pwc-archive/papers-with-abstracts huggingface dataset:
Multi-hop assortativities for networks classification
Authors: Leonardo Gutierrez Gomez, Jean-Charles Delvenne
Date: 2018-09-14 00:00:00
Tasks: Classification, General Classification

Several social, medical, engineering and biological challenges rely on
discovering the functionality of networks from their structure and node
metadata, when it is available. For example, in chemoinformatics one might want
to detect whether a molecule is toxic based on structure and atomic types, or
discover the research field of a scientific collaboration network. Existing
techniques rely on counting or measuring structural patterns that are known to
show large variations from network to network, such as the number of triangles,
or the assortativity of node metadata. We introduce the concept of multi-hop
assortativity, that captures the similarity of the nodes situated at the
extremities of a randomly selected path of a given length. We show that
multi-hop assortativity unifies various existing concepts and offers a
versatile family of 'fingerprints' to characterize networks. These fingerprints
allow in turn to recover the functionalities of a network, with the help of the
machine learning toolbox. Our method is evaluated empirically on established
social and chemoinformatic network benchmarks. Results reveal that our
assortativity based features are competitive providing highly accurate results
often outperforming state of the art methods for the network classification
task.
Left Ventricle Segmentation and Volume Estimation on Cardiac MRI using Deep Learning
Authors: Ehab Abdelmaguid, Jolene Huang, Sanjay Kenchareddy, Disha Singla, Laura Wilke, Mai H. Nguyen, Ilkay Altintas
Date: 2018-09-14 00:00:00
Tasks: Distributed Computing, GPU, Left Ventricle Segmentation, LV Segmentation, Management

In the United States, heart disease is the leading cause of death for both
men and women, accounting for 610,000 deaths each year [1]. Physicians use
Magnetic Resonance Imaging (MRI) scans to take images of the heart in order to
non-invasively estimate its structural and functional parameters for
cardiovascular diagnosis and disease management. The end-systolic volume (ESV)
and end-diastolic volume (EDV) of the left ventricle (LV), and the ejection
fraction (EF) are indicators of heart disease. These measures can be derived
from the segmented contours of the LV; thus, consistent and accurate
segmentation of the LV from MRI images are critical to the accuracy of the ESV,
EDV, and EF, and to non-invasive cardiac disease detection.
  In this work, various image preprocessing techniques, model configurations
using the U-Net deep learning architecture, postprocessing methods, and
approaches for volume estimation are investigated. An end-to-end analytics
pipeline with multiple stages is provided for automated LV segmentation and
volume estimation. First, image data are reformatted and processed from DICOM
and NIfTI formats to raw images in array format. Secondly, raw images are
processed with multiple image preprocessing methods and cropped to include only
the Region of Interest (ROI). Thirdly, preprocessed images are segmented using
U-Net models. Lastly, post processing of segmented images to remove extra
contours along with intelligent slice and frame selection are applied, followed
by calculation of the ESV, EDV, and EF. This analytics pipeline is implemented
and runs on a distributed computing environment with a GPU cluster at the San
Diego Supercomputer Center at UCSD.
GANs for Medical Image Analysis
Authors: Salome Kazeminia, Christoph Baur, Arjan Kuijper, Bram van Ginneken, Nassir Navab, Shadi Albarqouni, Anirban Mukhopadhyay
Date: 2018-09-13 00:00:00
Tasks: General Classification, Medical Image Analysis

Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification. Furthermore, their ability to synthesize images at unprecedented levels of realism also gives hope that the chronic scarcity of labeled data in the medical field can be resolved with the help of these generative models. In this review paper, a broad overview of recent literature on GANs for medical applications is given, the shortcomings and opportunities of the proposed methods are thoroughly discussed and potential future work is elaborated. We review the most relevant papers published until the submission date. For quick access, important details such as the underlying method, datasets and performance are tabulated. An interactive visualization which categorizes all papers to keep the review alive, is available at http://livingreview.in.tum.de/GANs_for_Medical_Applications.
Improving Reinforcement Learning Based Image Captioning with Natural Language Prior
Authors: Tszhang Guo, Shiyu Chang, Mo Yu, Kun Bai
Date: 2018-09-13 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Image Captioning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recently, Reinforcement Learning (RL) approaches have demonstrated advanced
performance in image captioning by directly optimizing the metric used for
testing. However, this shaped reward introduces learning biases, which reduces
the readability of generated text. In addition, the large sample space makes
training unstable and slow. To alleviate these issues, we propose a simple
coherent solution that constrains the action space using an n-gram language
prior. Quantitative and qualitative evaluations on benchmarks show that RL with
the simple add-on module performs favorably against its counterpart in terms of
both readability and speed of convergence. Human evaluation results show that
our model is more human readable and graceful. The implementation will become
publicly available upon the acceptance of the paper.
Linear and Deformable Image Registration with 3D Convolutional Neural Networks
Authors: Stergios Christodoulidis, Mihir Sahasrabudhe, Maria Vakalopoulou, Guillaume Chassagnon, Marie-Pierre Revel, Stavroula Mougiakakou, Nikos Paragios
Date: 2018-09-13 00:00:00
Tasks: General Classification, Image Registration

Image registration and in particular deformable registration methods are
pillars of medical imaging. Inspired by the recent advances in deep learning,
we propose in this paper, a novel convolutional neural network architecture
that couples linear and deformable registration within a unified architecture
endowed with near real-time performance. Our framework is modular with respect
to the global transformation component, as well as with respect to the
similarity function while it guarantees smooth displacement fields. We evaluate
the performance of our network on the challenging problem of MRI lung
registration, and demonstrate superior performance with respect to state of the
art elastic registration methods. The proposed deformation (between inspiration
& expiration) was considered within a clinically relevant task of interstitial
lung disease (ILD) classification and showed promising results.
Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion Recognition
Authors: Zheng Lian, Ya Li, Jian-Hua Tao, Jian Huang
Date: 2018-09-13 00:00:00
Tasks: Emotion Classification, Emotion Recognition, Multimodal Emotion Recognition, Transfer Learning

Automatic emotion recognition is a challenging task. In this paper, we
present our effort for the audio-video based sub-challenge of the Emotion
Recognition in the Wild (EmotiW) 2018 challenge, which requires participants to
assign a single emotion label to the video clip from the six universal emotions
(Anger, Disgust, Fear, Happiness, Sad and Surprise) and Neutral. The proposed
multimodal emotion recognition system takes audio, video and text information
into account. Except for handcraft features, we also extract bottleneck
features from deep neutral networks (DNNs) via transfer learning. Both temporal
classifiers and non-temporal classifiers are evaluated to obtain the best
unimodal emotion classification result. Then possibilities are extracted and
passed into the Beam Search Fusion (BS-Fusion). We test our method in the
EmotiW 2018 challenge and we gain promising results. Compared with the baseline
system, there is a significant improvement. We achieve 60.34% accuracy on the
testing dataset, which is only 1.5% lower than the winner. It shows that our
method is very competitive.
Canonical Sectors and Evolution of Firms in the US Stock Markets
Authors: Lorien X. Hayden, Ricky Chachra, Alexander A. Alemi, Paul H. Ginsparg, James P. Sethna
Date: 2015-03-20 00:00:00
Tasks: Classification

