pwc-archive/papers-with-abstracts huggingface dataset:
Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery
Authors: Joseph Camilo, Rui Wang, Leslie M. Collins, Kyle Bradbury, Jordan M. Malof
Date: 2018-01-11 00:00:00
Tasks: Semantic Segmentation

We consider the problem of automatically detecting small-scale solar
photovoltaic arrays for behind-the-meter energy resource assessment in high
resolution aerial imagery. Such algorithms offer a faster and more
cost-effective solution to collecting information on distributed solar
photovoltaic (PV) arrays, such as their location, capacity, and generated
energy. The surface area of PV arrays, a characteristic which can be estimated
from aerial imagery, provides an important proxy for array capacity and energy
generation. In this work, we employ a state-of-the-art convolutional neural
network architecture, called SegNet (Badrinarayanan et. al., 2015), to
semantically segment (or map) PV arrays in aerial imagery. This builds on
previous work focused on identifying the locations of PV arrays, as opposed to
their specific shapes and sizes. We measure the ability of our SegNet
implementation to estimate the surface area of PV arrays on a large, publicly
available, dataset that has been employed in several previous studies. The
results indicate that the SegNet model yields substantial performance
improvements with respect to estimating shape and size as compared to a
recently proposed convolutional neural network PV detection algorithm.
Did William Shakespeare and Thomas Kyd Write Edward III?
Authors: David Kernot, Terry Bossomaier, Roger Bradbury
Date: 2018-01-11 00:00:00

William Shakespeare is believed to be a significant author in the anonymous
play, The Reign of King Edward III, published in 1596. However, recently,
Thomas Kyd, has been suggested as the primary author. Using a neurolinguistics
approach to authorship identification we use a four-feature technique, RPAS, to
convert the 19 scenes in Edward III into a multi-dimensional vector. Three
complementary analytical techniques are applied to cluster the data and reduce
single technique bias before an alternate method, seriation, is used to measure
the distances between clusters and test the strength of the connections. We
find the multivariate techniques robust and are able to allocate up to 14
scenes to Thomas Kyd, and further question if scenes long believed to be
Shakespeare's are not his.
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
Authors: Judea Pearl
Date: 2018-01-11 00:00:00
Tasks: BIG-bench Machine Learning, Causal Inference

Current machine learning systems operate, almost exclusively, in a
statistical, or model-free mode, which entails severe theoretical limits on
their power and performance. Such systems cannot reason about interventions and
retrospection and, therefore, cannot serve as the basis for strong AI. To
achieve human level intelligence, learning machines need the guidance of a
model of reality, similar to the ones used in causal inference tasks. To
demonstrate the essential role of such models, I will present a summary of
seven tasks which are beyond reach of current machine learning systems and
which have been accomplished using the tools of causal modeling.
Brain Age Prediction Based on Resting-State Functional Connectivity Patterns Using Convolutional Neural Networks
Authors: Hongming Li, Theodore D. Satterthwaite, Yong Fan
Date: 2018-01-11 00:00:00
Tasks: Deep Learning, Functional Connectivity, Prediction

Brain age prediction based on neuroimaging data could help characterize both
the typical brain development and neuropsychiatric disorders. Pattern
recognition models built upon functional connectivity (FC) measures derived
from resting state fMRI (rsfMRI) data have been successfully used to predict
the brain age. However, most existing studies focus on coarse-grained FC
measures between brain regions or intrinsic connectivity networks (ICNs), which
may sacrifice fine-grained FC information of the rsfMRI data. Whole brain
voxel-wise FC measures could provide fine-grained FC information of the brain
and may improve the prediction performance. In this study, we develop a deep
learning method to use convolutional neural networks (CNNs) to learn
informative features from the fine-grained whole brain FC measures for the
brain age prediction. Experimental results on a large dataset of resting-state
fMRI demonstrate that the deep learning model with fine-grained FC measures
could better predict the brain age.
Non-Rigid Image Registration Using Self-Supervised Fully Convolutional Networks without Training Data
Authors: Hongming Li, Yong Fan
Date: 2018-01-11 00:00:00
Tasks: Image Registration, Self-Supervised Learning

