pwc-archive/papers-with-abstracts huggingface dataset:
Deepwound: Automated Postoperative Wound Assessment and Surgical Site Surveillance through Convolutional Neural Networks
Authors: Varun Shenoy, Elizabeth Foster, Lauren Aalami, Bakar Majeed, Oliver Aalami
Date: 2018-07-11 00:00:00
Tasks: Specificity

Postoperative wound complications are a significant cause of expense for
hospitals, doctors, and patients. Hence, an effective method to diagnose the
onset of wound complications is strongly desired. Algorithmically classifying
wound images is a difficult task due to the variability in the appearance of
wound sites. Convolutional neural networks (CNNs), a subgroup of artificial
neural networks that have shown great promise in analyzing visual imagery, can
be leveraged to categorize surgical wounds. We present a multi-label CNN
ensemble, Deepwound, trained to classify wound images using only image pixels
and corresponding labels as inputs. Our final computational model can
accurately identify the presence of nine labels: drainage, fibrinous exudate,
granulation tissue, surgical site infection, open wound, staples, steri strips,
and sutures. Our model achieves receiver operating curve (ROC) area under curve
(AUC) scores, sensitivity, specificity, and F1 scores superior to prior work in
this area. Smartphones provide a means to deliver accessible wound care due to
their increasing ubiquity. Paired with deep neural networks, they offer the
capability to provide clinical insight to assist surgeons during postoperative
care. We also present a mobile application frontend to Deepwound that assists
patients in tracking their wound and surgical recovery from the comfort of
their home.
A Reflectance Based Method For Shadow Detection and Removal
Authors: Sri Kalyan Yarlagadda, Fengqing Zhu
Date: 2018-07-11 00:00:00
Tasks: Detecting Shadows, Scene Understanding, Shadow Detection, Shadow Detection And Removal

Shadows are common aspect of images and when left undetected can hinder scene
understanding and visual processing. We propose a simple yet effective approach
based on reflectance to detect shadows from single image. An image is first
segmented and based on the reflectance, illumination and texture
characteristics, segments pairs are identified as shadow and non-shadow pairs.
The proposed method is tested on two publicly available and widely used
datasets. Our method achieves higher accuracy in detecting shadows compared to
previous reported methods despite requiring fewer parameters. We also show
results of shadow-free images by relighting the pixels in the detected shadow
regions.
A Generic Approach to Lung Field Segmentation from Chest Radiographs using Deep Space and Shape Learning
Authors: Awais Mansoor, Juan J. Cerrolaza, Geovanny Perez, Elijah Biggs, Kazunori Okada, Gustavo Nino, Marius George Linguraru
Date: 2018-07-11 00:00:00
Tasks: Capacity Estimation, Object Localization, parameter estimation, Representation Learning, Segmentation

Computer-aided diagnosis (CAD) techniques for lung field segmentation from
chest radiographs (CXR) have been proposed for adult cohorts, but rarely for
pediatric subjects. Statistical shape models (SSMs), the workhorse of most
state-of-the-art CXR-based lung field segmentation methods, do not efficiently
accommodate shape variation of the lung field during the pediatric
developmental stages. The main contributions of our work are: (1) a generic
lung field segmentation framework from CXR accommodating large shape variation
for adult and pediatric cohorts; (2) a deep representation learning detection
mechanism, \emph{ensemble space learning}, for robust object localization; and
(3) \emph{marginal shape deep learning} for the shape deformation parameter
estimation. Unlike the iterative approach of conventional SSMs, the proposed
shape learning mechanism transforms the parameter space into marginal subspaces
that are solvable efficiently using the recursive representation learning
mechanism. Furthermore, our method is the first to include the challenging
retro-cardiac region in the CXR-based lung segmentation for accurate lung
capacity estimation. The framework is evaluated on 668 CXRs of patients between
3 month to 89 year of age. We obtain a mean Dice similarity coefficient of
$0.96\pm0.03$ (including the retro-cardiac region). For a given accuracy, the
proposed approach is also found to be faster than conventional SSM-based
iterative segmentation methods. The computational simplicity of the proposed
generic framework could be similarly applied to the fast segmentation of other
deformable objects.
Automated Vulnerability Detection in Source Code Using Deep Representation Learning
Authors: Rebecca L. Russell, Louis Kim, Lei H. Hamilton, Tomo Lazovich, Jacob A. Harer, Onur Ozdemir, Paul M. Ellingwood, Marc W. McConley
Date: 2018-07-11 00:00:00
Tasks: Representation Learning, Vulnerability Detection

