pwc-archive/papers-with-abstracts huggingface dataset:
Direct Automated Quantitative Measurement of Spine via Cascade Amplifier Regression Network
Authors: Shumao Pang, Stephanie Leung, Ilanit Ben Nachum, Qianjin Feng, Shuo Li
Date: 2018-06-14 00:00:00
Tasks: regression

Automated quantitative measurement of the spine (i.e., multiple indices
estimation of heights, widths, areas, and so on for the vertebral body and
disc) is of the utmost importance in clinical spinal disease diagnoses, such as
osteoporosis, intervertebral disc degeneration, and lumbar disc herniation, yet
still an unprecedented challenge due to the variety of spine structure and the
high dimensionality of indices to be estimated. In this paper, we propose a
novel cascade amplifier regression network (CARN), which includes the CARN
architecture and local shape-constrained manifold regularization (LSCMR) loss
function, to achieve accurate direct automated multiple indices estimation. The
CARN architecture is composed of a cascade amplifier network (CAN) for
expressive feature embedding and a linear regression model for multiple indices
estimation. The CAN consists of cascade amplifier units (AUs), which are used
for selective feature reuse by stimulating effective feature and suppressing
redundant feature during propagating feature map between adjacent layers, thus
an expressive feature embedding is obtained. During training, the LSCMR is
utilized to alleviate overfitting and generate realistic estimation by learning
the multiple indices distribution. Experiments on MR images of 195 subjects
show that the proposed CARN achieves impressive performance with mean absolute
errors of 1.2496 mm, 1.2887 mm, and 1.2692 mm for estimation of 15 heights of
discs, 15 heights of vertebral bodies, and total indices respectively. The
proposed method has great potential in clinical spinal disease diagnoses.
Cardiac Motion Scoring with Segment- and Subject-level Non-Local Modeling
Authors: Wufeng Xue, Gary Brahm, Stephanie Leung, Ogla Shmuilovich, Shuo Li
Date: 2018-06-14 00:00:00
Tasks: Anomaly Detection

Motion scoring of cardiac myocardium is of paramount importance for early
detection and diagnosis of various cardiac disease. It aims at identifying
regional wall motions into one of the four types including normal, hypokinetic,
akinetic, and dyskinetic, and is extremely challenging due to the complex
myocardium deformation and subtle inter-class difference of motion patterns.
All existing work on automated motion analysis are focused on binary
abnormality detection to avoid the much more demanding motion scoring, which is
urgently required in real clinical practice yet has never been investigated
before. In this work, we propose Cardiac-MOS, the first powerful method for
cardiac motion scoring from cardiac MR sequences based on deep convolution
neural network. Due to the locality of convolution, the relationship between
distant non-local responses of the feature map cannot be explored, which is
closely related to motion difference between segments. In Cardiac-MOS, such
non-local relationship is modeled with non-local neural network within each
segment and across all segments of one subject, i.e., segment- and
subject-level non-local modeling, and lead to obvious performance improvement.
Besides, Cardiac-MOS can effectively extract motion information from MR
sequences of various lengths by interpolating the convolution kernel along the
temporal dimension, therefore can be applied to MR sequences of multiple
sources. Experiments on 1440 myocardium segments of 90 subjects from short axis
MR sequences of multiple lengths prove that Cardiac-MOS achieves reliable
performance, with correlation of 0.926 for motion score index estimation and
accuracy of 77.4\% for motion scoring. Cardiac-MOS also outperforms all
existing work for binary abnormality detection. As the first automatic motion
scoring solution, Cardiac-MOS demonstrates great potential in future clinical
application.
Adaptive Shooting for Bots in First Person Shooter Games Using Reinforcement Learning
Authors: Frank G. Glavin, Michael G. Madden
Date: 2018-06-14 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In current state-of-the-art commercial first person shooter games, computer
controlled bots, also known as non player characters, can often be easily
distinguishable from those controlled by humans. Tell-tale signs such as failed
navigation, "sixth sense" knowledge of human players' whereabouts and
deterministic, scripted behaviors are some of the causes of this. We propose,
however, that one of the biggest indicators of non humanlike behavior in these
games can be found in the weapon shooting capability of the bot. Consistently
perfect accuracy and "locking on" to opponents in their visual field from any
distance are indicative capabilities of bots that are not found in human
players. Traditionally, the bot is handicapped in some way with either a timed
reaction delay or a random perturbation to its aim, which doesn't adapt or
improve its technique over time. We hypothesize that enabling the bot to learn
the skill of shooting through trial and error, in the same way a human player
learns, will lead to greater variation in game-play and produce less
predictable non player characters. This paper describes a reinforcement
learning shooting mechanism for adapting shooting over time based on a dynamic
reward signal from the amount of damage caused to opponents.
APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework
Authors: Ayelet Heimowitz, Joakim andén, Amit Singer
Date: 2018-02-01 00:00:00
Tasks: Template Matching

