pwc-archive/papers-with-abstracts huggingface dataset:
Constrained Thompson Sampling for Wireless Link Optimization
Authors: Vidit Saxena, Joseph E. Gonzalez, Ion Stoica, Hugo Tullberg, Joakim Jaldén
Date: 2019-02-28 00:00:00
Tasks: Thompson Sampling

Wireless communication systems operate in complex time-varying environments. Therefore, selecting the optimal configuration parameters in these systems is a challenging problem. For wireless links, \emph{rate selection} is used to select the optimal data transmission rate that maximizes the link throughput subject to an application-defined latency constraint. We model rate selection as a stochastic multi-armed bandit (MAB) problem, where a finite set of transmission rates are modeled as independent bandit arms. For this setup, we propose Con-TS, a novel constrained version of the Thompson sampling algorithm, where the latency requirement is modeled by a high-probability linear constraint. We show that for Con-TS, the expected number of constraint violations over T transmission intervals is upper bounded by O(\sqrt{KT}), where K is the number of available rates. Further, the expected loss in cumulative throughput compared to the optimal rate selection scheme (i.e., the egret is also upper bounded by O(\sqrt{KT \log K}). Through numerical simulations, we demonstrate that Con-TS significantly outperforms state-of-the-art bandit schemes for rate selection.
Real-time 3D Shape Instantiation for Partially-deployed Stent Segment from a Single 2D Fluoroscopic Image in Robot-assisted Fenestrated Endovascular Aortic Repair
Authors: Jian-Qing Zheng, Xiao-Yun Zhou, Guang-Zhong Yang
Date: 2019-02-28 00:00:00
Tasks: 3D Shape Reconstruction

In robot-assisted Fenestrated Endovascular Aortic Repair (FEVAR), accurate
alignment of stent graft fenestrations or scallops with aortic branches is
essential for establishing complete blood flow perfusion. Current navigation is
largely based on 2D fluoroscopic images, which lacks 3D anatomical information,
thus causing longer operation time as well as high risks of radiation exposure.
Previously, 3D shape instantiation frameworks for real-time 3D shape
reconstruction of fully-deployed or fully-compressed stent graft from a single
2D fluoroscopic image have been proposed for 3D navigation in robot-assisted
FEVAR. However, these methods could not instantiate partially-deployed stent
segments, as the 3D marker references are unknown. In this paper, an adapted
Graph Convolutional Network (GCN) is proposed to predict 3D marker references
from 3D fully-deployed markers. As original GCN is for classification, in this
paper, the coarsening layers are removed and the softmax function at the
network end is replaced with linear mapping for the regression task. The
derived 3D and the 2D marker references are used to instantiate
partially-deployed stent segment shape with the existing 3D shape instantiation
framework. Validations were performed on three commonly used stent grafts and
five patient-specific 3D printed aortic aneurysm phantoms. Comparable
performances with average mesh distance errors of 1$\sim$3mm and average
angular errors around 7degree were achieved.
Scaling Matters in Deep Structured-Prediction Models
Authors: Aleksandr Shevchenko, Anton Osokin
Date: 2019-02-28 00:00:00
Tasks: Prediction, Structured Prediction

Deep structured-prediction energy-based models combine the expressive power
of learned representations and the ability of embedding knowledge about the
task at hand into the system. A common way to learn parameters of such models
consists in a multistage procedure where different combinations of components
are trained at different stages. The joint end-to-end training of the whole
system is then done as the last fine-tuning stage. This multistage approach is
time-consuming and cumbersome as it requires multiple runs until convergence
and multiple rounds of hyperparameter tuning. From this point of view, it is
beneficial to start the joint training procedure from the beginning. However,
such approaches often unexpectedly fail and deliver results worse than the
multistage ones. In this paper, we hypothesize that one reason for joint
training of deep energy-based models to fail is the incorrect relative
normalization of different components in the energy function. We propose online
and offline scaling algorithms that fix the joint training and demonstrate
their efficacy on three different tasks.
Rolling Shutter Camera Synchronization with Sub-millisecond Accuracy
Authors: Matej Smid, Jiri Matas
Date: 2019-02-28 00:00:00
Tasks: Video Synchronization