Increasing numbers of software vulnerabilities are discovered every year
whether they are reported publicly or discovered internally in proprietary
code. These vulnerabilities can pose serious risk of exploit and result in
system compromise, information leaks, or denial of service. We leveraged the
wealth of C and C++ open-source code available to develop a large-scale
function-level vulnerability detection system using machine learning. To
supplement existing labeled vulnerability datasets, we compiled a vast dataset
of millions of open-source functions and labeled it with carefully-selected
findings from three different static analyzers that indicate potential
exploits. The labeled dataset is available at: https://osf.io/d45bw/. Using
these datasets, we developed a fast and scalable vulnerability detection tool
based on deep feature representation learning that directly interprets lexed
source code. We evaluated our tool on code from both real software packages and
the NIST SATE IV benchmark dataset. Our results demonstrate that deep feature
representation learning on source code is a promising approach for automated
software vulnerability detection.
Manifold regularization with GANs for semi-supervised learning
Authors: Bruno Lecouat, Chuan-Sheng Foo, Houssam Zenati, Vijay Chandrasekhar
Date: 2018-07-11 00:00:00
Proceeding: ICLR 2019 5

Generative Adversarial Networks are powerful generative models that are able
to model the manifold of natural images. We leverage this property to perform
manifold regularization by approximating a variant of the Laplacian norm using
a Monte Carlo approximation that is easily computed with the GAN. When
incorporated into the semi-supervised feature-matching GAN we achieve
state-of-the-art results for GAN-based semi-supervised learning on CIFAR-10 and
SVHN benchmarks, with a method that is significantly easier to implement than
competing methods. We also find that manifold regularization improves the
quality of generated images, and is affected by the quality of the GAN used to
approximate the regularizer.
Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion
Authors: Steven Atkinson, Nicholas Zabaras
Date: 2018-07-11 00:00:00
Tasks: Dimensionality Reduction

We introduce a methodology for nonlinear inverse problems using a variational
Bayesian approach where the unknown quantity is a spatial field. A structured
Bayesian Gaussian process latent variable model is used both to construct a
low-dimensional generative model of the sample-based stochastic prior as well
as a surrogate for the forward evaluation. Its Bayesian formulation captures
epistemic uncertainty introduced by the limited number of input and output
examples, automatically selects an appropriate dimensionality for the learned
latent representation of the data, and rigorously propagates the uncertainty of
the data-driven dimensionality reduction of the stochastic space through the
forward model surrogate. The structured Gaussian process model explicitly
leverages spatial information for an informative generative prior to improve
sample efficiency while achieving computational tractability through Kronecker
product decompositions of the relevant kernel matrices. Importantly, the
Bayesian inversion is carried out by solving a variational optimization
problem, replacing traditional computationally-expensive Monte Carlo sampling.
The methodology is demonstrated on an elliptic PDE and is shown to return
well-calibrated posteriors and is tractable with latent spaces with over 100
dimensions.
PDE-constrained LDDMM via geodesic shooting and inexact Gauss-Newton-Krylov optimization using the incremental adjoint Jacobi equations
Authors: Monica Hernandez
Date: 2018-07-11 00:00:00