Particle picking is a crucial first step in the computational pipeline of
single-particle cryo-electron microscopy (cryo-EM). Selecting particles from
the micrographs is difficult especially for small particles with low contrast.
As high-resolution reconstruction typically requires hundreds of thousands of
particles, manually picking that many particles is often too time-consuming.
While semi-automated particle picking is currently a popular approach, it may
suffer from introducing manual bias into the selection process. In addition,
semi-automated particle picking is still somewhat time-consuming. This paper
presents the APPLE (Automatic Particle Picking with Low user Effort) picker, a
simple and novel approach for fast, accurate, and fully automatic particle
picking. While our approach was inspired by template matching, it is completely
template-free. This approach is evaluated on publicly available datasets
containing micrographs of $\beta$-galactosidase and keyhole limpet hemocyanin
projections.
Correlation Tracking via Robust Region Proposals
Authors: Yuqi Han, Jinghong Nan, Zengshuo Zhang, Jingjing Wang, Baojun Zhao
Date: 2018-06-14 00:00:00
Tasks: Region Proposal, Visual Tracking

Recently, correlation filter-based trackers have received extensive attention
due to their simplicity and superior speed. However, such trackers perform
poorly when the target undergoes occlusion, viewpoint change or other
challenging attributes due to pre-defined sampling strategy. To tackle these
issues, in this paper, we propose an adaptive region proposal scheme to
facilitate visual tracking. To be more specific, a novel tracking monitoring
indicator is advocated to forecast tracking failure. Afterwards, we incorporate
detection and scale proposals respectively, to recover from model drift as well
as handle aspect ratio variation. We test the proposed algorithm on several
challenging sequences, which have demonstrated that the proposed tracker
performs favourably against state-of-the-art trackers.
Variational Message Passing with Structured Inference Networks
Authors: Wu Lin, Nicolas Hubacher, Mohammad Emtiyaz Khan
Date: 2018-03-15 00:00:00
Proceeding: ICLR 2018 1
Tasks: Variational Inference

Recent efforts on combining deep models with probabilistic graphical models
are promising in providing flexible models that are also easy to interpret. We
propose a variational message-passing algorithm for variational inference in
such models. We make three contributions. First, we propose structured
inference networks that incorporate the structure of the graphical model in the
inference network of variational auto-encoders (VAE). Second, we establish
conditions under which such inference networks enable fast amortized inference
similar to VAE. Finally, we derive a variational message passing algorithm to
perform efficient natural-gradient inference while retaining the efficiency of
the amortized inference. By simultaneously enabling structured, amortized, and
natural-gradient inference for deep structured models, our method simplifies
and generalizes existing methods.
EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
Authors: Mohsen Ghafoorian, Cedric Nugteren, Nóra Baka, Olaf Booij, Michael Hofmann
Date: 2018-06-14 00:00:00
Tasks: Lane Detection, Segmentation, Semantic Segmentation

