pwc-archive/papers-with-abstracts huggingface dataset:
Clearing noisy annotations for computed tomography imaging
Authors: Roman Khudorozhkov, Alexander Koryagin, Alexey Kozhevin
Date: 2018-07-23 00:00:00
Tasks: Computed Tomography (CT), Segmentation, Semantic Segmentation

One of the problems on the way to successful implementation of neural
networks is the quality of annotation. For instance, different annotators can
annotate images in a different way and very often their decisions do not match
exactly and in extreme cases are even mutually exclusive which results in noisy
annotations and, consequently, inaccurate predictions.
  To avoid that problem in the task of computed tomography (CT) imaging
segmentation we propose a clearing algorithm for annotations. It consists of 3
stages:
  - annotators scoring, which assigns a higher confidence level to better
annotators;
  - nodules scoring, which assigns a higher confidence level to nodules
confirmed by good annotators;
  - nodules merging, which aggregates annotations according to nodules
confidence.
  In general, the algorithm can be applied to many different tasks (namely,
binary and multi-class semantic segmentation, and also with trivial adjustments
to classification and regression) where there are several annotators labeling
each image.
Algorithm Selection for Collaborative Filtering: the influence of graph metafeatures and multicriteria metatargets
Authors: Tiago Cunha, Carlos Soares, André C. P. L. F. de Carvalho
Date: 2018-07-23 00:00:00
Tasks: Collaborative Filtering, feature selection

To select the best algorithm for a new problem is an expensive and difficult
task. However, there are automatic solutions to address this problem: using
Metalearning, which takes advantage of problem characteristics (i.e.
metafeatures), one is able to predict the relative performance of algorithms.
In the Collaborative Filtering scope, recent works have proposed diverse
metafeatures describing several dimensions of this problem. Despite interesting
and effective findings, it is still unknown whether these are the most
effective metafeatures. Hence, this work proposes a new set of graph
metafeatures, which approach the Collaborative Filtering problem from a Graph
Theory perspective. Furthermore, in order to understand whether metafeatures
from multiple dimensions are a better fit, we investigate the effects of
comprehensive metafeatures. These metafeatures are a selection of the best
metafeatures from all existing Collaborative Filtering metafeatures. The impact
of the most representative metafeatures is investigated in a controlled
experimental setup. Another contribution we present is the use of a
Pareto-Efficient ranking procedure to create multicriteria metatargets. These
new rankings of algorithms, which take into account multiple evaluation
measures, allow to explore the algorithm selection problem in a fairer and more
detailed way. According to the experimental results, the graph metafeatures are
a good alternative to related work metafeatures. However, the results have
shown that the feature selection procedure used to create the comprehensive
metafeatures is is not effective, since there is no gain in predictive
performance. Finally, an extensive metaknowledge analysis was conducted to
identify the most influential metafeatures.
Recurrent Neural Networks for Long and Short-Term Sequential Recommendation
Authors: Kiewan Villatel, Elena Smirnova, Jérémie Mary, Philippe Preux
Date: 2018-07-23 00:00:00
Tasks: Dimensionality Reduction, Recommendation Systems, Sequential Recommendation, Session-Based Recommendations

Recommender systems objectives can be broadly characterized as modeling user
preferences over short-or long-term time horizon. A large body of previous
research studied long-term recommendation through dimensionality reduction
techniques applied to the historical user-item interactions. A recently
introduced session-based recommendation setting highlighted the importance of
modeling short-term user preferences. In this task, Recurrent Neural Networks
(RNN) have shown to be successful at capturing the nuances of user's
interactions within a short time window. In this paper, we evaluate RNN-based
models on both short-term and long-term recommendation tasks. Our experimental
results suggest that RNNs are capable of predicting immediate as well as
distant user interactions. We also find the best performing configuration to be
a stacked RNN with layer normalization and tied item embeddings.
Human peripheral blur is optimal for object recognition
Authors: R. T. Pramod, Harish Katti, S. P. Arun
Date: 2018-07-23 00:00:00
Tasks: Object, Object Recognition

Our vision is sharpest at the center of our gaze and becomes progressively blurry into the periphery. It is widely believed that this high foveal resolution evolved at the expense of peripheral acuity. But what if this sampling scheme is actually optimal for object recognition? To test this hypothesis, we trained deep neural networks on 'foveated' images with high resolution near objects and increasingly sparse sampling into the periphery. Neural networks trained using a blur profile matching the human eye yielded the best performance compared to shallower and steeper blur profiles. Even in humans, categorization accuracy deteriorated only for steeper blur profiles. Thus, our blurry peripheral vision may have evolved to optimize object recognition rather than merely due to wiring constraints.
A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging
Authors: Karan Aggarwal, Swaraj Khadanga, Shafiq R. Joty, Louis Kazaglis, Jaideep Srivastava
Date: 2018-07-23 00:00:00
Tasks: Sleep Staging