A simple method for synchronization of video streams with a precision better
than one millisecond is proposed. The method is applicable to any number of
rolling shutter cameras and when a few photographic flashes or other abrupt
lighting changes are present in the video. The approach exploits the rolling
shutter sensor property that every sensor row starts its exposure with a small
delay after the onset of the previous row. The cameras may have different frame
rates and resolutions, and need not have overlapping fields of view. The method
was validated on five minutes of four streams from an ice hockey match. The
found transformation maps events visible in all cameras to a reference time
with a standard deviation of the temporal error in the range of 0.3 to 0.5
milliseconds. The quality of the synchronization is demonstrated on temporally
and spatially overlapping images of a fast moving puck observed in two cameras.
AFS: An Attention-based mechanism for Supervised Feature Selection
Authors: Ning Gui, Danni Ge, Ziyin Hu
Date: 2019-02-28 00:00:00
Tasks: Binary Classification, feature selection

As an effective data preprocessing step, feature selection has shown its
effectiveness to prepare high-dimensional data for many machine learning tasks.
The proliferation of high di-mension and huge volume big data, however, has
brought major challenges, e.g. computation complexity and stability on noisy
data, upon existing feature-selection techniques. This paper introduces a novel
neural network-based feature selection architecture, dubbed Attention-based
Feature Selec-tion (AFS). AFS consists of two detachable modules: an at-tention
module for feature weight generation and a learning module for the problem
modeling. The attention module for-mulates correlation problem among features
and supervision target into a binary classification problem, supported by a
shallow attention net for each feature. Feature weights are generated based on
the distribution of respective feature se-lection patterns adjusted by
backpropagation during the train-ing process. The detachable structure allows
existing off-the-shelf models to be directly reused, which allows for much less
training time, demands for the training data and requirements for expertise. A
hybrid initialization method is also intro-duced to boost the selection
accuracy for datasets without enough samples for feature weight generation.
Experimental results show that AFS achieves the best accuracy and stability in
comparison to several state-of-art feature selection algo-rithms upon both
MNIST, noisy MNIST and several datasets with small samples.
Biometric Presentation Attack Detection: Beyond the Visible Spectrum
Authors: Ruben Tolosana, Marta Gomez-Barrero, Christoph Busch, Javier Ortega-Garcia
Date: 2019-02-28 00:00:00

The increased need for unattended authentication in multiple scenarios has
motivated a wide deployment of biometric systems in the last few years. This
has in turn led to the disclosure of security concerns specifically related to
biometric systems. Among them, Presentation Attacks (PAs, i.e., attempts to log
into the system with a fake biometric characteristic or presentation attack
instrument) pose a severe threat to the security of the system: any person
could eventually fabricate or order a gummy finger or face mask to impersonate
someone else. The biometrics community has thus made a considerable effort to
the development of automatic Presentation Attack Detection (PAD) mechanisms,
for instance through the international LivDet competitions.
  In this context, we present a novel fingerprint PAD scheme based on $i)$ a
new capture device able to acquire images within the short wave infrared (SWIR)
spectrum, and $ii)$ an in-depth analysis of several state-of-the-art techniques
based on both handcrafted and deep learning features. The approach is evaluated
on a database comprising over 4700 samples, stemming from 562 different
subjects and 35 different presentation attack instrument (PAI) species. The
results show the soundness of the proposed approach with a detection equal
error rate (D-EER) as low as 1.36\% even in a realistic scenario where five
different PAI species are considered only for testing purposes (i.e., unknown
attacks).
Context-aware Neural-based Dialog Act Classification on Automatically Generated Transcriptions
Authors: Daniel Ortega, Chia-Yu Li, Gisela Vallejo, Pavel Denisov, Ngoc Thang Vu
Date: 2019-02-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Classification, Dialog Act Classification, General Classification, speech-recognition, Speech Recognition