Convolutional neural networks have been successfully applied to semantic
segmentation problems. However, there are many problems that are inherently not
pixel-wise classification problems but are nevertheless frequently formulated
as semantic segmentation. This ill-posed formulation consequently necessitates
hand-crafted scenario-specific and computationally expensive post-processing
methods to convert the per pixel probability maps to final desired outputs.
Generative adversarial networks (GANs) can be used to make the semantic
segmentation network output to be more realistic or better
structure-preserving, decreasing the dependency on potentially complex
post-processing. In this work, we propose EL-GAN: a GAN framework to mitigate
the discussed problem using an embedding loss. With EL-GAN, we discriminate
based on learned embeddings of both the labels and the prediction at the same
time. This results in more stable training due to having better discriminative
information, benefiting from seeing both `fake' and `real' predictions at the
same time. This substantially stabilizes the adversarial training process. We
use the TuSimple lane marking challenge to demonstrate that with our proposed
framework it is viable to overcome the inherent anomalies of posing it as a
semantic segmentation problem. Not only is the output considerably more similar
to the labels when compared to conventional methods, the subsequent
post-processing is also simpler and crosses the competitive 96% accuracy
threshold.
Improved Density-Based Spatio--Textual Clustering on Social Media
Authors: Minh D. Nguyen, Won-Yong Shin
Date: 2018-06-14 00:00:00
Tasks: Clustering

DBSCAN may not be sufficient when the input data type is heterogeneous in
terms of textual description. When we aim to discover clusters of geo-tagged
records relevant to a particular point-of-interest (POI) on social media,
examining only one type of input data (e.g., the tweets relevant to a POI) may
draw an incomplete picture of clusters due to noisy regions. To overcome this
problem, we introduce DBSTexC, a newly defined density-based clustering
algorithm using spatio--textual information. We first characterize POI-relevant
and POI-irrelevant tweets as the texts that include and do not include a POI
name or its semantically coherent variations, respectively. By leveraging the
proportion of POI-relevant and POI-irrelevant tweets, the proposed algorithm
demonstrates much higher clustering performance than the DBSCAN case in terms
of $\mathcal{F}_1$ score and its variants. While DBSTexC performs exactly as
DBSCAN with the textually homogeneous inputs, it far outperforms DBSCAN with
the textually heterogeneous inputs. Furthermore, to further improve the
clustering quality by fully capturing the geographic distribution of tweets, we
present fuzzy DBSTexC (F-DBSTexC), an extension of DBSTexC, which incorporates
the notion of fuzzy clustering into the DBSTexC. We then demonstrate the
robustness of F-DBSTexC via intensive experiments. The computational complexity
of our algorithms is also analytically and numerically shown.
SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment
Authors: Jisun An, Haewoon Kwak, Yong-Yeol Ahn
Date: 2018-06-14 00:00:00
Proceeding: ACL 2018 7

Because word semantics can substantially change across communities and
contexts, capturing domain-specific word semantics is an important challenge.
Here, we propose SEMAXIS, a simple yet powerful framework to characterize word
semantics using many semantic axes in word- vector spaces beyond sentiment. We
demonstrate that SEMAXIS can capture nuanced semantic representations in
multiple online communities. We also show that, when the sentiment axis is
examined, SEMAXIS outperforms the state-of-the-art approaches in building
domain-specific sentiment lexicons.
Features, Projections, and Representation Change for Generalized Planning
Authors: Blai Bonet, Hector Geffner
Date: 2018-01-30 00:00:00

Generalized planning is concerned with the characterization and computation
of plans that solve many instances at once. In the standard formulation, a
generalized plan is a mapping from feature or observation histories into
actions, assuming that the instances share a common pool of features and
actions. This assumption, however, excludes the standard relational planning
domains where actions and objects change across instances. In this work, we
extend the standard formulation of generalized planning to such domains. This
is achieved by projecting the actions over the features, resulting in a common
set of abstract actions which can be tested for soundness and completeness, and
which can be used for generating general policies such as "if the gripper is
empty, pick the clear block above x and place it on the table" that achieve the
goal clear(x) in any Blocksworld instance. In this policy, "pick the clear
block above x" is an abstract action that may represent the action Unstack(a,
b) in one situation and the action Unstack(b, c) in another. Transformations
are also introduced for computing such policies by means of fully observable
non-deterministic (FOND) planners. The value of generalized representations for
learning general policies is also discussed.
Split-door criterion: Identification of causal effects through auxiliary outcomes
Authors: Amit Sharma, Jake M. Hofman, Duncan J. Watts
Date: 2016-11-28 00:00:00
Tasks: Recommendation Systems, Time Series Analysis