Sleep plays a vital role in human health, both mental and physical. Sleep
disorders like sleep apnea are increasing in prevalence, with the rapid
increase in factors like obesity. Sleep apnea is most commonly treated with
Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is
no mechanism to monitor a patient's progress with CPAP. Accurate detection of
sleep stages from CPAP flow signal is crucial for such a mechanism. We propose,
for the first time, an automated sleep staging model based only on the flow
signal. Deep neural networks have recently shown high accuracy on sleep staging
by eliminating handcrafted features. However, these methods focus exclusively
on extracting informative features from the input signal, without paying much
attention to the dynamics of sleep stages in the output sequence. We propose an
end-to-end framework that uses a combination of deep convolution and recurrent
neural networks to extract high-level features from raw flow signal with a
structured output layer based on a conditional random field to model the
temporal transition structure of the sleep stages. We improve upon the previous
methods by 10% using our model, that can be augmented to the previous sleep
staging deep learning methods. We also show that our method can be used to
accurately track sleep metrics like sleep efficiency calculated from sleep
stages that can be deployed for monitoring the response of CPAP therapy on
sleep apnea patients. Apart from the technical contributions, we expect this
study to motivate new research questions in sleep science.
Finite Time Adaptive Stabilization of LQ Systems
Authors: Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
Date: 2018-07-22 00:00:00

Stabilization of linear systems with unknown dynamics is a canonical problem
in adaptive control. Since the lack of knowledge of system parameters can cause
it to become destabilized, an adaptive stabilization procedure is needed prior
to regulation. Therefore, the adaptive stabilization needs to be completed in
finite time. In order to achieve this goal, asymptotic approaches are not very
helpful. There are only a few existing non-asymptotic results and a full
treatment of the problem is not currently available.
  In this work, leveraging the novel method of random linear feedbacks, we
establish high probability guarantees for finite time stabilization. Our
results hold for remarkably general settings because we carefully choose a
minimal set of assumptions. These include stabilizability of the underlying
system and restricting the degree of heaviness of the noise distribution. To
derive our results, we also introduce a number of new concepts and technical
tools to address regularity and instability of the closed-loop matrix.
Toward Scale-Invariance and Position-Sensitive Region Proposal Networks
Authors: Hsueh-Fu Lu, Xiaofei Du, Ping-Lin Chang
Date: 2018-07-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, object-detection, Object Detection, Position, Region Proposal, Translation

Accurately localising object proposals is an important precondition for high
detection rate for the state-of-the-art object detection frameworks. The
accuracy of an object detection method has been shown highly related to the
average recall (AR) of the proposals. In this work, we propose an advanced
object proposal network in favour of translation-invariance for objectness
classification, translation-variance for bounding box regression, large
effective receptive fields for capturing global context and scale-invariance
for dealing with a range of object sizes from extremely small to large. The
design of the network architecture aims to be simple while being effective and
with real time performance. Without bells and whistles the proposed object
proposal network significantly improves the AR at 1,000 proposals by $35\%$ and
$45\%$ on PASCAL VOC and COCO dataset respectively and has a fast inference
time of 44.8 ms for input image size of $640^{2}$. Empirical studies have also
shown that the proposed method is class-agnostic to be generalised for general
object proposal.
Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search
Authors: Karl Kurzer, Chenyang Zhou, J. Marius Zöllner
Date: 2018-07-25 00:00:00

Today's automated vehicles lack the ability to cooperate implicitly with
others. This work presents a Monte Carlo Tree Search (MCTS) based approach for
decentralized cooperative planning using macro-actions for automated vehicles
in heterogeneous environments. Based on cooperative modeling of other agents
and Decoupled-UCT (a variant of MCTS), the algorithm evaluates the
state-action-values of each agent in a cooperative and decentralized manner,
explicitly modeling the interdependence of actions between traffic
participants. Macro-actions allow for temporal extension over multiple time
steps and increase the effective search depth requiring fewer iterations to
plan over longer horizons. Without predefined policies for macro-actions, the
algorithm simultaneously learns policies over and within macro-actions. The
proposed method is evaluated under several conflict scenarios, showing that the
algorithm can achieve effective cooperative planning with learned macro-actions
in heterogeneous environments.
Flow-Grounded Spatial-Temporal Video Prediction from Still Images
Authors: Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang
Date: 2018-07-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: Diversity, Prediction, Video Prediction