A novel non-rigid image registration algorithm is built upon fully
convolutional networks (FCNs) to optimize and learn spatial transformations
between pairs of images to be registered in a self-supervised learning
framework. Different from most existing deep learning based image registration
methods that learn spatial transformations from training data with known
corresponding spatial transformations, our method directly estimates spatial
transformations between pairs of images by maximizing an image-wise similarity
metric between fixed and deformed moving images, similar to conventional image
registration algorithms. The image registration is implemented in a
multi-resolution image registration framework to jointly optimize and learn
spatial transformations and FCNs at different spatial resolutions with deep
self-supervision through typical feedforward and backpropagation computation.
The proposed method has been evaluated for registering 3D structural brain
magnetic resonance (MR) images and obtained better performance than
state-of-the-art image registration algorithms.
Enhancing Underwater Imagery using Generative Adversarial Networks
Authors: Cameron Fabbri, Md Jahidul Islam, Junaed Sattar
Date: 2018-01-11 00:00:00
Tasks: Decision Making, Image Restoration, Underwater Image Restoration

Autonomous underwater vehicles (AUVs) rely on a variety of sensors -
acoustic, inertial and visual - for intelligent decision making. Due to its
non-intrusive, passive nature, and high information content, vision is an
attractive sensing modality, particularly at shallower depths. However, factors
such as light refraction and absorption, suspended particles in the water, and
color distortion affect the quality of visual data, resulting in noisy and
distorted images. AUVs that rely on visual sensing thus face difficult
challenges, and consequently exhibit poor performance on vision-driven tasks.
This paper proposes a method to improve the quality of visual underwater scenes
using Generative Adversarial Networks (GANs), with the goal of improving input
to vision-driven behaviors further down the autonomy pipeline. Furthermore, we
show how recently proposed methods are able to generate a dataset for the
purpose of such underwater image restoration. For any visually-guided
underwater robots, this improvement can result in increased safety and
reliability through robust visual perception. To that effect, we present
quantitative and qualitative data which demonstrates that images corrected
through the proposed approach generate more visually appealing images, and also
provide increased accuracy for a diver tracking algorithm.
Implicit LOD using points ordering for processing and visualisation in Point Cloud Servers
Authors: Rémi Cura, Julien Perret, Nicolas Paparoditis
Date: 2016-02-22 00:00:00

Lidar datasets now commonly reach Billions of points and are very dense.
Using these point cloud becomes challenging, as the high number of points is
intractable for most applications and for visualisation.In this work we propose
a new paradigm to easily get a portable geometric Level Of Details (LOD) inside
a Point Cloud Server.The main idea is to not store the LOD information in an
external additional file, but instead to store it implicitly by exploiting the
order of the points.The point cloud is divided into groups (patches). These
patches are ordered so that their order gradually provides more and more
details on the patch. We demonstrate the interest of our method with several
classical uses of LOD, such as visualisation of massive point cloud, algorithm
acceleration, fast density peak detection and correction.
Adversarial Spheres
Authors: Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian Goodfellow
Date: 2018-01-09 00:00:00
Proceeding: ICLR 2018 1

State of the art computer vision models have been shown to be vulnerable to
small adversarial perturbations of the input. In other words, most images in
the data distribution are both correctly classified by the model and are very
close to a visually similar misclassified image. Despite substantial research
interest, the cause of the phenomenon is still poorly understood and remains
unsolved. We hypothesize that this counter intuitive behavior is a naturally
occurring result of the high dimensional geometry of the data manifold. As a
first step towards exploring this hypothesis, we study a simple synthetic
dataset of classifying between two concentric high dimensional spheres. For
this dataset we show a fundamental tradeoff between the amount of test error
and the average distance to nearest error. In particular, we prove that any
model which misclassifies a small constant fraction of a sphere will be
vulnerable to adversarial perturbations of size $O(1/\sqrt{d})$. Surprisingly,
when we train several different architectures on this dataset, all of their
error sets naturally approach this theoretical bound. As a result of the
theory, the vulnerability of neural networks to small adversarial perturbations
is a logical consequence of the amount of test error observed. We hope that our
theoretical analysis of this very simple case will point the way forward to
explore how the geometry of complex real-world data sets leads to adversarial
examples.
An Order Preserving Bilinear Model for Person Detection in Multi-Modal Data
Authors: Oytun Ulutan, Benjamin S. Riggan, Nasser M. Nasrabadi, B. S. Manjunath
Date: 2017-12-20 00:00:00
Tasks: Human Detection