This paper presents our latest investigations on dialog act (DA)
classification on automatically generated transcriptions. We propose a novel
approach that combines convolutional neural networks (CNNs) and conditional
random fields (CRFs) for context modeling in DA classification. We explore the
impact of transcriptions generated from different automatic speech recognition
systems such as hybrid TDNN/HMM and End-to-End systems on the final
performance. Experimental results on two benchmark datasets (MRDA and SwDA)
show that the combination CNN and CRF improves consistently the accuracy.
Furthermore, they show that although the word error rates are comparable,
End-to-End ASR system seems to be more suitable for DA classification.
Representation Learning for Recommender Systems with Application to the Scientific Literature
Authors: Robin Brochier
Date: 2019-02-28 00:00:00
Tasks: Network Embedding, Recommendation Systems, Representation Learning

The scientific literature is a large information network linking various
actors (laboratories, companies, institutions, etc.). The vast amount of data
generated by this network constitutes a dynamic heterogeneous attributed
network (HAN), in which new information is constantly produced and from which
it is increasingly difficult to extract content of interest. In this article, I
present my first thesis works in partnership with an industrial company,
Digital Scientific Research Technology. This later offers a scientific watch
tool, Peerus, addressing various issues, such as the real time recommendation
of newly published papers or the search for active experts to start new
collaborations. To tackle this diversity of applications, a common approach
consists in learning representations of the nodes and attributes of this HAN
and use them as features for a variety of recommendation tasks. However, most
works on attributed network embedding pay too little attention to textual
attributes and do not fully take advantage of recent natural language
processing techniques. Moreover, proposed methods that jointly learn node and
document representations do not provide a way to effectively infer
representations for new documents for which network information is missing,
which happens to be crucial in real time recommender systems. Finally, the
interplay between textual and graph data in text-attributed heterogeneous
networks remains an open research direction.
Link Prediction with Mutual Attention for Text-Attributed Networks
Authors: Robin Brochier, Adrien Guille, Julien Velcin
Date: 2019-02-28 00:00:00
Tasks: Link Prediction, Prediction

In this extended abstract, we present an algorithm that learns a similarity
measure between documents from the network topology of a structured corpus. We
leverage the Scaled Dot-Product Attention, a recently proposed attention
mechanism, to design a mutual attention mechanism between pairs of documents.
To train its parameters, we use the network links as supervision. We provide
preliminary experiment results with a citation dataset on two prediction tasks,
demonstrating the capacity of our model to learn a meaningful textual
similarity.
Segmentation of Roots in Soil with U-Net
Authors: Abraham George Smith, Jens Petersen, Raghavendra Selvan, Camilla Ruø Rasmussen
Date: 2019-02-28 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Plant root research can provide a way to attain stress-tolerant crops that
produce greater yield in a diverse array of conditions. Phenotyping roots in
soil is often challenging due to the roots being difficult to access and the
use of time consuming manual methods. Rhizotrons allow visual inspection of
root growth through transparent surfaces. Agronomists currently manually label
photographs of roots obtained from rhizotrons using a line-intersect method to
obtain root length density and rooting depth measurements which are essential
for their experiments. We investigate the effectiveness of an automated image
segmentation method based on the U-Net Convolutional Neural Network (CNN)
architecture to enable such measurements. We design a data-set of 50 annotated
Chicory (Cichorium intybus L.) root images which we use to train, validate and
test the system and compare against a baseline built using the Frangi
vesselness filter. We obtain metrics using manual annotations and
line-intersect counts. Our results on the held out data show our proposed
automated segmentation system to be a viable solution for detecting and
quantifying roots. We evaluate our system using 867 images for which we have
obtained line-intersect counts, attaining a Spearman rank correlation of 0.9748
and an $r^2$ of 0.9217. We also achieve an $F_1$ of 0.7 when comparing the
automated segmentation to the manual annotations, with our automated
segmentation system producing segmentations with higher quality than the manual
annotations for large portions of the image.
Evaluating Rewards for Question Generation Models
Authors: Tom Hosking, Sebastian Riedel
Date: 2019-02-28 00:00:00
Proceeding: NAACL 2019 6
Tasks: Machine Translation, Policy Gradient Methods, Question Generation, Question-Generation, Reinforcement Learning, Translation