Existing video prediction methods mainly rely on observing multiple
historical frames or focus on predicting the next one-frame. In this work, we
study the problem of generating consecutive multiple future frames by observing
one single still image only. We formulate the multi-frame prediction task as a
multiple time step flow (multi-flow) prediction phase followed by a
flow-to-frame synthesis phase. The multi-flow prediction is modeled in a
variational probabilistic manner with spatial-temporal relationships learned
through 3D convolutions. The flow-to-frame synthesis is modeled as a generative
process in order to keep the predicted results lying closer to the manifold
shape of real video sequence. Such a two-phase design prevents the model from
directly looking at the high-dimensional pixel space of the frame sequence and
is demonstrated to be more effective in predicting better and diverse results.
Extensive experimental results on videos with different types of motion show
that the proposed algorithm performs favorably against existing methods in
terms of quality, diversity and human perceptual evaluation.
PADME: A Deep Learning-based Framework for Drug-Target Interaction Prediction
Authors: Qingyuan Feng, Evgenia Dueva, Artem Cherkasov, Martin Ester
Date: 2018-07-25 00:00:00
Tasks: Feature Engineering, Prediction

In silico drug-target interaction (DTI) prediction is an important and challenging problem in biomedical research with a huge potential benefit to the pharmaceutical industry and patients. Most existing methods for DTI prediction including deep learning models generally have binary endpoints, which could be an oversimplification of the problem, and those methods are typically unable to handle cold-target problems, i.e., problems involving target protein that never appeared in the training set. Towards this, we contrived PADME (Protein And Drug Molecule interaction prEdiction), a framework based on Deep Neural Networks, to predict real-valued interaction strength between compounds and proteins without requiring feature engineering. PADME takes both compound and protein information as inputs, so it is capable of solving cold-target (and cold-drug) problems. To our knowledge, we are the first to combine Molecular Graph Convolution (MGC) for compound featurization with protein descriptors for DTI prediction. We used multiple cross-validation split schemes and evaluation metrics to measure the performance of PADME on multiple datasets, including the ToxCast dataset, and PADME consistently dominates baseline methods. The results of a case study, which predicts the binding affinity between various compounds and androgen receptor (AR), suggest PADME's potential in drug development. The scalability of PADME is another advantage in the age of Big Data.
Convergence Rates of Gaussian ODE Filters
Authors: Hans Kersting, T. J. Sullivan, Philipp Hennig
Date: 2018-07-25 00:00:00

A recently-introduced class of probabilistic (uncertainty-aware) solvers for ordinary differential equations (ODEs) applies Gaussian (Kalman) filtering to initial value problems. These methods model the true solution $x$ and its first $q$ derivatives \emph{a priori} as a Gauss--Markov process $\boldsymbol{X}$, which is then iteratively conditioned on information about $\dot{x}$. This article establishes worst-case local convergence rates of order $q+1$ for a wide range of versions of this Gaussian ODE filter, as well as global convergence rates of order $q$ in the case of $q=1$ and an integrated Brownian motion prior, and analyses how inaccurate information on $\dot{x}$ coming from approximate evaluations of $f$ affects these rates. Moreover, we show that, in the globally convergent case, the posterior credible intervals are well calibrated in the sense that they globally contract at the same rate as the truncation error. We illustrate these theoretical results by numerical experiments which might indicate their generalizability to $q \in \{2,3,\dots\}$.
Deep Unsupervised Multi-View Detection of Video Game Stream Highlights
Authors: Charles Ringer, Mihalis A. Nicolaou
Date: 2018-07-25 00:00:00
Tasks: Highlight Detection, multi-view detection

We consider the problem of automatic highlight-detection in video game
streams. Currently, the vast majority of highlight-detection systems for games
are triggered by the occurrence of hard-coded game events (e.g., score change,
end-game), while most advanced tools and techniques are based on detection of
highlights via visual analysis of game footage. We argue that in the context of
game streaming, events that may constitute highlights are not only dependent on
game footage, but also on social signals that are conveyed by the streamer
during the play session (e.g., when interacting with viewers, or when
commenting and reacting to the game). In this light, we present a multi-view
unsupervised deep learning methodology for novelty-based highlight detection.
The method jointly analyses both game footage and social signals such as the
players facial expressions and speech, and shows promising results for
generating highlights on streams of popular games such as Player Unknown's
Battlegrounds.
Asynchronous, Photometric Feature Tracking using Events and Frames
Authors: Daniel Gehrig, Henri Rebecq, Guillermo Gallego, Davide Scaramuzza
Date: 2018-07-25 00:00:00
Proceeding: ECCV 2018 9

