pwc-archive/papers-with-abstracts huggingface dataset:
Analysis Dictionary Learning: An Efficient and Discriminative Solution
Authors: Wen Tang, Ashkan Panahi, Hamid Krim, Liyi Dai
Date: 2019-03-07 00:00:00
Tasks: Dictionary Learning, General Classification, image-classification, Image Classification

Discriminative Dictionary Learning (DL) methods have been widely advocated
for image classification problems. To further sharpen their discriminative
capabilities, most state-of-the-art DL methods have additional constraints
included in the learning stages. These various constraints, however, lead to
additional computational complexity. We hence propose an efficient
Discriminative Convolutional Analysis Dictionary Learning (DCADL) method, as a
lower cost Discriminative DL framework, to both characterize the image
structures and refine the interclass structure representations. The proposed
DCADL jointly learns a convolutional analysis dictionary and a universal
classifier, while greatly reducing the time complexity in both training and
testing phases, and achieving a competitive accuracy, thus demonstrating great
performance in many experiments with standard databases.
Characterization of Posidonia Oceanica Seagrass Aerenchyma through Whole Slide Imaging: A Pilot Study
Authors: Olivier Debeir, Justine Allard, Christine Decaestecker, Jean-Pierre Hermand
Date: 2019-03-07 00:00:00
Tasks: Anatomy

Characterizing the tissue morphology and anatomy of seagrasses is essential
to predicting their acoustic behavior. In this pilot study, we use histology
techniques and whole slide imaging (WSI) to describe the composition and
topology of the aerenchyma of an entire leaf blade in an automatic way
combining the advantages of X-ray microtomography and optical microscopy.
Paraffin blocks are prepared in such a way that microtome slices contain an
arbitrarily large number of cross sections distributed along the full length of
a blade. The sample organization in the paraffin block coupled with whole slide
image analysis allows high throughput data extraction and an exhaustive
characterization along the whole blade length. The core of the work are image
processing algorithms that can identify cells and air lacunae (or void) from
fiber strand, epidermis, mesophyll and vascular system. A set of specific
features is developed to adequately describe the convexity of cells and voids
where standard descriptors fail. The features scrutinize the local curvature of
the object borders to allow an accurate discrimination between void and cell
through machine learning. The algorithm allows to reconstruct the cells and
cell membrane features that are relevant to tissue density, compressibility and
rigidity. Size distribution of the different cell types and gas spaces, total
biomass and total void volume fraction are then extracted from the high
resolution slices to provide a complete characterization of the tissue along
the leave from its base to the apex.
Deep learning observables in computational fluid dynamics
Authors: Kjetil O. Lye, Siddhartha Mishra, Deep Ray
Date: 2019-03-07 00:00:00
Tasks: Deep Learning, Efficient Neural Network, Uncertainty Quantification

Many large scale problems in computational fluid dynamics such as uncertainty quantification, Bayesian inversion, data assimilation and PDE constrained optimization are considered very challenging computationally as they require a large number of expensive (forward) numerical solutions of the corresponding PDEs. We propose a machine learning algorithm, based on deep artificial neural networks, that predicts the underlying \emph{input parameters to observable} map from a few training samples (computed realizations of this map). By a judicious combination of theoretical arguments and empirical observations, we find suitable network architectures and training hyperparameters that result in robust and efficient neural network approximations of the parameters to observable map. Numerical experiments are presented to demonstrate low prediction errors for the trained network networks, even when the network has been trained with a few samples, at a computational cost which is several orders of magnitude lower than the underlying PDE solver. Moreover, we combine the proposed deep learning algorithm with Monte Carlo (MC) and Quasi-Monte Carlo (QMC) methods to efficiently compute uncertainty propagation for nonlinear PDEs. Under the assumption that the underlying neural networks generalize well, we prove that the deep learning MC and QMC algorithms are guaranteed to be faster than the baseline (quasi-) Monte Carlo methods. Numerical experiments demonstrating one to two orders of magnitude speed up over baseline QMC and MC algorithms, for the intricate problem of computing probability distributions of the observable, are also presented.
Option Comparison Network for Multiple-choice Reading Comprehension
Authors: Qiu Ran, Peng Li, Weiwei Hu, Jie zhou
Date: 2019-03-07 00:00:00
Tasks: Multiple-choice, Question Answering, Reading Comprehension