A classification of companies into sectors of the economy is important for
macroeconomic analysis and for investments into the sector-specific financial
indices and exchange traded funds (ETFs). Major industrial classification
systems and financial indices have historically been based on expert opinion
and developed manually. Here we show how unsupervised machine learning can
provide a more objective and comprehensive broad-level sector decomposition of
stocks. An emergent low-dimensional structure in the space of historical stock
price returns automatically identifies "canonical sectors" in the market, and
assigns every stock a participation weight into these sectors. Furthermore, by
analyzing data from different periods, we show how these weights for listed
firms have evolved over time.
On the Learning Dynamics of Deep Neural Networks
Authors: Remi Tachet, Mohammad Pezeshki, Samira Shabanian, Aaron Courville, Yoshua Bengio
Date: 2018-09-18 00:00:00
Tasks: Binary Classification, General Classification

While a lot of progress has been made in recent years, the dynamics of learning in deep nonlinear neural networks remain to this day largely misunderstood. In this work, we study the case of binary classification and prove various properties of learning in such networks under strong assumptions such as linear separability of the data. Extending existing results from the linear case, we confirm empirical observations by proving that the classification error also follows a sigmoidal shape in nonlinear architectures. We show that given proper initialization, learning expounds parallel independent modes and that certain regions of parameter space might lead to failed training. We also demonstrate that input norm and features' frequency in the dataset lead to distinct convergence speeds which might shed some light on the generalization capabilities of deep neural networks. We provide a comparison between the dynamics of learning with cross-entropy and hinge losses, which could prove useful to understand recent progress in the training of generative adversarial networks. Finally, we identify a phenomenon that we baptize gradient starvation where the most frequent features in a dataset prevent the learning of other less frequent but equally informative features.
MNIST Dataset Classification Utilizing k-NN Classifier with Modified Sliding-window Metric
Authors: Divas Grover, Behrad Toghi
Date: 2018-09-18 00:00:00
Tasks: Classification, General Classification

The MNIST dataset of the handwritten digits is known as one of the commonly
used datasets for machine learning and computer vision research. We aim to
study a widely applicable classification problem and apply a simple yet
efficient K-nearest neighbor classifier with an enhanced heuristic. We evaluate
the performance of the K-nearest neighbor classification algorithm on the MNIST
dataset where the $L2$ Euclidean distance metric is compared to a modified
distance metric which utilizes the sliding window technique in order to avoid
performance degradation due to slight spatial misalignments. The accuracy
metric and confusion matrices are used as the performance indicators to compare
the performance of the baseline algorithm versus the enhanced sliding window
method and results show significant improvement using this proposed method.
Albumentations: fast and flexible image augmentations
Authors: Alexander Buslaev, Alex Parinov, Eugene Khvedchenya, Vladimir I. Iglovikov, Alexandr A. Kalinin
Date: 2018-09-18 00:00:00
Tasks: Data Augmentation, Diversity, Image Augmentation

Data augmentation is a commonly used technique for increasing both the size
and the diversity of labeled training sets by leveraging input transformations
that preserve output labels. In computer vision domain, image augmentations
have become a common implicit regularization technique to combat overfitting in
deep convolutional neural networks and are ubiquitously used to improve
performance. While most deep learning frameworks implement basic image
transformations, the list is typically limited to some variations and
combinations of flipping, rotating, scaling, and cropping. Moreover, the image
processing speed varies in existing tools for image augmentation. We present
Albumentations, a fast and flexible library for image augmentations with many
various image transform operations available, that is also an easy-to-use
wrapper around other augmentation libraries. We provide examples of image
augmentations for different computer vision tasks and show that Albumentations
is faster than other commonly used image augmentation tools on the most of
commonly used image transformations. The source code for Albumentations is made
publicly available online at https://github.com/albu/albumentations
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks
Authors: Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes
Date: 2018-09-18 00:00:00