The class of non-rigid registration methods proposed in the framework of
PDE-constrained Large Deformation Diffeomorphic Metric Mapping is a
particularly interesting family of physically meaningful diffeomorphic
registration methods. Inexact Newton-Krylov optimization has shown an excellent
numerical accuracy and an extraordinarily fast convergence rate in this
framework. However, the Galerkin representation of the non-stationary velocity
fields does not provide proper geodesic paths. In this work, we propose a
method for PDE-constrained LDDMM parameterized in the space of initial velocity
fields under the EPDiff equation. The derivation of the gradient and the
Hessian-vector products are performed on the final velocity field and
transported backward using the adjoint and the incremental adjoint Jacobi
equations. This way, we avoid the complex dependence on the initial velocity
field in the derivations and the computation of the adjoint equation and its
incremental counterpart. The proposed method provides geodesics in the
framework of PDE-constrained LDDMM, and it shows performance competitive to
benchmark PDE-constrained LDDMM and EPDiff-LDDMM methods.
Moving Objects Analytics: Survey on Future Location & Trajectory Prediction Methods
Authors: Harris Georgiou, Sophia Karagiorgou, Yannis Kontoulis, Nikos Pelekis, Petros Petrou, David Scarlatti, Yannis Theodoridis
Date: 2018-07-11 00:00:00
Tasks: Prediction, Survey, Trajectory Prediction

The tremendous growth of positioning technologies and GPS enabled devices has
produced huge volumes of tracking data during the recent years. This source of
information constitutes a rich input for data analytics processes, either
offline (e.g. cluster analysis, hot motion discovery) or online (e.g.
short-term forecasting of forthcoming positions). This paper focuses on
predictive analytics for moving objects (could be pedestrians, cars, vessels,
planes, animals, etc.) and surveys the state-of-the-art in the context of
future location and trajectory prediction. We provide an extensive review of
over 50 works, also proposing a novel taxonomy of predictive algorithms over
moving objects. We also list the properties of several real datasets used in
the past for validation purposes of those works and, motivated by this, we
discuss challenges that arise in the transition from conventional to Big Data
applications.
  CCS Concepts: Information systems > Spatial-temporal systems; Information
systems > Data analytics; Information systems > Data mining; Computing
methodologies > Machine learning Additional Key Words and Phrases: mobility
data, moving object trajectories, trajectory prediction, future location
prediction.
Improving DNN-based Music Source Separation using Phase Features
Authors: Joachim Muth, Stefan Uhlich, Nathanael Perraudin, Thomas Kemp, Fabien Cardinaux, Yuki Mitsufuji
Date: 2018-07-07 00:00:00
Tasks: Music Source Separation

Music source separation with deep neural networks typically relies only on
amplitude features. In this paper we show that additional phase features can
improve the separation performance. Using the theoretical relationship between
STFT phase and amplitude, we conjecture that derivatives of the phase are a
good feature representation opposed to the raw phase. We verify this conjecture
experimentally and propose a new DNN architecture which combines amplitude and
phase. This joint approach achieves a better signal-to distortion ratio on the
DSD100 dataset for all instruments compared to a network that uses only
amplitude features. Especially, the bass instrument benefits from the phase
information.
A quantum-inspired classical algorithm for recommendation systems
Authors: Ewin Tang
Date: 2018-07-10 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning, Recommendation Systems

We give a classical analogue to Kerenidis and Prakash's quantum recommendation system, previously believed to be one of the strongest candidates for provably exponential speedups in quantum machine learning. Our main result is an algorithm that, given an $m \times n$ matrix in a data structure supporting certain $\ell^2$-norm sampling operations, outputs an $\ell^2$-norm sample from a rank-$k$ approximation of that matrix in time $O(\text{poly}(k)\log(mn))$, only polynomially slower than the quantum algorithm. As a consequence, Kerenidis and Prakash's algorithm does not in fact give an exponential speedup over classical algorithms. Further, under strong input assumptions, the classical recommendation system resulting from our algorithm produces recommendations exponentially faster than previous classical systems, which run in time linear in $m$ and $n$. The main insight of this work is the use of simple routines to manipulate $\ell^2$-norm sampling distributions, which play the role of quantum superpositions in the classical setting. This correspondence indicates a potentially fruitful framework for formally comparing quantum machine learning algorithms to classical machine learning algorithms.
Phylogenetic automata, pruning, and multiple alignment
Authors: Oscar Westesson, Gerton Lunter, Benedict Paten, Ian Holmes
Date: 2011-03-22 00:00:00
Tasks: Multiple Sequence Alignment