We propose a new order preserving bilinear framework that exploits
low-resolution video for person detection in a multi-modal setting using deep
neural networks. In this setting cameras are strategically placed such that
less robust sensors, e.g. geophones that monitor seismic activity, are located
within the field of views (FOVs) of cameras. The primary challenge is being
able to leverage sufficient information from videos where there are less than
40 pixels on targets, while also taking advantage of less discriminative
information from other modalities, e.g. seismic. Unlike state-of-the-art
methods, our bilinear framework retains spatio-temporal order when computing
the vector outer products between pairs of features. Despite the high
dimensionality of these outer products, we demonstrate that our order
preserving bilinear framework yields better performance than recent orderless
bilinear models and alternative fusion methods.
Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification
Authors: Xiaoyu Cao, Neil Zhenqiang Gong
Date: 2017-09-17 00:00:00
Tasks: Classification, General Classification, Self-Driving Cars, speech-recognition, Speech Recognition

Deep neural networks (DNNs) have transformed several artificial intelligence research areas including computer vision, speech recognition, and natural language processing. However, recent studies demonstrated that DNNs are vulnerable to adversarial manipulations at testing time. Specifically, suppose we have a testing example, whose label can be correctly predicted by a DNN classifier. An attacker can add a small carefully crafted noise to the testing example such that the DNN classifier predicts an incorrect label, where the crafted testing example is called adversarial example. Such attacks are called evasion attacks. Evasion attacks are one of the biggest challenges for deploying DNNs in safety and security critical applications such as self-driving cars. In this work, we develop new methods to defend against evasion attacks. Our key observation is that adversarial examples are close to the classification boundary. Therefore, we propose region-based classification to be robust to adversarial examples. For a benign/adversarial testing example, we ensemble information in a hypercube centered at the example to predict its label. In contrast, traditional classifiers are point-based classification, i.e., given a testing example, the classifier predicts its label based on the testing example alone. Our evaluation results on MNIST and CIFAR-10 datasets demonstrate that our region-based classification can significantly mitigate evasion attacks without sacrificing classification accuracy on benign examples. Specifically, our region-based classification achieves the same classification accuracy on testing benign examples as point-based classification, but our region-based classification is significantly more robust than point-based classification to various evasion attacks.
Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction
Authors: Mingze Xu, Chenyou Fan, John D Paden, Geoffrey C. Fox, David J. Crandall
Date: 2018-01-11 00:00:00
Tasks: Structured Prediction, Surface Reconstruction

Deep learning methods have surpassed the performance of traditional
techniques on a wide range of problems in computer vision, but nearly all of
this work has studied consumer photos, where precisely correct output is often
not critical. It is less clear how well these techniques may apply on
structured prediction problems where fine-grained output with high precision is
required, such as in scientific imaging domains. Here we consider the problem
of segmenting echogram radar data collected from the polar ice sheets, which is
challenging because segmentation boundaries are often very weak and there is a
high degree of noise. We propose a multi-task spatiotemporal neural network
that combines 3D ConvNets and Recurrent Neural Networks (RNNs) to estimate ice
surface boundaries from sequences of tomographic radar images. We show that our
model outperforms the state-of-the-art on this problem by (1) avoiding the need
for hand-tuned parameters, (2) extracting multiple surfaces (ice-air and
ice-bed) simultaneously, (3) requiring less non-visual metadata, and (4) being
about 6 times faster.
A Brain-Inspired Trust Management Model to Assure Security in a Cloud based IoT Framework for Neuroscience Applications
Authors: Mufti Mahmud, M. Shamim Kaiser, M. Mostafizur Rahman, M. Arifur Rahman, Antesar Shabut, Shamim Al-Mamun, Amir Hussain
Date: 2018-01-11 00:00:00
Tasks: Cloud Computing, Management