Multiple-choice reading comprehension (MCRC) is the task of selecting the
correct answer from multiple options given a question and an article. Existing
MCRC models typically either read each option independently or compute a
fixed-length representation for each option before comparing them. However,
humans typically compare the options at multiple-granularity level before
reading the article in detail to make reasoning more efficient. Mimicking
humans, we propose an option comparison network (OCN) for MCRC which compares
options at word-level to better identify their correlations to help reasoning.
Specially, each option is encoded into a vector sequence using a skimmer to
retain fine-grained information as much as possible. An attention mechanism is
leveraged to compare these sequences vector-by-vector to identify more subtle
correlations between options, which is potentially valuable for reasoning.
Experimental results on the human English exam MCRC dataset RACE show that our
model outperforms existing methods significantly. Moreover, it is also the
first model that surpasses Amazon Mechanical Turker performance on the whole
dataset.
Attack Type Agnostic Perceptual Enhancement of Adversarial Images
Authors: Bilgin Aksoy, Alptekin Temizel
Date: 2019-03-07 00:00:00
Tasks: Adversarial Attack, Image Generation, Vocal Bursts Type Prediction

Adversarial images are samples that are intentionally modified to deceive machine learning systems. They are widely used in applications such as CAPTHAs to help distinguish legitimate human users from bots. However, the noise introduced during the adversarial image generation process degrades the perceptual quality and introduces artificial colours; making it also difficult for humans to classify images and recognise objects. In this letter, we propose a method to enhance the perceptual quality of these adversarial images. The proposed method is attack type agnostic and could be used in association with the existing attacks in the literature. Our experiments show that the generated adversarial images have lower Euclidean distance values while maintaining the same adversarial attack performance. Distances are reduced by 5.88% to 41.27% with an average reduction of 22% over the different attack and network types.
Provably Robust Blackbox Optimization for Reinforcement Learning
Authors: Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Deepali Jain, Yuxiang Yang, Atil Iscen, Jasmine Hsu, Vikas Sindhwani
Date: 2019-03-07 00:00:00
Tasks: MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Text-to-Image Generation

Interest in derivative-free optimization (DFO) and "evolutionary strategies" (ES) has recently surged in the Reinforcement Learning (RL) community, with growing evidence that they can match state of the art methods for policy optimization problems in Robotics. However, it is well known that DFO methods suffer from prohibitively high sampling complexity. They can also be very sensitive to noisy rewards and stochastic dynamics. In this paper, we propose a new class of algorithms, called Robust Blackbox Optimization (RBO). Remarkably, even if up to $23\%$ of all the measurements are arbitrarily corrupted, RBO can provably recover gradients to high accuracy. RBO relies on learning gradient flows using robust regression methods to enable off-policy updates. On several MuJoCo robot control tasks, when all other RL approaches collapse in the presence of adversarial noise, RBO is able to train policies effectively. We also show that RBO can be applied to legged locomotion tasks including path tracking for quadruped robots.
Accurate reconstruction of EBSD datasets by a multimodal data approach using an evolutionary algorithm
Authors: Marie-Agathe Charpagne, Florian Strub, Tresa M. Pollock
Date: 2019-03-07 00:00:00