We present a method that leverages the complementarity of event cameras and
standard cameras to track visual features with low-latency. Event cameras are
novel sensors that output pixel-level brightness changes, called "events". They
offer significant advantages over standard cameras, namely a very high dynamic
range, no motion blur, and a latency in the order of microseconds. However,
because the same scene pattern can produce different events depending on the
motion direction, establishing event correspondences across time is
challenging. By contrast, standard cameras provide intensity measurements
(frames) that do not depend on motion direction. Our method extracts features
on frames and subsequently tracks them asynchronously using events, thereby
exploiting the best of both types of data: the frames provide a photometric
representation that does not depend on motion direction and the events provide
low-latency updates. In contrast to previous works, which are based on
heuristics, this is the first principled method that uses raw intensity
measurements directly, based on a generative event model within a
maximum-likelihood framework. As a result, our method produces feature tracks
that are both more accurate (subpixel accuracy) and longer than the state of
the art, across a wide variety of scenes.
Limitations of the Lipschitz constant as a defense against adversarial examples
Authors: Todd Huster, Cho-Yu Jason Chiang, Ritu Chadha
Date: 2018-07-25 00:00:00

Several recent papers have discussed utilizing Lipschitz constants to limit
the susceptibility of neural networks to adversarial examples. We analyze
recently proposed methods for computing the Lipschitz constant. We show that
the Lipschitz constant may indeed enable adversarially robust neural networks.
However, the methods currently employed for computing it suffer from
theoretical and practical limitations. We argue that addressing this
shortcoming is a promising direction for future research into certified
adversarial defenses.
The Time-SIFT method : detecting 3-D changes from archival photogrammetric analysis with almost exclusively image information
Authors: Denis Feurer, Fabrice Vinatier
Date: 2018-07-25 00:00:00

Archival aerial imagery is a source of worldwide very high resolution data
for documenting paste 3-D changes. However, external information is required so
that accurate 3-D models can be computed from archival aerial imagery. In this
research, we propose and test a new method, termed Time-SIFT (Scale Invariant
Feature Transform), which allows for computing coherent multi-temporal Digital
Elevation Models (DEMs) with almost exclusively image information. This method
is based on the invariance properties of the SIFT-like methods which are at the
root of the Structure from Motion (SfM) algorithms. On a test site of 170 km2,
we applied SfM algorithms to a unique image block with all the images of four
different dates covering forty years. We compared this method to more classical
methods based on the use of affordable additional data such as ground control
points collected in recent orthophotos. We did extensive tests to determine
which processing choices were most impacting on the final result. With these
tests, we aimed at evaluating the potential of the proposed Time-SIFT method
for the detection and mapping of 3-D changes. Our study showed that the
Time-SIFT method was the prime criteria that allowed for computing informative
DEMs of difference with almost exclusively image information and limited
photogrammetric expertise and human intervention. Due to the fact that the
proposed Time-SIFT method can be automatically applied with exclusively image
information, our results pave the way to a systematic processing of the
archival aerial imagery on very large spatio-temporal windows, and should hence
greatly help the unlocking of archival aerial imagery for the documenting of
past 3-D changes.
Grounding Visual Explanations
Authors: Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, Zeynep Akata
Date: 2018-07-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: AI Agent, General Classification, Sentence

Existing visual explanation generating agents learn to fluently justify a
class prediction. However, they may mention visual attributes which reflect a
strong class prior, although the evidence may not actually be in the image.
This is particularly concerning as ultimately such agents fail in building
trust with human users. To overcome this limitation, we propose a phrase-critic
model to refine generated candidate explanations augmented with flipped phrases
which we use as negative examples while training. At inference time, our
phrase-critic model takes an image and a candidate explanation as input and
outputs a score indicating how well the candidate explanation is grounded in
the image. Our explainable AI agent is capable of providing counter arguments
for an alternative prediction, i.e. counterfactuals, along with explanations
that justify the correct classification decisions. Our model improves the
textual explanation quality of fine-grained classification decisions on the CUB
dataset by mentioning phrases that are grounded in the image. Moreover, on the
FOIL tasks, our agent detects when there is a mistake in the sentence, grounds
the incorrect phrase and corrects it significantly better than other models.
A Novel ILP Framework for Summarizing Content with High Lexical Variety
Authors: Wencan Luo, Fei Liu, Zitao Liu, Diane Litman
Date: 2018-07-25 00:00:00
Tasks: Abstractive Text Summarization, Articles, Diversity, Sentence, Vocal Bursts Intensity Prediction

Summarizing content contributed by individuals can be challenging, because
people make different lexical choices even when describing the same events.
However, there remains a significant need to summarize such content. Examples
include the student responses to post-class reflective questions, product
reviews, and news articles published by different news agencies related to the
same events. High lexical diversity of these documents hinders the system's
ability to effectively identify salient content and reduce summary redundancy.
In this paper, we overcome this issue by introducing an integer linear
programming-based summarization framework. It incorporates a low-rank
approximation to the sentence-word co-occurrence matrix to intrinsically group
semantically-similar lexical items. We conduct extensive experiments on
datasets of student responses, product reviews, and news documents. Our
approach compares favorably to a number of extractive baselines as well as a
neural abstractive summarization system. The paper finally sheds light on when
and why the proposed framework is effective at summarizing content with high
lexical variety.