We present a method for estimating causal effects in time series data when
fine-grained information about the outcome of interest is available.
Specifically, we examine what we call the split-door setting, where the outcome
variable can be split into two parts: one that is potentially affected by the
cause being studied and another that is independent of it, with both parts
sharing the same (unobserved) confounders. We show that under these conditions,
the problem of identification reduces to that of testing for independence among
observed variables, and present a method that uses this approach to
automatically find subsets of the data that are causally identified. We
demonstrate the method by estimating the causal impact of Amazon's recommender
system on traffic to product pages, finding thousands of examples within the
dataset that satisfy the split-door criterion. Unlike past studies based on
natural experiments that were limited to a single product category, our method
applies to a large and representative sample of products viewed on the site. In
line with previous work, we find that the widely-used click-through rate (CTR)
metric overestimates the causal impact of recommender systems; depending on the
product category, we estimate that 50-80\% of the traffic attributed to
recommender systems would have happened even without any recommendations. We
conclude with guidelines for using the split-door criterion as well as a
discussion of other contexts where the method can be applied.
Real-time Cardiovascular MR with Spatio-temporal Artifact Suppression using Deep Learning - Proof of Concept in Congenital Heart Disease
Authors: Andreas Hauptmann, Simon Arridge, Felix Lucka, Vivek Muthurangu, Jennifer A. Steeden
Date: 2018-03-14 00:00:00
Tasks: compressed sensing, De-aliasing

PURPOSE: Real-time assessment of ventricular volumes requires high
acceleration factors. Residual convolutional neural networks (CNN) have shown
potential for removing artifacts caused by data undersampling. In this study we
investigated the effect of different radial sampling patterns on the accuracy
of a CNN. We also acquired actual real-time undersampled radial data in
patients with congenital heart disease (CHD), and compare CNN reconstruction to
Compressed Sensing (CS).
  METHODS: A 3D (2D plus time) CNN architecture was developed, and trained
using 2276 gold-standard paired 3D data sets, with 14x radial undersampling.
Four sampling schemes were tested, using 169 previously unseen 3D 'synthetic'
test data sets. Actual real-time tiny Golden Angle (tGA) radial SSFP data was
acquired in 10 new patients (122 3D data sets), and reconstructed using the 3D
CNN as well as a CS algorithm; GRASP.
  RESULTS: Sampling pattern was shown to be important for image quality, and
accurate visualisation of cardiac structures. For actual real-time data,
overall reconstruction time with CNN (including creation of aliased images) was
shown to be more than 5x faster than GRASP. Additionally, CNN image quality and
accuracy of biventricular volumes was observed to be superior to GRASP for the
same raw data.
  CONCLUSION: This paper has demonstrated the potential for the use of a 3D CNN
for deep de-aliasing of real-time radial data, within the clinical setting.
Clinical measures of ventricular volumes using real-time data with CNN
reconstruction are not statistically significantly different from the
gold-standard, cardiac gated, BH techniques.
Translations as Additional Contexts for Sentence Classification
Authors: Reinald Kim Amplayo, Kyungjae Lee, Jinyeong Yeo, Seung-won Hwang
Date: 2018-06-14 00:00:00
Tasks: Classification, General Classification, Sentence, Sentence Classification, Subjectivity Analysis, Text Classification

In sentence classification tasks, additional contexts, such as the
neighboring sentences, may improve the accuracy of the classifier. However,
such contexts are domain-dependent and thus cannot be used for another
classification task with an inappropriate domain. In contrast, we propose the
use of translated sentences as context that is always available regardless of
the domain. We find that naive feature expansion of translations gains only
marginal improvements and may decrease the performance of the classifier, due
to possible inaccurate translations thus producing noisy sentence vectors. To
this end, we present multiple context fixing attachment (MCFA), a series of
modules attached to multiple sentence vectors to fix the noise in the vectors
using the other sentence vectors as context. We show that our method performs
competitively compared to previous models, achieving best classification
performance on multiple data sets. We are the first to use translations as
domain-free contexts for sentence classification.
The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing
Authors: Cencheng Shen, Joshua T. Vogelstein
Date: 2018-06-14 00:00:00
Tasks: Two-sample testing