Recent approaches to question generation have used modifications to a Seq2Seq architecture inspired by advances in machine translation. Models are trained using teacher forcing to optimise only the one-step-ahead prediction. However, at test time, the model is asked to generate a whole sequence, causing errors to propagate through the generation process (exposure bias). A number of authors have proposed countering this bias by optimising for a reward that is less tightly coupled to the training data, using reinforcement learning. We optimise directly for quality metrics, including a novel approach using a discriminator learned directly from the training data. We confirm that policy gradient methods can be used to decouple training from the ground truth, leading to increases in the metrics used as rewards. We perform a human evaluation, and show that although these metrics have previously been assumed to be good proxies for question quality, they are poorly aligned with human judgement and the model simply learns to exploit the weaknesses of the reward source.
GCNv2: Efficient Correspondence Prediction for Real-Time SLAM
Authors: Jiexiong Tang, Ludvig Ericson, John Folkesson, Patric Jensfelt
Date: 2019-02-28 00:00:00
Tasks: Computational Efficiency, Prediction

In this paper, we present a deep learning-based network, GCNv2, for generation of keypoints and descriptors. GCNv2 is built on our previous method, GCN, a network trained for 3D projective geometry. GCNv2 is designed with a binary descriptor vector as the ORB feature so that it can easily replace ORB in systems such as ORB-SLAM2. GCNv2 significantly improves the computational efficiency over GCN that was only able to run on desktop hardware. We show how a modified version of ORB-SLAM2 using GCNv2 features runs on a Jetson TX2, an embedded low-power platform. Experimental results show that GCNv2 retains comparable accuracy as GCN and that it is robust enough to use for control of a flying drone.
Virtual Adversarial Training on Graph Convolutional Networks in Node Classification
Authors: Ke Sun, Zhouchen Lin, Hantao Guo, Zhanxing Zhu
Date: 2019-02-28 00:00:00
Tasks: Classification, General Classification, Node Classification

The effectiveness of Graph Convolutional Networks (GCNs) has been demonstrated in a wide range of graph-based machine learning tasks. However, the update of parameters in GCNs is only from labeled nodes, lacking the utilization of unlabeled data. In this paper, we apply Virtual Adversarial Training (VAT), an adversarial regularization method based on both labeled and unlabeled data, on the supervised loss of GCN to enhance its generalization performance. By imposing virtually adversarial smoothness on the posterior distribution in semi-supervised learning, VAT yields improvement on the Symmetrical Laplacian Smoothness of GCNs. In addition, due to the difference of property in features, we perturb virtual adversarial perturbations on sparse and dense features, resulting in GCN Sparse VAT (GCNSVAT) and GCN Dense VAT (GCNDVAT) algorithms, respectively. Extensive experiments verify the effectiveness of our two methods across different training sizes. Our work paves the way towards better understanding the direction of improvement on GCNs in the future.
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels
Authors: Ke Sun, Zhouchen Lin, Zhanxing Zhu
Date: 2019-02-28 00:00:00
Tasks: Graph Embedding, Graph Learning, Self-Supervised Learning

Graph Convolutional Networks(GCNs) play a crucial role in graph learning tasks, however, learning graph embedding with few supervised signals is still a difficult problem. In this paper, we propose a novel training algorithm for Graph Convolutional Network, called Multi-Stage Self-Supervised(M3S) Training Algorithm, combined with self-supervised learning approach, focusing on improving the generalization performance of GCNs on graphs with few labeled nodes. Firstly, a Multi-Stage Training Framework is provided as the basis of M3S training method. Then we leverage DeepCluster technique, a popular form of self-supervised learning, and design corresponding aligning mechanism on the embedding space to refine the Multi-Stage Training Framework, resulting in M3S Training Algorithm. Finally, extensive experimental results verify the superior performance of our algorithm on graphs with few labeled nodes under different label rates compared with other state-of-the-art approaches.
Unsupervised Abnormality Detection through Mixed Structure Regularization (MSR) in Deep Sparse Autoencoders
Authors: Moti Freiman, Ravindra Manjeshwar, Liran Goshen
Date: 2019-02-28 00:00:00
Tasks: Anomaly Detection, Denoising

