pwc-archive/papers-with-abstracts huggingface dataset:
Wolf in Sheep's Clothing - The Downscaling Attack Against Deep Learning Applications
Authors: Qixue Xiao, Kang Li, Deyue Zhang, Yier Jin
Date: 2017-12-21 00:00:00
Tasks: Data Poisoning, Deep Learning, image-classification, Image Classification

This paper considers security risks buried in the data processing pipeline in
common deep learning applications. Deep learning models usually assume a fixed
scale for their training and input data. To allow deep learning applications to
handle a wide range of input data, popular frameworks, such as Caffe,
TensorFlow, and Torch, all provide data scaling functions to resize input to
the dimensions used by deep learning models. Image scaling algorithms are
intended to preserve the visual features of an image after scaling. However,
common image scaling algorithms are not designed to handle human crafted
images. Attackers can make the scaling outputs look dramatically different from
the corresponding input images.
  This paper presents a downscaling attack that targets the data scaling
process in deep learning applications. By carefully crafting input data that
mismatches with the dimension used by deep learning models, attackers can
create deceiving effects. A deep learning application effectively consumes data
that are not the same as those presented to users. The visual inconsistency
enables practical evasion and data poisoning attacks to deep learning
applications. This paper presents proof-of-concept attack samples to popular
deep-learning-based image classification applications. To address the
downscaling attacks, the paper also suggests multiple potential mitigation
strategies.
Model-Based Clustering of Nonparametric Weighted Networks with Application to Water Pollution Analysis
Authors: Amal Agarwal, Lingzhou Xue
Date: 2017-12-21 00:00:00
Tasks: Clustering, Diversity

Water pollution is a major global environmental problem, and it poses a great environmental risk to public health and biological diversity. This work is motivated by assessing the potential environmental threat of coal mining through increased sulfate concentrations in river networks, which do not belong to any simple parametric distribution. However, existing network models mainly focus on binary or discrete networks and weighted networks with known parametric weight distributions. We propose a principled nonparametric weighted network model based on exponential-family random graph models and local likelihood estimation and study its model-based clustering with application to large-scale water pollution network analysis. We do not require any parametric distribution assumption on network weights. The proposed method greatly extends the methodology and applicability of statistical network models. Furthermore, it is scalable to large and complex networks in large-scale environmental studies. The power of our proposed methods is demonstrated in simulation studies and a real application to sulfate pollution network analysis in Ohio watershed located in Pennsylvania, United States.
Towards a Deep Improviser: a prototype deep learning post-tonal free music generator
Authors: Roger T. Dean, Jamie Forth
Date: 2017-12-21 00:00:00

Two modest-sized symbolic corpora of post-tonal and post-metric keyboard
music have been constructed, one algorithmic, the other improvised. Deep
learning models of each have been trained and largely optimised. Our purpose is
to obtain a model with sufficient generalisation capacity that in response to a
small quantity of separate fresh input seed material, it can generate outputs
that are distinctive, rather than recreative of the learned corpora or the seed
material. This objective has been first assessed statistically, and as judged
by k-sample Anderson-Darling and Cramer tests, has been achieved. Music has
been generated using the approach, and informal judgements place it roughly on
a par with algorithmic and composed music in related forms. Future work will
aim to enhance the model such that it can be evaluated in relation to
expression, meaning and utility in real-time performance.
Deep learning for predicting refractive error from retinal fundus images
Authors: Avinash V. Varadarajan, Ryan Poplin, Katy Blumer, Christof Angermueller, Joe Ledsam, Reena Chopra, Pearse A. Keane, Greg S. Corrado, Lily Peng, Dale R. Webster
Date: 2017-12-21 00:00:00
Tasks: Deep Learning