Rapid popularity of Internet of Things (IoT) and cloud computing permits
neuroscientists to collect multilevel and multichannel brain data to better
understand brain functions, diagnose diseases, and devise treatments. To ensure
secure and reliable data communication between end-to-end (E2E) devices
supported by current IoT and cloud infrastructure, trust management is needed
at the IoT and user ends. This paper introduces a Neuro-Fuzzy based
Brain-inspired trust management model (TMM) to secure IoT devices and relay
nodes, and to ensure data reliability. The proposed TMM utilizes node
behavioral trust and data trust estimated using Adaptive Neuro-Fuzzy Inference
System and weighted-additive methods respectively to assess the nodes
trustworthiness. In contrast to the existing fuzzy based TMMs, the NS2
simulation results confirm the robustness and accuracy of the proposed TMM in
identifying malicious nodes in the communication network. With the growing
usage of cloud based IoT frameworks in Neuroscience research, integrating the
proposed TMM into the existing infrastructure will assure secure and reliable
data communication among the E2E devices.
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
Authors: Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F. Stewart, Jimeng Sun
Date: 2017-03-19 00:00:00
Tasks: Attribute, Generative Adversarial Network

Access to electronic health record (EHR) data has motivated computational
advances in medical research. However, various concerns, particularly over
privacy, can limit access to and collaborative use of EHR data. Sharing
synthetic EHR data could mitigate risk. In this paper, we propose a new
approach, medical Generative Adversarial Network (medGAN), to generate
realistic synthetic patient records. Based on input real patient records,
medGAN can generate high-dimensional discrete variables (e.g., binary and count
features) via a combination of an autoencoder and generative adversarial
networks. We also propose minibatch averaging to efficiently avoid mode
collapse, and increase the learning efficiency with batch normalization and
shortcut connections. To demonstrate feasibility, we showed that medGAN
generates synthetic patient records that achieve comparable performance to real
data on many experiments including distribution statistics, predictive modeling
tasks and a medical expert review. We also empirically observe a limited
privacy risk in both identity and attribute disclosure using medGAN.
Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition
Authors: Mingze Xu, Aidean Sharghi, Xin Chen, David J. Crandall
Date: 2018-01-11 00:00:00
Tasks: Action Recognition, Temporal Action Localization

A major emerging challenge is how to protect people's privacy as cameras and
computer vision are increasingly integrated into our daily lives, including in
smart devices inside homes. A potential solution is to capture and record just
the minimum amount of information needed to perform a task of interest. In this
paper, we propose a fully-coupled two-stream spatiotemporal architecture for
reliable human action recognition on extremely low resolution (e.g., 12x16
pixel) videos. We provide an efficient method to extract spatial and temporal
features and to aggregate them into a robust feature representation for an
entire action video sequence. We also consider how to incorporate high
resolution videos during training in order to build better low resolution
action recognition models. We evaluate on two publicly-available datasets,
showing significant improvements over the state-of-the-art.
Clusters of Driving Behavior from Observational Smartphone Data
Authors: Josh Warren, Jeff Lipkowitz, Vadim Sokolov
Date: 2017-10-12 00:00:00

