguish speakers, we propose a multi-objective optimization training for
PLDA. Experiment results show that the proposed method has more than 10%
relative performance improvement in both EER and MinDCF on the NIST SRE14
i-vector challenge dataset, and about 20% relative performance improvement in
EER on the MCE18 dataset.
BOP: Benchmark for 6D Object Pose Estimation
Authors: Tomas Hodan, Frank Michel, Eric Brachmann, Wadim Kehl, Anders Glent Buch, Dirk Kraft, Bertram Drost, Joel Vidal, Stephan Ihrke, Xenophon Zabulis, Caner Sahin, Fabian Manhardt, Federico Tombari, Tae-Kyun Kim, Jiri Matas, Carsten Rother
Date: 2018-08-24 00:00:00
Proceeding: ECCV 2018 9
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, 6D Pose Estimation using RGBD, Object, Pose Estimation, Template Matching

We propose a benchmark for 6D pose estimation of a rigid object from a single
RGB-D input image. The training data consists of a texture-mapped 3D object
model or images of the object in known 6D poses. The benchmark comprises of: i)
eight datasets in a unified format that cover different practical scenarios,
including two new datasets focusing on varying lighting conditions, ii) an
evaluation methodology with a pose-error function that deals with pose
ambiguities, iii) a comprehensive evaluation of 15 diverse recent methods that
captures the status quo of the field, and iv) an online evaluation system that
is open for continuous submission of new results. The evaluation shows that
methods based on point-pair features currently perform best, outperforming
template matching methods, learning-based methods and methods based on 3D local
features. The project website is available at bop.felk.cvut.cz.
To Cluster, or Not to Cluster: An Analysis of Clusterability Methods
Authors: A. Adolfsson, M. Ackerman, N. C. Brownstein
Date: 2018-08-24 00:00:00
Tasks: Clustering

Clustering is an essential data mining tool that aims to discover inherent
cluster structure in data. For most applications, applying clustering is only
appropriate when cluster structure is present. As such, the study of
clusterability, which evaluates whether data possesses such structure, is an
integral part of cluster analysis. However, methods for evaluating
clusterability vary radically, making it challenging to select a suitable
measure. In this paper, we perform an extensive comparison of measures of
clusterability and provide guidelines that clustering users can reference to
select suitable measures for their applications.
A Trio Neural Model for Dynamic Entity Relatedness Ranking
Authors: Tu Nguyen, Tuan Tran, Wolfgang Nejdl
Date: 2018-08-24 00:00:00
Proceeding: CONLL 2018 10
Tasks: Information Retrieval, Retrieval

Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in static settings and an unsupervised manner. However, entities in real-world are often involved in many different relationships, consequently entity-relations are very dynamic over time. In this work, we propose a neural networkbased approach for dynamic entity relatedness, leveraging the collective attention as supervision. Our model is capable of learning rich and different entity representations in a joint framework. Through extensive experiments on large-scale datasets, we demonstrate that our method achieves better results than competitive baselines.
A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification
Authors: Wenbin Zhang, Jianwu Wang, Daeho Jin, Lazaros Oreopoulos, Zhibo Zhang
Date: 2018-08-24 00:00:00
Tasks: General Classification

A self-organizing map (SOM) is a type of competitive artificial neural
network, which projects the high-dimensional input space of the training
samples into a low-dimensional space with the topology relations preserved.
This makes SOMs supportive of organizing and visualizing complex data sets and
have been pervasively used among numerous disciplines with different
applications. Notwithstanding its wide applications, the self-organizing map is
perplexed by its inherent randomness, which produces dissimilar SOM patterns
even when being trained on identical training samples with the same parameters
every time, and thus causes usability concerns for other domain practitioners
and precludes more potential users from exploring SOM based applications in a
broader spectrum. Motivated by this practical concern, we propose a
deterministic approach as a supplement to the standard self-organizing map. In
accordance with the theoretical design, the experimental results with satellite
cloud data demonstrate the effective and efficient organization as well as
simplification capabilities of the proposed approach.
Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response
Authors: Sadegh Riyahi, Wookjin Choi, Chia-Ju Liu, Saad Nadeem, Shan Tan, Hualiang Zhong, Wengen Chen, Abraham J. Wu, James G. Mechalakos, Joseph O. Deasy, Wei Lu
Date: 2018-08-24 00:00:00
Tasks: Anatomy