Refractive error, one of the leading cause of visual impairment, can be
corrected by simple interventions like prescribing eyeglasses. We trained a
deep learning algorithm to predict refractive error from the fundus photographs
from participants in the UK Biobank cohort, which were 45 degree field of view
images and the AREDS clinical trial, which contained 30 degree field of view
images. Our model use the "attention" method to identify features that are
correlated with refractive error. Mean absolute error (MAE) of the algorithm's
prediction compared to the refractive error obtained in the AREDS and UK
Biobank. The resulting algorithm had a MAE of 0.56 diopters (95% CI: 0.55-0.56)
for estimating spherical equivalent on the UK Biobank dataset and 0.91 diopters
(95% CI: 0.89-0.92) for the AREDS dataset. The baseline expected MAE (obtained
by simply predicting the mean of this population) was 1.81 diopters (95% CI:
1.79-1.84) for UK Biobank and 1.63 (95% CI: 1.60-1.67) for AREDS. Attention
maps suggested that the foveal region was one of the most important areas used
by the algorithm to make this prediction, though other regions also contribute
to the prediction. The ability to estimate refractive error with high accuracy
from retinal fundus photos has not been previously known and demonstrates that
deep learning can be applied to make novel predictions from medical images.
Given that several groups have recently shown that it is feasible to obtain
retinal fundus photos using mobile phones and inexpensive attachments, this
work may be particularly relevant in regions of the world where autorefractors
may not be readily available.
The Character Thinks Ahead: creative writing with deep learning nets and its stylistic assessment
Authors: Roger T. Dean, Hazel Smith
Date: 2017-12-21 00:00:00

We discuss how to control outputs from deep learning models of text corpora
so as to create contemporary poetic works. We assess whether these controls are
successful in the immediate sense of creating stylo- metric distinctiveness.
The specific context is our piece The Character Thinks Ahead (2016/17); the
potential applications are broad.
Adversarial Structured Prediction for Multivariate Measures
Authors: Hong Wang, Ashkan Rezaei, Brian D. Ziebart
Date: 2017-12-20 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Prediction, Structured Prediction, Word Alignment

Many predicted structured objects (e.g., sequences, matchings, trees) are
evaluated using the F-score, alignment error rate (AER), or other multivariate
performance measures. Since inductively optimizing these measures using
training data is typically computationally difficult, empirical risk
minimization of surrogate losses is employed, using, e.g., the hinge loss for
(structured) support vector machines. These approximations often introduce a
mismatch between the learner's objective and the desired application
performance, leading to inconsistency. We take a different approach:
adversarially approximate training data while optimizing the exact F-score or
AER. Structured predictions under this formulation result from solving zero-sum
games between a predictor seeking the best performance and an adversary seeking
the worst while required to (approximately) match certain structured properties
of the training data. We explore this approach for word alignment (AER
evaluation) and named entity recognition (F-score evaluation) with linear-chain
constraints.
Efficient B-mode Ultrasound Image Reconstruction from Sub-sampled RF Data using Deep Learning
Authors: Yeo Hun Yoon, Shujaat Khan, Jaeyoung Huh, Jong Chul Ye
Date: 2017-12-17 00:00:00
Tasks: compressed sensing, Diagnostic, Image Reconstruction

In portable, three dimensional, and ultra-fast ultrasound imaging systems,
there is an increasing demand for the reconstruction of high quality images
from a limited number of radio-frequency (RF) measurements due to receiver (Rx)
or transmit (Xmit) event sub-sampling. However, due to the presence of side
lobe artifacts from RF sub-sampling, the standard beamformer often produces
blurry images with less contrast, which are unsuitable for diagnostic purposes.
Existing compressed sensing approaches often require either hardware changes or
computationally expensive algorithms, but their quality improvements are
limited. To address this problem, here we propose a novel deep learning
approach that directly interpolates the missing RF data by utilizing redundancy
in the Rx-Xmit plane. Our extensive experimental results using sub-sampled RF
data from a multi-line acquisition B-mode system confirm that the proposed
method can effectively reduce the data rate without sacrificing image quality.
Digital image splicing detection based on Markov features in QDCT and QWT domain
Authors: Ruxin Wang, Wei Lu, Shijun Xiang, Xianfeng Zhao, Jinwei Wang
Date: 2017-08-28 00:00:00
Tasks: Position