A new method has been developed for the correction of the distortions and/or
enhanced phase differentiation in Electron Backscatter Diffraction (EBSD) data.
Using a multi-modal data approach, the method uses segmented images of the
phase of interest (laths, precipitates, voids, inclusions) on images gathered
by backscattered or secondary electrons of the same area as the EBSD map. The
proposed approach then search for the best transformation to correct their
relative distortions and recombines the data in a new EBSD file. Speckles of
the features of interest are first segmented in both the EBSD and image data
modes. The speckle extracted from the EBSD data is then meshed, and the
Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is implemented to
distort the mesh until the speckles superimpose. The quality of the matching is
quantified via a score that is linked to the number of overlapping pixels in
the speckles. The locations of the points of the distorted mesh are compared to
those of the initial positions to create pairs of matching points that are used
to calculate the polynomial function that describes the distortion the best.
This function is then applied to un-distort the EBSD data, and the phase
information is inferred using the data of the segmented speckle. Fast and
versatile, this method does not require any human annotation and can be applied
to large datasets and wide areas. Besides, this method requires very few
assumptions concerning the shape of the distortion function. It can be used for
the single compensation of the distortions or combined with the phase
differentiation. The accuracy of this method is of the order of the pixel size.
Some application examples in multiphase materials with feature sizes down to 1
$\mu$m are presented, including Ti-6Al-4V Titanium alloy, Rene 65 and additive
manufactured Inconel 718 Nickel-base superalloys.
The Variational Predictive Natural Gradient
Authors: Da Tang, Rajesh Ranganath
Date: 2019-03-07 00:00:00
Tasks: General Classification, Variational Inference

Variational inference transforms posterior inference into parametric optimization thereby enabling the use of latent variable models where otherwise impractical. However, variational inference can be finicky when different variational parameters control variables that are strongly correlated under the model. Traditional natural gradients based on the variational approximation fail to correct for correlations when the approximation is not the true posterior. To address this, we construct a new natural gradient called the Variational Predictive Natural Gradient (VPNG). Unlike traditional natural gradients for variational inference, this natural gradient accounts for the relationship between model parameters and variational parameters. We demonstrate the insight with a simple example as well as the empirical value on a classification task, a deep generative model of images, and probabilistic matrix factorization for recommendation.
Correction of Electron Back-scattered Diffraction datasets using an evolutionary algorithm
Authors: Florian Strub, Marie-Agathe Charpagne, Tresa M. Pollock
Date: 2019-03-07 00:00:00

In materials science and particularly electron microscopy, Electron
Back-scatter Diffraction (EBSD) is a common and powerful mapping technique for
collecting local crystallographic data at the sub-micron scale. The quality of
the reconstruction of the maps is critical to study the spatial distribution of
phases and crystallographic orientation relationships between phases, a key
interest in materials science. However, EBSD data is known to suffer from
distortions that arise from several instrument and detector artifacts. In this
paper, we present an unsupervised method that corrects those distortions, and
enables or enhances phase differentiation in EBSD data. The method uses a
segmented electron image of the phases of interest (laths, precipitates, voids,
inclusions) gathered using detectors that generate less distorted data, of the
same area than the EBSD map, and then searches for the best transformation to
correct the distortions of the initial EBSD data. To do so, the Covariance
Matrix Adaptation Evolution Strategy (CMA-ES) is implemented to distort the
EBSD until it matches the reference electron image. Fast and versatile, this
method does not require any human annotation and can be applied to large
datasets and wide areas, where the distortions are important. Besides, this
method requires very little assumption concerning the shape of the distortion
function. Some application examples in multiphase materials with feature sizes
down to 1 $\mu$m are presented, including a Titanium alloy and a Nickel-base
superalloy.
Integrating Artificial and Human Intelligence for Efficient Translation
Authors: Nico Herbig, Santanu Pal, Josef van Genabith, Antonio Krüger
Date: 2019-03-07 00:00:00
Tasks: Machine Translation, Translation

Current advances in machine translation increase the need for translators to
switch from traditional translation to post-editing of machine-translated text,
a process that saves time and improves quality. Human and artificial
intelligence need to be integrated in an efficient way to leverage the
advantages of both for the translation task. This paper outlines approaches at
this boundary of AI and HCI and discusses open research questions to further
advance the field.
Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention
Authors: Richard Droste, Yifan Cai, Harshita Sharma, Pierre Chatelain, Lior Drukker, Aris T. Papageorghiou, J. Alison Noble
Date: 2019-03-07 00:00:00
Tasks: regression, Representation Learning, Saliency Prediction, Transfer Learning