Quantification of local metabolic tumor volume (MTV) chan-ges after
Chemo-radiotherapy would allow accurate tumor response evaluation. Currently,
local MTV changes in esophageal (soft-tissue) cancer are measured by
registering follow-up PET to baseline PET using the same transformation
obtained by deformable registration of follow-up CT to baseline CT. Such
approach is suboptimal because PET and CT capture fundamentally different
properties (metabolic vs. anatomy) of a tumor. In this work we combined PET and
CT images into a single blended PET-CT image and registered follow-up blended
PET-CT image to baseline blended PET-CT image. B-spline regularized
diffeomorphic registration was used to characterize the large MTV shrinkage.
Jacobian of the resulting transformation was computed to measure the local MTV
changes. Radiomic features (intensity and texture) were then extracted from the
Jacobian map to predict pathologic tumor response. Local MTV changes calculated
using blended PET-CT registration achieved the highest correlation with ground
truth segmentation (R=0.88) compared to PET-PET (R=0.80) and CT-CT (R=0.67)
registrations. Moreover, using blended PET-CT registration, the multivariate
prediction model achieved the highest accuracy with only one Jacobian
co-occurrence texture feature (accuracy=82.3%). This novel framework can
replace the conventional approach that applies CT-CT transformation to the PET
data for longitudinal evaluation of tumor response.
Voice Conversion with Conditional SampleRNN
Authors: Cong Zhou, Michael Horgan, Vivek Kumar, Cristina Vasco, Dan Darcy
Date: 2018-08-24 00:00:00
Tasks: Voice Conversion

Here we present a novel approach to conditioning the SampleRNN generative
model for voice conversion (VC). Conventional methods for VC modify the
perceived speaker identity by converting between source and target acoustic
features. Our approach focuses on preserving voice content and depends on the
generative network to learn voice style. We first train a multi-speaker
SampleRNN model conditioned on linguistic features, pitch contour, and speaker
identity using a multi-speaker speech corpus. Voice-converted speech is
generated using linguistic features and pitch contour extracted from the source
speaker, and the target speaker identity. We demonstrate that our system is
capable of many-to-many voice conversion without requiring parallel data,
enabling broad applications. Subjective evaluation demonstrates that our
approach outperforms conventional VC methods.
ParaNet - Using Dense Blocks for Early Inference
Authors: Joseph Chuang, Eric Tsai, Kevin Huang, Jay Fetter
Date: 2018-08-24 00:00:00

DenseNets have been shown to be a competitive model among recent
convolutional network architectures. These networks utilize Dense Blocks, which
are groups of densely connected layers where the output of a hidden layer is
fed in as the input of every other layer following it. In this paper, we aim to
improve certain aspects of DenseNet, especially when it comes to practicality.
We introduce ParaNet, a new architecture that constructs three pipelines which
allow for early inference. We additionally introduce a cascading mechanism such
that different pipelines are able to share parameters, as well as logit
matching between the outputs of the pipelines. We separately evaluate each of
the newly introduced mechanisms of ParaNet, then evaluate our proposed
architecture on CIFAR-100.
Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening
Authors: Wookjin Choi, Saad Nadeem, Sadegh Riyahi, Joseph O. Deasy, Allen Tannenbaum, Wei Lu
Date: 2018-08-24 00:00:00

Spiculations are important predictors of lung cancer malignancy, which are spikes on the surface of the pulmonary nodules. In this study, we proposed an interpretable and parameter-free technique to quantify the spiculation using area distortion metric obtained by the conformal (angle-preserving) spherical parameterization. We exploit the insight that for an angle-preserved spherical mapping of a given nodule, the corresponding negative area distortion precisely characterizes the spiculations on that nodule. We introduced novel spiculation scores based on the area distortion metric and spiculation measures. We also semi-automatically segment lung nodule (for reproducibility) as well as vessel and wall attachment to differentiate the real spiculations from lobulation and attachment. A simple pathological malignancy prediction model is also introduced. We used the publicly-available LIDC-IDRI dataset pathologists (strong-label) and radiologists (weak-label) ratings to train and test radiomics models containing this feature, and then externally validate the models. We achieved AUC$=$0.80 and 0.76, respectively, with the models trained on the 811 weakly-labeled LIDC datasets and tested on the 72 strongly-labeled LIDC and 73 LUNGx datasets; the previous best model for LUNGx had AUC$=$0.68. The number-of-spiculations feature was found to be highly correlated (Spearman's rank correlation coefficient $\rho = 0.44$) with the radiologists' spiculation score. We developed a reproducible and interpretable, parameter-free technique for quantifying spiculations on nodules. The spiculation quantification measures was then applied to the radiomics framework for pathological malignancy prediction with reproducible semi-automatic segmentation of nodule. Using our interpretable features (size, attachment, spiculation, lobulation), we were able to achieve higher performance than previous models.
GlymphVIS: Visualizing Glymphatic Transport Pathways Using Regularized Optimal Transport
Authors: Rena Elkin, Saad Nadeem, Eldad Haber, Klara Steklova, Hedok Lee, Helene Benveniste, Allen Tannenbaum
Date: 2018-08-24 00:00:00