Gene regulatory networks play a crucial role in controlling an organism's
biological processes, which is why there is significant interest in developing
computational methods that are able to extract their structure from
high-throughput genetic data. A typical approach consists of a series of
conditional independence tests on the covariance structure meant to
progressively reduce the space of possible causal models. We propose a novel
efficient Bayesian method for discovering the local causal relationships among
triplets of (normally distributed) variables. In our approach, we score the
patterns in the covariance matrix in one go and we incorporate the available
background knowledge in the form of priors over causal structures. Our method
is flexible in the sense that it allows for different types of causal
structures and assumptions. We apply the approach to the task of inferring gene
regulatory networks by learning regulatory relationships between gene
expression levels. We show that our algorithm produces stable and conservative
posterior probability estimates over local causal structures that can be used
to derive an honest ranking of the most meaningful regulatory relationships. We
demonstrate the stability and efficacy of our method both on simulated data and
on real-world data from an experiment on yeast.
Nonconvex Demixing From Bilinear Measurements
Authors: Jialin Dong, Yuanming Shi
Date: 2018-09-18 00:00:00
Tasks: Dictionary Learning

We consider the problem of demixing a sequence of source signals from the sum
of noisy bilinear measurements. It is a generalized mathematical model for
blind demixing with blind deconvolution, which is prevalent across the areas of
dictionary learning, image processing, and communications. However, state-of-
the-art convex methods for blind demixing via semidefinite programming are
computationally infeasible for large-scale problems. Although the existing
nonconvex algorithms are able to address the scaling issue, they normally
require proper regularization to establish optimality guarantees. The
additional regularization yields tedious algorithmic parameters and pessimistic
convergence rates with conservative step sizes. To address the limitations of
existing methods, we thus develop a provable nonconvex demixing procedure
viaWirtinger flow, much like vanilla gradient descent, to harness the benefits
of regularization-free fast convergence rate with aggressive step size and
computational optimality guarantees. This is achieved by exploiting the benign
geometry of the blind demixing problem, thereby revealing that Wirtinger flow
enforces the regularization-free iterates in the region of strong convexity and
qualified level of smoothness, where the step size can be chosen aggressively.
Generalized Content-Preserving Warps for Image Stitching
Authors: Kai Chen, Jingmin Tu, Jian Yao
Date: 2018-09-18 00:00:00
Tasks: Image Stitching

Local misalignment caused by global homography is a common issue in image
stitching task. Content-Preserving Warping (CPW) is a typical method to deal
with this issue, in which geometric and photometric constraints are imposed to
guide the warping process. One of its essential condition however, is colour
consistency, and an elusive goal in real world applications. In this paper, we
propose a Generalized Content-Preserving Warping (GCPW) method to alleviate
this problem. GCPW extends the original CPW by applying a colour model that
expresses the colour transformation between images locally, thus meeting the
photometric constraint requirements for effective image stitching. We combine
the photometric and geometric constraints and jointly estimate the colour
transformation and the warped mesh vertexes, simultaneously. We align images
locally with an optimal grid mesh generated by our GCPW method. Experiments on
both synthetic and real images demonstrate that our new method is robust to
colour variations, outperforming other state-of-the-art CPW-based image
stitching methods.
A generalized financial time series forecasting model based on automatic feature engineering using genetic algorithms and support vector machine
Authors: Norberto Ritzmann Junior, Julio Cesar Nievola
Date: 2018-09-18 00:00:00
Tasks: Feature Engineering, feature selection, Time Series, Time Series Analysis, Time Series Forecasting

We propose the genetic algorithm for time window optimization, which is an
embedded genetic algorithm (GA), to optimize the time window (TW) of the
attributes using feature selection and support vector machine. This GA is
evolved using the results of a trading simulation, and it determines the best
TW for each technical indicator. An appropriate evaluation was conducted using
a walk-forward trading simulation, and the trained model was verified to be
generalizable for forecasting other stock data. The results show that using the
GA to determine the TW can improve the rate of return, leading to better
prediction models than those resulting from using the default TW.
3D segmentation of mandible from multisectional CT scans by convolutional neural networks
Authors: Bingjiang Qiu, Jiapan Guo, J. Kraeima, R. J. H. Borra, M. J. H. Witjes, P. M. A. van Ooijen
Date: 2018-09-18 00:00:00
Tasks: Segmentation