Image splicing detection is of fundamental importance in digital forensics
and therefore has attracted increasing attention recently. In this paper, a
color image splicing detection approach is proposed based on Markov transition
probability of quaternion component separation in quaternion discrete cosine
transform (QDCT) domain and quaternion wavelet transform (QWT) domain. Firstly,
Markov features of the intra-block and inter-block between block QDCT
coefficients are obtained from the real part and three imaginary parts of QDCT
coefficients respectively. Then, additional Markov features are extracted from
luminance (Y) channel in quaternion wavelet transform domain to characterize
the dependency of position among quaternion wavelet subband coefficients.
Finally, ensemble classifier (EC) is exploited to classify the spliced and
authentic color images. The experiment results demonstrate that the proposed
approach can outperforms some state-of-the-art methods.
Context-Aware Semantic Inpainting
Authors: Haofeng Li, Guanbin Li, Liang Lin, Yizhou Yu
Date: 2017-12-21 00:00:00
Tasks: Generative Adversarial Network, Image Inpainting

Recently image inpainting has witnessed rapid progress due to generative
adversarial networks (GAN) that are able to synthesize realistic contents.
However, most existing GAN-based methods for semantic inpainting apply an
auto-encoder architecture with a fully connected layer, which cannot accurately
maintain spatial information. In addition, the discriminator in existing GANs
struggle to understand high-level semantics within the image context and yield
semantically consistent content. Existing evaluation criteria are biased
towards blurry results and cannot well characterize edge preservation and
visual authenticity in the inpainting results. In this paper, we propose an
improved generative adversarial network to overcome the aforementioned
limitations. Our proposed GAN-based framework consists of a fully convolutional
design for the generator which helps to better preserve spatial structures and
a joint loss function with a revised perceptual loss to capture high-level
semantics in the context. Furthermore, we also introduce two novel measures to
better assess the quality of image inpainting results. Experimental results
demonstrate that our method outperforms the state of the art under a wide range
of criteria.
Bit-Vector Model Counting using Statistical Estimation
Authors: Seonmo Kim, Stephen McCamant
Date: 2017-12-21 00:00:00
Tasks: model

Approximate model counting for bit-vector SMT formulas (generalizing \#SAT)
has many applications such as probabilistic inference and quantitative
information-flow security, but it is computationally difficult. Adding random
parity constraints (XOR streamlining) and then checking satisfiability is an
effective approximation technique, but it requires a prior hypothesis about the
model count to produce useful results. We propose an approach inspired by
statistical estimation to continually refine a probabilistic estimate of the
model count for a formula, so that each XOR-streamlined query yields as much
information as possible. We implement this approach, with an approximate
probability model, as a wrapper around an off-the-shelf SMT solver or SAT
solver. Experimental results show that the implementation is faster than the
most similar previous approaches which used simpler refinement strategies. The
technique also lets us model count formulas over floating-point constraints,
which we demonstrate with an application to a vulnerability in differential
privacy mechanisms.
Automatic Estimation of Ice Bottom Surfaces from Radar Imagery
Authors: Mingze Xu, David J. Crandall, Geoffrey C. Fox, John D Paden
Date: 2017-12-21 00:00:00

Ground-penetrating radar on planes and satellites now makes it practical to
collect 3D observations of the subsurface structure of the polar ice sheets,
providing crucial data for understanding and tracking global climate change.
But converting these noisy readings into useful observations is generally done
by hand, which is impractical at a continental scale. In this paper, we propose
a computer vision-based technique for extracting 3D ice-bottom surfaces by
viewing the task as an inference problem on a probabilistic graphical model. We
first generate a seed surface subject to a set of constraints, and then
incorporate additional sources of evidence to refine it via discrete energy
minimization. We evaluate the performance of the tracking algorithm on 7
topographic sequences (each with over 3000 radar images) collected from the
Canadian Arctic Archipelago with respect to human-labeled ground truth.
Combining Static and Dynamic Features for Multivariate Sequence Classification
Authors: Anna Leontjeva, Ilya Kuzovkin
Date: 2017-12-20 00:00:00
Tasks: Classification, General Classification