Image representations are commonly learned from class labels, which are a simplistic approximation of human image understanding. In this paper we demonstrate that transferable representations of images can be learned without manual annotations by modeling human visual attention. The basis of our analyses is a unique gaze tracking dataset of sonographers performing routine clinical fetal anomaly screenings. Models of sonographer visual attention are learned by training a convolutional neural network (CNN) to predict gaze on ultrasound video frames through visual saliency prediction or gaze-point regression. We evaluate the transferability of the learned representations to the task of ultrasound standard plane detection in two contexts. Firstly, we perform transfer learning by fine-tuning the CNN with a limited number of labeled standard plane images. We find that fine-tuning the saliency predictor is superior to training from random initialization, with an average F1-score improvement of 9.6% overall and 15.3% for the cardiac planes. Secondly, we train a simple softmax regression on the feature activations of each CNN layer in order to evaluate the representations independently of transfer learning hyper-parameters. We find that the attention models derive strong representations, approaching the precision of a fully-supervised baseline model for all but the last layer.
Detection of Advanced Malware by Machine Learning Techniques
Authors: Sanjay Sharma, C. Rama Krishna, Sanjay K. Sahay
Date: 2019-03-07 00:00:00
Tasks: BIG-bench Machine Learning, feature selection, General Classification, Malware Classification

In today's digital world most of the anti-malware tools are signature based
which is ineffective to detect advanced unknown malware viz. metamorphic
malware. In this paper, we study the frequency of opcode occurrence to detect
unknown malware by using machine learning technique. For the purpose, we have
used kaggle Microsoft malware classification challenge dataset. The top 20
features obtained from fisher score, information gain, gain ratio, chi-square
and symmetric uncertainty feature selection methods are compared. We also
studied multiple classifier available in WEKA GUI based machine learning tool
and found that five of them (Random Forest, LMT, NBT, J48 Graft and REPTree)
detect malware with almost 100% accuracy.
Reparameterizing Distributions on Lie Groups
Authors: Luca Falorsi, Pim de Haan, Tim R. Davidson, Patrick Forré
Date: 2019-03-07 00:00:00
Tasks: Pose Estimation

Reparameterizable densities are an important way to learn probability
distributions in a deep learning setting. For many distributions it is possible
to create low-variance gradient estimators by utilizing a `reparameterization
trick'. Due to the absence of a general reparameterization trick, much research
has recently been devoted to extend the number of reparameterizable
distributional families. Unfortunately, this research has primarily focused on
distributions defined in Euclidean space, ruling out the usage of one of the
most influential class of spaces with non-trivial topologies: Lie groups. In
this work we define a general framework to create reparameterizable densities
on arbitrary Lie groups, and provide a detailed practitioners guide to further
the ease of usage. We demonstrate how to create complex and multimodal
distributions on the well known oriented group of 3D rotations,
$\operatorname{SO}(3)$, using normalizing flows. Our experiments on applying
such distributions in a Bayesian setting for pose estimation on objects with
discrete and continuous symmetries, showcase their necessity in achieving
realistic uncertainty estimates.
ViTOR: Learning to Rank Webpages Based on Visual Features
Authors: Bram van den Akker, Ilya Markov, Maarten de Rijke
Date: 2019-03-07 00:00:00
Tasks: General Classification, image-classification, Image Classification, Learning-To-Rank, Transfer Learning