Deep sparse auto-encoders with mixed structure regularization (MSR) in
addition to explicit sparsity regularization term and stochastic corruption of
the input data with Gaussian noise have the potential to improve unsupervised
abnormality detection. Unsupervised abnormality detection based on identifying
outliers using deep sparse auto-encoders is a very appealing approach for
medical computer aided detection systems as it requires only healthy data for
training rather than expert annotated abnormality.
  In the task of detecting coronary artery disease from Coronary Computed
Tomography Angiography (CCTA), our results suggests that the MSR has the
potential to improve overall performance by 20-30% compared to deep sparse and
denoising auto-encoders.
SAFE ML: Surrogate Assisted Feature Extraction for Model Learning
Authors: Alicja Gosiewska, Aleksandra Gacek, Piotr Lubon, Przemyslaw Biecek
Date: 2019-02-28 00:00:00
Tasks: AutoML, Feature Engineering

Complex black-box predictive models may have high accuracy, but opacity
causes problems like lack of trust, lack of stability, sensitivity to concept
drift. On the other hand, interpretable models require more work related to
feature engineering, which is very time consuming. Can we train interpretable
and accurate models, without timeless feature engineering? In this article, we
show a method that uses elastic black-boxes as surrogate models to create a
simpler, less opaque, yet still accurate and interpretable glass-box models.
New models are created on newly engineered features extracted/learned with the
help of a surrogate model. We show applications of this method for model level
explanations and possible extensions for instance level explanations. We also
present an example implementation in Python and benchmark this method on a
number of tabular data sets.
Multispectral snapshot demosaicing via non-convex matrix completion
Authors: Giancarlo A. Antonucci, Simon Vary, David Humphreys, Robert A. Lamb, Jonathan Piper, Jared Tanner
Date: 2019-02-28 00:00:00
Tasks: Demosaicking, Matrix Completion

Snapshot mosaic multispectral imagery acquires an undersampled data cube by
acquiring a single spectral measurement per spatial pixel. Sensors which
acquire $p$ frequencies, therefore, suffer from severe $1/p$ undersampling of
the full data cube. We show that the missing entries can be accurately imputed
using non-convex techniques from sparse approximation and matrix completion
initialised with traditional demosaicing algorithms. In particular, we observe
the peak signal-to-noise ratio can typically be improved by 2 to 5 dB over
current state-of-the-art methods when simulating a $p=16$ mosaic sensor
measuring both high and low altitude urban and rural scenes as well as
ground-based scenes.
Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN
Authors: Ke Sun, Zhanxing Zhu, Zhouchen Lin
Date: 2019-02-28 00:00:00
Tasks: Data Augmentation

Deep neural networks have been widely deployed in various machine learning
tasks. However, recent works have demonstrated that they are vulnerable to
adversarial examples: carefully crafted small perturbations to cause
misclassification by the network. In this work, we propose a novel defense
mechanism called Boundary Conditional GAN to enhance the robustness of deep
neural networks against adversarial examples. Boundary Conditional GAN, a
modified version of Conditional GAN, can generate boundary samples with true
labels near the decision boundary of a pre-trained classifier. These boundary
samples are fed to the pre-trained classifier as data augmentation to make the
decision boundary more robust. We empirically show that the model improved by
our approach consistently defenses against various types of adversarial attacks
successfully. Further quantitative investigations about the improvement of
robustness and visualization of decision boundaries are also provided to
justify the effectiveness of our strategy. This new defense mechanism that uses
boundary samples to enhance the robustness of networks opens up a new way to
defense adversarial attacks consistently.