Model precision in a classification task is highly dependent on the feature
space that is used to train the model. Moreover, whether the features are
sequential or static will dictate which classification method can be applied as
most of the machine learning algorithms are designed to deal with either one or
another type of data. In real-life scenarios, however, it is often the case
that both static and dynamic features are present, or can be extracted from the
data. In this work, we demonstrate how generative models such as Hidden Markov
Models (HMM) and Long Short-Term Memory (LSTM) artificial neural networks can
be used to extract temporal information from the dynamic data. We explore how
the extracted information can be combined with the static features in order to
improve the classification performance. We evaluate the existing techniques and
suggest a hybrid approach, which outperforms other methods on several public
datasets.
Contemporary machine learning: a guide for practitioners in the physical sciences
Authors: Brian K. Spears
Date: 2017-12-20 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

Machine learning is finding increasingly broad application in the physical
sciences. This most often involves building a model relationship between a
dependent, measurable output and an associated set of controllable, but
complicated, independent inputs. We present a tutorial on current techniques in
machine learning -- a jumping-off point for interested researchers to advance
their work. We focus on deep neural networks with an emphasis on demystifying
deep learning. We begin with background ideas in machine learning and some
example applications from current research in plasma physics. We discuss
supervised learning techniques for modeling complicated functions, beginning
with familiar regression schemes, then advancing to more sophisticated deep
learning methods. We also address unsupervised learning and techniques for
reducing the dimensionality of input spaces. Along the way, we describe methods
for practitioners to help ensure that their models generalize from their
training data to as-yet-unseen test data. We describe classes of tasks --
predicting scalars, handling images, fitting time-series -- and prepare the
reader to choose an appropriate technique. We finally point out some
limitations to modern machine learning and speculate on some ways that
practitioners from the physical sciences may be particularly suited to help.
Context-aware Path Ranking for Knowledge Base Completion
Authors: Sahisnu Mazumder, Bing Liu
Date: 2017-12-20 00:00:00
Tasks: Knowledge Base Completion

Knowledge base (KB) completion aims to infer missing facts from existing ones
in a KB. Among various approaches, path ranking (PR) algorithms have received
increasing attention in recent years. PR algorithms enumerate paths between
entity pairs in a KB and use those paths as features to train a model for
missing fact prediction. Due to their good performances and high model
interpretability, several methods have been proposed. However, most existing
methods suffer from scalability (high RAM consumption) and feature explosion
(trains on an exponentially large number of features) problems. This paper
proposes a Context-aware Path Ranking (C-PR) algorithm to solve these problems
by introducing a selective path exploration strategy. C-PR learns global
semantics of entities in the KB using word embedding and leverages the
knowledge of entity semantics to enumerate contextually relevant paths using
bidirectional random walk. Experimental results on three large KBs show that
the path features (fewer in number) discovered by C-PR not only improve
predictive performance but also are more interpretable than existing baselines.
Morphology dictates a robot's ability to ground crowd-proposed language
Authors: Zahra Mahoor, Jack Felag, Josh Bongard
Date: 2017-12-16 00:00:00

As more robots act in physical proximity to people, it is essential to ensure
they make decisions and execute actions that align with human values. To do so,
robots need to understand the true intentions behind human-issued commands. In
this paper, we define a safe robot as one that receives a natural-language
command from humans, considers an action in response to that command, and
accurately predicts how humans will judge that action if is executed in
reality. Our contribution is two-fold: First, we introduce a web platform for
human users to propose commands to simulated robots. The robots receive
commands and act based on those proposed commands, and then the users provide
positive and/or negative reinforcement. Next, we train a critic for each robot
to predict the crowd's responses to one of the crowd-proposed commands. Second,
we show that the morphology of a robot plays a role in the way it grounds
language: The critics show that two of the robots used in the experiment
achieve a lower prediction error than the others. Thus, those two robots are
safer, according to our definition, since they ground the proposed command more
accurately.
Enhance Visual Recognition under Adverse Conditions via Deep Networks
Authors: Ding Liu, Bowen Cheng, Zhangyang Wang, Haichao Zhang, Thomas S. Huang
Date: 2017-12-20 00:00:00
Tasks: Data Augmentation, Image Restoration, Transfer Learning, Unsupervised Pre-training, Video Recognition