The glymphatic system (GS) is a transit passage that facilitates brain
metabolic waste removal and its dysfunction has been associated with
neurodegenerative diseases such as Alzheimer's disease. The GS has been studied
by acquiring temporal contrast enhanced magnetic resonance imaging (MRI)
sequences of a rodent brain, and tracking the cerebrospinal fluid injected
contrast agent as it flows through the GS. We present here a novel
visualization framework, GlymphVIS, which uses regularized optimal transport
(OT) to study the flow behavior between time points at which the images are
taken. Using this regularized OT approach, we can incorporate diffusion, handle
noise, and accurately capture and visualize the time varying dynamics in GS
transport. Moreover, we are able to reduce the registration mean-squared and
infinity-norm error across time points by up to a factor of 5 as compared to
the current state-of-the-art method. Our visualization pipeline yields flow
patterns that align well with experts' current findings of the glymphatic
system.
Unknown Examples & Machine Learning Model Generalization
Authors: Yeounoh Chung, Peter J. Haas, Eli Upfal, Tim Kraska
Date: 2018-08-24 00:00:00
Tasks: BIG-bench Machine Learning, model, Selection bias

Over the past decades, researchers and ML practitioners have come up with better and better ways to build, understand and improve the quality of ML models, but mostly under the key assumption that the training data is distributed identically to the testing data. In many real-world applications, however, some potential training examples are unknown to the modeler, due to sample selection bias or, more generally, covariate shift, i.e., a distribution shift between the training and deployment stage. The resulting discrepancy between training and testing distributions leads to poor generalization performance of the ML model and hence biased predictions. We provide novel algorithms that estimate the number and properties of these unknown training examples---unknown unknowns. This information can then be used to correct the training set, prior to seeing any test data. The key idea is to combine species-estimation techniques with data-driven methods for estimating the feature values for the unknown unknowns. Experiments on a variety of ML models and datasets indicate that taking the unknown examples into account can yield a more robust ML model that generalizes better.
Probabilistic Graphical Modeling approach to dynamic PET direct parametric map estimation and image reconstruction
Authors: Michele Scipioni, Stefano Pedemonte, Maria Filomena Santarelli, Luigi Landini
Date: 2018-08-24 00:00:00
Tasks: Image Reconstruction

In the context of dynamic emission tomography, the conventional processing
pipeline consists of independent image reconstruction of single time frames,
followed by the application of a suitable kinetic model to time activity curves
(TACs) at the voxel or region-of-interest level. The relatively new field of 4D
PET direct reconstruction, by contrast, seeks to move beyond this scheme and
incorporate information from multiple time frames within the reconstruction
task. Existing 4D direct models are based on a deterministic description of
voxels' TACs, captured by the chosen kinetic model, considering the photon
counting process the only source of uncertainty. In this work, we introduce a
new probabilistic modeling strategy based on the key assumption that activity
time course would be subject to uncertainty even if the parameters of the
underlying dynamic process were known. This leads to a hierarchical Bayesian
model, which we formulate using the formalism of Probabilistic Graphical
Modeling (PGM). The inference of the joint probability density function arising
from PGM is addressed using a new gradient-based iterative algorithm, which
presents several advantages compared to existing direct methods: it is flexible
to an arbitrary choice of linear and nonlinear kinetic model; it enables the
inclusion of arbitrary (sub)differentiable priors for parametric maps; it is
simpler to implement and suitable to integration in computing frameworks for
machine learning. Computer simulations and an application to real patient scan
showed how the proposed approach allows us to weight the importance of the
kinetic model, providing a bridge between indirect and deterministic direct
methods.
Robust Text Classifier on Test-Time Budgets
Authors: Md. Rizwan Parvez, Tolga Bolukbasi, Kai-Wei Chang, Venkatesh Saligrama
Date: 2018-08-24 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: General Classification, text-classification, Text Classification

We propose a generic and interpretable learning framework for building robust text classification model that achieves accuracy comparable to full models under test-time budget constraints. Our approach learns a selector to identify words that are relevant to the prediction tasks and passes them to the classifier for processing. The selector is trained jointly with the classifier and directly learns to incorporate with the classifier. We further propose a data aggregation scheme to improve the robustness of the classifier. Our learning framework is general and can be incorporated with any type of text classification model. On real-world data, we show that the proposed approach improves the performance of a given classifier and speeds up the model with a mere loss in accuracy performance.
Learning Models for Shared Control of Human-Machine Systems with Unknown Dynamics
Authors: Alexander Broad, Todd Murphey, Brenna Argall
Date: 2018-08-24 00:00:00

We present a novel approach to shared control of human-machine systems. Our
method assumes no a priori knowledge of the system dynamics. Instead, we learn
both the dynamics and information about the user's interaction from observation
through the use of the Koopman operator. Using the learned model, we define an
optimization problem to compute the optimal policy for a given task, and
compare the user input to the optimal input. We demonstrate the efficacy of our
approach with a user study. We also analyze the individual nature of the
learned models by comparing the effectiveness of our approach when the
demonstration data comes from a user's own interactions, from the interactions
of a group of users and from a domain expert. Positive results include
statistically significant improvements on task metrics when comparing a
user-only control paradigm with our shared control paradigm. Surprising results
include findings that suggest that individualizing the model based on a user's
own data does not effect the ability to learn a useful dynamic system. We
explore this tension as it relates to developing human-in-the-loop systems
further in the discussion.