Segmentation of mandibles in CT scans during virtual surgical planning is
crucial for 3D surgical planning in order to obtain a detailed surface
representation of the patients bone. Automatic segmentation of mandibles in CT
scans is a challenging task due to large variation in their shape and size
between individuals. In order to address this challenge we propose a
convolutional neural network approach for mandible segmentation in CT scans by
considering the continuum of anatomical structures through different planes.
The proposed convolutional neural network adopts the architecture of the U-Net
and then combines the resulting 2D segmentations from three different planes
into a 3D segmentation. We implement such a segmentation approach on 11 neck CT
scans and then evaluate the performance. We achieve an average dice coefficient
of $ 0.89 $ on two testing mandible segmentation. Experimental results show
that our proposed approach for mandible segmentation in CT scans exhibits high
accuracy.
From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers
Authors: James Large, Anthony Bagnall, Simon Malinowski, Romain Tavenard
Date: 2018-09-18 00:00:00
Tasks: General Classification, image-classification, Image Classification, Time Series, Time Series Analysis, Time Series Classification

A family of algorithms for time series classification (TSC) involve running a
sliding window across each series, discretising the window to form a word,
forming a histogram of word counts over the dictionary, then constructing a
classifier on the histograms. A recent evaluation of two of this type of
algorithm, Bag of Patterns (BOP) and Bag of Symbolic Fourier Approximation
Symbols (BOSS) found a significant difference in accuracy between these
seemingly similar algorithms. We investigate this phenomenon by deconstructing
the classifiers and measuring the relative importance of the four key
components between BOP and BOSS. We find that whilst ensembling is a key
component for both algorithms, the effect of the other components is mixed and
more complex. We conclude that BOSS represents the state of the art for
dictionary based TSC. Both BOP and BOSS can be classed as bag of words
approaches. These are particularly popular in Computer Vision for tasks such as
image classification. Converting approaches from vision requires careful
engineering. We adapt three techniques used in Computer Vision for TSC: Scale
Invariant Feature Transform; Spatial Pyramids; and Histrogram Intersection. We
find that using Spatial Pyramids in conjunction with BOSS (SP) produces a
significantly more accurate classifier. SP is significantly more accurate than
standard benchmarks and the original BOSS algorithm. It is not significantly
worse than the best shapelet based approach, and is only outperformed by
HIVE-COTE, an ensemble that includes BOSS as a constituent module.
Multiobjective Reinforcement Learning for Reconfigurable Adaptive Optimal Control of Manufacturing Processes
Authors: Johannes Dornheim, Norbert Link
Date: 2018-09-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In industrial applications of adaptive optimal control often multiple
contrary objectives have to be considered. The weights (relative importance) of
the objectives are often not known during the design of the control and can
change with changing production conditions and requirements. In this work a
novel model-free multiobjective reinforcement learning approach for adaptive
optimal control of manufacturing processes is proposed. The approach enables
sample-efficient learning in sequences of control configurations, given by
particular objective weights.
Transfer and Multi-Task Learning for Noun-Noun Compound Interpretation
Authors: Murhaf Fares, Stephan Oepen, Erik Velldal
Date: 2018-09-18 00:00:00
Tasks: Classification, General Classification, Multi-Task Learning, Transfer Learning

In this paper, we empirically evaluate the utility of transfer and multi-task
learning on a challenging semantic classification task: semantic interpretation
of noun--noun compounds. Through a comprehensive series of experiments and
in-depth error analysis, we show that transfer learning via parameter
initialization and multi-task learning via parameter sharing can help a neural
classification model generalize over a highly skewed distribution of relations.
Further, we demonstrate how dual annotation with two distinct sets of relations
over the same set of compounds can be exploited to improve the overall accuracy
of a neural classifier and its F1 scores on the less frequent, but more
difficult relations.