Visual recognition under adverse conditions is a very important and
challenging problem of high practical value, due to the ubiquitous existence of
quality distortions during image acquisition, transmission, or storage. While
deep neural networks have been extensively exploited in the techniques of
low-quality image restoration and high-quality image recognition tasks
respectively, few studies have been done on the important problem of
recognition from very low-quality images. This paper proposes a deep learning
based framework for improving the performance of image and video recognition
models under adverse conditions, using robust adverse pre-training or its
aggressive variant. The robust adverse pre-training algorithms leverage the
power of pre-training and generalizes conventional unsupervised pre-training
and data augmentation methods. We further develop a transfer learning approach
to cope with real-world datasets of unknown adverse conditions. The proposed
framework is comprehensively evaluated on a number of image and video
recognition benchmarks, and obtains significant performance improvements under
various single or mixed adverse conditions. Our visualization and analysis
further add to the explainability of results.
Support vector comparison machines
Authors: David Venuto, Toby Dylan Hocking, Lakjaree Sphanurattana, Masashi Sugiyama
Date: 2014-01-30 00:00:00
Tasks: Learning-To-Rank

In ranking problems, the goal is to learn a ranking function from labeled pairs of input points. In this paper, we consider the related comparison problem, where the label indicates which element of the pair is better, or if there is no significant difference. We cast the learning problem as a margin maximization, and show that it can be solved by converting it to a standard SVM. We use simulated nonlinear patterns, a real learning to rank sushi data set, and a chess data set to show that our proposed SVMcompare algorithm outperforms SVMrank when there are equality pairs.
Unsupervised learning of dynamical and molecular similarity using variance minimization
Authors: Brooke E. Husic, Vijay S. Pande
Date: 2017-12-20 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

In this report, we present an unsupervised machine learning method for
determining groups of molecular systems according to similarity in their
dynamics or structures using Ward's minimum variance objective function. We
first apply the minimum variance clustering to a set of simulated tripeptides
using the information theoretic Jensen-Shannon divergence between Markovian
transition matrices in order to gain insight into how point mutations affect
protein dynamics. Then, we extend the method to partition two chemoinformatic
datasets according to structural similarity to motivate a train/validation/test
split for supervised learning that avoids overfitting.
Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth
Authors: Yuankai Huo, Zhoubing Xu, Shunxing Bao, Albert Assad, Richard G. Abramson, Bennett A. Landman
Date: 2017-12-20 00:00:00
Tasks: Image Generation, Image-to-Image Translation, Medical Image Segmentation, Segmentation, Splenomegaly Segmentation On Multi-Modal Mri

A lack of generalizability is one key limitation of deep learning based
segmentation. Typically, one manually labels new training images when
segmenting organs in different imaging modalities or segmenting abnormal organs
from distinct disease cohorts. The manual efforts can be alleviated if one is
able to reuse manual labels from one modality (e.g., MRI) to train a
segmentation network for a new modality (e.g., CT). Previously, two stage
methods have been proposed to use cycle generative adversarial networks
(CycleGAN) to synthesize training images for a target modality. Then, these
efforts trained a segmentation network independently using synthetic images.
However, these two independent stages did not use the complementary information
between synthesis and segmentation. Herein, we proposed a novel end-to-end
synthesis and segmentation network (EssNet) to achieve the unpaired MRI to CT
image synthesis and CT splenomegaly segmentation simultaneously without using
manual labels on CT. The end-to-end EssNet achieved significantly higher median
Dice similarity coefficient (0.9188) than the two stages strategy (0.8801), and
even higher than canonical multi-atlas segmentation (0.9125) and ResNet method
(0.9107), which used the CT manual labels.