Distance-based tests, also called "energy statistics", are leading methods for two-sample and independence tests from the statistics community. Kernel-based tests, developed from "kernel mean embeddings", are leading methods for two-sample and independence tests from the machine learning community. A fixed-point transformation was previously proposed to connect the distance methods and kernel methods for the population statistics. In this paper, we propose a new bijective transformation between metrics and kernels. It simplifies the fixed-point transformation, inherits similar theoretical properties, allows distance methods to be exactly the same as kernel methods for sample statistics and p-value, and better preserves the data structure upon transformation. Our results further advance the understanding in distance and kernel-based tests, streamline the code base for implementing these tests, and enable a rich literature of distance-based and kernel-based methodologies to directly communicate with each other.
Humor Detection in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System
Authors: Ankush Khandelwal, Sahil Swami, Syed S. Akhtar, Manish Shrivastava
Date: 2018-06-14 00:00:00
Proceeding: LREC 2018 5
Tasks: General Classification, Humor Detection, Sentence, text-classification, Text Classification

The tremendous amount of user generated data through social networking sites
led to the gaining popularity of automatic text classification in the field of
computational linguistics over the past decade. Within this domain, one problem
that has drawn the attention of many researchers is automatic humor detection
in texts. In depth semantic understanding of the text is required to detect
humor which makes the problem difficult to automate. With increase in the
number of social media users, many multilingual speakers often interchange
between languages while posting on social media which is called code-mixing. It
introduces some challenges in the field of linguistic analysis of social media
content (Barman et al., 2014), like spelling variations and non-grammatical
structures in a sentence. Past researches include detecting puns in texts (Kao
et al., 2016) and humor in one-lines (Mihalcea et al., 2010) in a single
language, but with the tremendous amount of code-mixed data available online,
there is a need to develop techniques which detects humor in code-mixed tweets.
In this paper, we analyze the task of humor detection in texts and describe a
freely available corpus containing English-Hindi code-mixed tweets annotated
with humorous(H) or non-humorous(N) tags. We also tagged the words in the
tweets with Language tags (English/Hindi/Others). Moreover, we describe the
experiments carried out on the corpus and provide a baseline classification
system which distinguishes between humorous and non-humorous texts.
NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical On-Device Edge Usage
Authors: Alexander Wong
Date: 2018-06-14 00:00:00
Tasks: image-classification, Image Classification, Object Recognition

Much of the focus in the design of deep neural networks has been on improving
accuracy, leading to more powerful yet highly complex network architectures
that are difficult to deploy in practical scenarios, particularly on edge
devices such as mobile and other consumer devices given their high
computational and memory requirements. As a result, there has been a recent
interest in the design of quantitative metrics for evaluating deep neural
networks that accounts for more than just model accuracy as the sole indicator
of network performance. In this study, we continue the conversation towards
universal metrics for evaluating the performance of deep neural networks for
practical on-device edge usage. In particular, we propose a new balanced metric
called NetScore, which is designed specifically to provide a quantitative
assessment of the balance between accuracy, computational complexity, and
network architecture complexity of a deep neural network, which is important
for on-device edge operation. In what is one of the largest comparative
analysis between deep neural networks in literature, the NetScore metric, the
top-1 accuracy metric, and the popular information density metric were compared
across a diverse set of 60 different deep convolutional neural networks for
image classification on the ImageNet Large Scale Visual Recognition Challenge
(ILSVRC 2012) dataset. The evaluation results across these three metrics for
this diverse set of networks are presented in this study to act as a reference
guide for practitioners in the field. The proposed NetScore metric, along with
the other tested metrics, are by no means perfect, but the hope is to push the
conversation towards better universal metrics for evaluating deep neural
networks for use in practical on-device edge scenarios to help guide
practitioners in model design for such scenarios.