We present an extension of Felsenstein's algorithm to indel models defined on
entire sequences, without the need to condition on one multiple alignment. The
algorithm makes use of a generalization from probabilistic substitution
matrices to weighted finite-state transducers. Our approach may equivalently be
viewed as a probabilistic formulation of progressive multiple sequence
alignment, using partial-order graphs to represent ensemble profiles of
ancestral sequences. We present a hierarchical stochastic approximation
technique which makes this algorithm tractable for alignment analyses of
reasonable size.
The Garching-Bonn Deep Survey (GaBoDS) Wide-Field-Imaging Reduction Pipeline
Authors: H. Hildebrandt, T. Erben, M. Schirmer, J. P. Dietrich, P. Schneider
Date: 2007-05-03 00:00:00
Tasks: Survey

We introduce our publicly available Wide-Field-Imaging reduction pipeline
THELI. The procedures applied for the efficient pre-reduction and astrometric
calibration are presented. A special emphasis is put on the methods applied to
the photometric calibration. As a test case the reduction of optical data from
the ESO Deep Public Survey including the WFI-GOODS data is described. The
end-products of this project are now available via the ESO archive Advanced
Data Products section.
Active model learning and diverse action sampling for task and motion planning
Authors: Zi Wang, Caelan Reed Garrett, Leslie Pack Kaelbling, Tomás Lozano-Pérez
Date: 2018-03-02 00:00:00
Tasks: Active Learning, Motion Planning, Task and Motion Planning

The objective of this work is to augment the basic abilities of a robot by
learning to use new sensorimotor primitives to enable the solution of complex
long-horizon problems. Solving long-horizon problems in complex domains
requires flexible generative planning that can combine primitive abilities in
novel combinations to solve problems as they arise in the world. In order to
plan to combine primitive actions, we must have models of the preconditions and
effects of those actions: under what circumstances will executing this
primitive achieve some particular effect in the world?
  We use, and develop novel improvements on, state-of-the-art methods for
active learning and sampling. We use Gaussian process methods for learning the
conditions of operator effectiveness from small numbers of expensive training
examples collected by experimentation on a robot. We develop adaptive sampling
methods for generating diverse elements of continuous sets (such as robot
configurations and object poses) during planning for solving a new task, so
that planning is as efficient as possible. We demonstrate these methods in an
integrated system, combining newly learned models with an efficient
continuous-space robot task and motion planner to learn to solve long horizon
problems more efficiently than was previously possible.
The group fused Lasso for multiple change-point detection
Authors: Kevin Bleakley, Jean-Philippe Vert
Date: 2011-06-21 00:00:00
Tasks: Change Point Detection

We present the group fused Lasso for detection of multiple change-points
shared by a set of co-occurring one-dimensional signals. Change-points are
detected by approximating the original signals with a constraint on the
multidimensional total variation, leading to piecewise-constant approximations.
Fast algorithms are proposed to solve the resulting optimization problems,
either exactly or approximately. Conditions are given for consistency of both
algorithms as the number of signals increases, and empirical evidence is
provided to support the results on simulated and array comparative genomic
hybridization data.
Parametric generation of conditional geological realizations using generative neural networks
Authors: Shing Chan, Ahmed H. Elsheikh
Date: 2018-07-13 00:00:00
Tasks: Dimensionality Reduction

Deep learning techniques are increasingly being considered for geological applications where -- much like in computer vision -- the challenges are characterized by high-dimensional spatial data dominated by multipoint statistics. In particular, a novel technique called generative adversarial networks has been recently studied for geological parametrization and synthesis, obtaining very impressive results that are at least qualitatively competitive with previous methods. The method obtains a neural network parametrization of the geology -- so-called a generator -- that is capable of reproducing very complex geological patterns with dimensionality reduction of several orders of magnitude. Subsequent works have addressed the conditioning task, i.e. using the generator to generate realizations honoring spatial observations (hard data). The current approaches, however, do not provide a parametrization of the conditional generation process. In this work, we propose a method to obtain a parametrization for direct generation of conditional realizations. The main idea is to simply extend the existing generator network by stacking a second inference network that learns to perform the conditioning. This inference network is a neural network trained to sample a posterior distribution derived using a Bayesian formulation of the conditioning task. The resulting extended neural network thus provides the conditional parametrization. Our method is assessed on a benchmark image of binary channelized subsurface, obtaining very promising results for a wide variety of conditioning configurations.
Image Classification for Arabic: Assessing the Accuracy of Direct English to Arabic Translations
Authors: Abdulkareem Alsudais
Date: 2018-07-13 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Translation