The visual appearance of a webpage carries valuable information about its
quality and can be used to improve the performance of learning to rank (LTR).
We introduce the Visual learning TO Rank (ViTOR) model that integrates
state-of-the-art visual features extraction methods by (i) transfer learning
from a pre-trained image classification model, and (ii) synthetic saliency heat
maps generated from webpage snapshots. Since there is currently no public
dataset for the task of LTR with visual features, we also introduce and release
the ViTOR dataset, containing visually rich and diverse webpages. The ViTOR
dataset consists of visual snapshots, non-visual features and relevance
judgments for ClueWeb12 webpages and TREC Web Track queries. We experiment with
the proposed ViTOR model on the ViTOR dataset and show that it significantly
improves the performance of LTR with visual features
TEST: A Terminology Extraction System for Technology Related Terms
Authors: Murhaf Hossari, Soumyabrata Dev, John D. Kelleher
Date: 2018-12-22 00:00:00
Tasks: Sentence, Sentence Classification

Tracking developments in the highly dynamic data-technology landscape are
vital to keeping up with novel technologies and tools, in the various areas of
Artificial Intelligence (AI). However, It is difficult to keep track of all the
relevant technology keywords. In this paper, we propose a novel system that
addresses this problem. This tool is used to automatically detect the existence
of new technologies and tools in text, and extract terms used to describe these
new technologies. The extracted new terms can be logged as new AI technologies
as they are found on-the-fly in the web. It can be subsequently classified into
the relevant semantic labels and AI domains. Our proposed tool is based on a
two-stage cascading model -- the first stage classifies if the sentence
contains a technology term or not; and the second stage identifies the
technology keyword in the sentence. We obtain a competitive accuracy for both
tasks of sentence classification and text identification.
Discovering seminal works with marker papers
Authors: Robin Haunschild, Werner Marx
Date: 2019-01-22 00:00:00
Tasks: Information Retrieval, Retrieval

Bibliometric information retrieval in databases can employ different strategies. Com-monly, queries are performed by searching in title, abstract and/or author keywords (author vocabulary). More advanced queries employ database keywords to search in a controlled vo-cabulary. Queries based on search terms can be augmented with their citing papers if a re-search field cannot be curtailed by the search query alone. Here, we present another strategy to discover the most important papers of a research field. A marker paper is used to reveal the most important works for the relevant community. All papers co-cited with the marker paper are analyzed using reference publication year spectroscopy (RPYS). For demonstration of the marker paper approach, density functional theory (DFT) is used as a research field. Compari-sons between a prior RPYS on a publication set compiled using a keyword-based search in a controlled vocabulary and three different co-citation RPYS (RPYS-CO) analyses show very similar results. Similarities and differences are discussed.
Neural Language Modeling with Visual Features
Authors: Antonios Anastasopoulos, Shankar Kumar, Hank Liao
Date: 2019-03-07 00:00:00
Tasks: Language Modeling, Language Modelling

Multimodal language models attempt to incorporate non-linguistic features for
the language modeling task. In this work, we extend a standard recurrent neural
network (RNN) language model with features derived from videos. We train our
models on data that is two orders-of-magnitude bigger than datasets used in
prior work. We perform a thorough exploration of model architectures for
combining visual and text features. Our experiments on two corpora (YouCookII
and 20bn-something-something-v2) show that the best performing architecture
consists of middle fusion of visual and text features, yielding over 25%
relative improvement in perplexity. We report analysis that provides insights
into why our multimodal language model improves upon a standard RNN language
model.
Adversarial Out-domain Examples for Generative Models
Authors: Dario Pasquini, Marco Mingione, Massimo Bernaschi
Date: 2019-03-07 00:00:00
Tasks: Adversarial Attack, Image Generation

Deep generative models are rapidly becoming a common tool for researchers and developers. However, as exhaustively shown for the family of discriminative models, the test-time inference of deep neural networks cannot be fully controlled and erroneous behaviors can be induced by an attacker. In the present work, we show how a malicious user can force a pre-trained generator to reproduce arbitrary data instances by feeding it suitable adversarial inputs. Moreover, we show that these adversarial latent vectors can be shaped so as to be statistically indistinguishable from the set of genuine inputs. The proposed attack technique is evaluated with respect to various GAN images generators using different architectures, training processes and for both conditional and not-conditional setups.