Understanding driving behaviors is essential for improving safety and
mobility of our transportation systems. Data is usually collected via
simulator-based studies or naturalistic driving studies. Those techniques allow
for understanding relations between demographics, road conditions and safety.
On the other hand, they are very costly and time consuming. Thanks to the
ubiquity of smartphones, we have an opportunity to substantially complement
more traditional data collection techniques with data extracted from phone
sensors, such as GPS, accelerometer gyroscope and camera. We developed
statistical models that provided insight into driver behavior in the San
Francisco metro area based on tens of thousands of driver logs. We used novel
data sources to support our work. We used cell phone sensor data drawn from
five hundred drivers in San Francisco to understand the speed of traffic across
the city as well as the maneuvers of drivers in different areas. Specifically,
we clustered drivers based on their driving behavior. We looked at driver norms
by street and flagged driving behaviors that deviated from the norm.
Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
Authors: Robin Tibor Schirrmeister, Lukas Gemein, Katharina Eggensperger, Frank Hutter, Tonio Ball
Date: 2017-08-26 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

We apply convolutional neural networks (ConvNets) to the task of
distinguishing pathological from normal EEG recordings in the Temple University
Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet
architectures recently shown to decode task-related information from EEG at
least as well as established algorithms designed for this purpose. In decoding
EEG pathology, both ConvNets reached substantially better accuracies (about 6%
better, ~85% vs. ~79%) than the only published result for this dataset, and
were still better when using only 1 minute of each recording for training and
only six seconds of each recording for testing. We used automated methods to
optimize architectural hyperparameters and found intriguingly different ConvNet
architectures, e.g., with max pooling as the only nonlinearity. Visualizations
of the ConvNet decoding behavior showed that they used spectral power changes
in the delta (0-4 Hz) and theta (4-8 Hz) frequency range, possibly alongside
other features, consistent with expectations derived from spectral analysis of
the EEG data and from the textual medical reports. Analysis of the textual
medical reports also highlighted the potential for accuracy increases by
integrating contextual information, such as the age of subjects. In summary,
the ConvNets and visualization techniques used in this study constitute a next
step towards clinically useful automated EEG diagnosis and establish a new
baseline for future work on this topic.
The Complexity of Learning Acyclic Conditional Preference Networks
Authors: Eisa Alanazi, Malek Mouhoub, Sandra Zilles
Date: 2018-01-11 00:00:00
Tasks: Attribute

Learning of user preferences, as represented by, for example, Conditional
Preference Networks (CP-nets), has become a core issue in AI research. Recent
studies investigate learning of CP-nets from randomly chosen examples or from
membership and equivalence queries. To assess the optimality of learning
algorithms as well as to better understand the combinatorial structure of
classes of CP-nets, it is helpful to calculate certain learning-theoretic
information complexity parameters. This article focuses on the frequently
studied case of learning from so-called swap examples, which express
preferences among objects that differ in only one attribute. It presents bounds
on or exact values of some well-studied information complexity parameters,
namely the VC dimension, the teaching dimension, and the recursive teaching
dimension, for classes of acyclic CP-nets. We further provide algorithms that
learn tree-structured and general acyclic CP-nets from membership queries.
Using our results on complexity parameters, we assess the optimality of our
algorithms as well as that of another query learning algorithm for acyclic
CP-nets presented in the literature. Our algorithms are near-optimal, and can,
under certain assumptions, be adapted to the case when the membership oracle is
faulty.
Unsupervised Discovery of Toxoplasma gondii Motility Phenotypes
Authors: Mojtaba S. Fazli, Stephen A. Vella, Silvia N. J. Moreno, Shannon Quinn
Date: 2018-01-08 00:00:00

Toxoplasma gondii is a parasitic protozoan that causes dis- seminated
toxoplasmosis, a disease that afflicts roughly a third of the worlds
population. Its virulence is predicated on its motility and ability to enter
and exit nucleated cells; therefore, studies elucidating its mechanism of
motility and in particular, its motility patterns in the context of its lytic
cycle, are critical to the eventual development of therapeutic strate- gies.
Here, we present an end-to-end computational pipeline for identifying T. gondii
motility phenotypes in a completely unsupervised, data-driven way. We track the
parasites before and after addition of extracellular Ca2+ to study its effects
on the parasite motility patterns and use this information to parameterize the
motion and group it according to similarity of spatiotemporal dynamics.