Image classification is an ongoing research challenge. Most of the available research focuses on image classification for the English language, however there is very little research on image classification for the Arabic language. Expanding image classification to Arabic has several applications. The present study investigated a method for generating Arabic labels for images of objects. The method used in this study involved a direct English to Arabic translation of the labels that are currently available on ImageNet, a database commonly used in image classification research. The purpose of this study was to test the accuracy of this method. In this study, 2,887 labeled images were randomly selected from ImageNet. All of the labels were translated from English to Arabic using Google Translate. The accuracy of the translations was evaluated. Results indicated that that 65.6% of the Arabic labels were accurate. This study makes three important contributions to the image classification literature: (1) it determined the baseline level of accuracy for algorithms that provide Arabic labels for images, (2) it provided 1,895 images that are tagged with accurate Arabic labels, and (3) provided the accuracy of translations of image labels from English to Arabic.
Artificial Intelligence for Long-Term Robot Autonomy: A Survey
Authors: Lars Kunze, Nick Hawes, Tom Duckett, Marc Hanheide, Tomáš Krajník
Date: 2018-07-13 00:00:00
Tasks: Survey

Autonomous systems will play an essential role in many applications across
diverse domains including space, marine, air, field, road, and service
robotics. They will assist us in our daily routines and perform dangerous,
dirty and dull tasks. However, enabling robotic systems to perform autonomously
in complex, real-world scenarios over extended time periods (i.e. weeks,
months, or years) poses many challenges. Some of these have been investigated
by sub-disciplines of Artificial Intelligence (AI) including navigation &
mapping, perception, knowledge representation & reasoning, planning,
interaction, and learning. The different sub-disciplines have developed
techniques that, when re-integrated within an autonomous system, can enable
robots to operate effectively in complex, long-term scenarios. In this paper,
we survey and discuss AI techniques as 'enablers' for long-term robot autonomy,
current progress in integrating these techniques within long-running robotic
systems, and the future challenges and opportunities for AI in long-term
autonomy.
Low-Resource Text Classification using Domain-Adversarial Learning
Authors: Daniel Grießhaber, Ngoc Thang Vu, Johannes Maucher
Date: 2018-07-13 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

Deep learning techniques have recently shown to be successful in many natural language processing tasks forming state-of-the-art systems. They require, however, a large amount of annotated data which is often missing. This paper explores the use of domain-adversarial learning as a regularizer to avoid overfitting when training domain invariant features for deep, complex neural networks in low-resource and zero-resource settings in new target domains or languages. In case of new languages, we show that monolingual word vectors can be directly used for training without prealignment. Their projection into a common space can be learnt ad-hoc at training time reaching the final performance of pretrained multilingual word vectors.
Model Reconstruction from Model Explanations
Authors: Smitha Milli, Ludwig Schmidt, Anca D. Dragan, Moritz Hardt
Date: 2018-07-13 00:00:00
Tasks: model

We show through theory and experiment that gradient-based explanations of a
model quickly reveal the model itself. Our results speak to a tension between
the desire to keep a proprietary model secret and the ability to offer model
explanations. On the theoretical side, we give an algorithm that provably
learns a two-layer ReLU network in a setting where the algorithm may query the
gradient of the model with respect to chosen inputs. The number of queries is
independent of the dimension and nearly optimal in its dependence on the model
size. Of interest not only from a learning-theoretic perspective, this result
highlights the power of gradients rather than labels as a learning primitive.
Complementing our theory, we give effective heuristics for reconstructing
models from gradient explanations that are orders of magnitude more
query-efficient than reconstruction attacks relying on prediction interfaces.