pwc-archive/papers-with-abstracts huggingface dataset:
Detecting and Explaining Drifts in Yearly Grant Applications
Authors: Stephen Pauwels, Toon Calders
Date: 2018-09-15 00:00:00

During the lifetime of a Business Process changes can be made to the
workflow, the required resources, required documents, . . . . Different traces
from the same Business Process within a single log file can thus differ
substantially due to these changes. We propose a method that is able to detect
concept drift in multivariate log files with a dozen attributes. We test our
approach on the BPI Challenge 2018 data con- sisting of applications for EU
direct payment from farmers in Germany where we use it to detect Concept Drift.
In contrast to other methods our algorithm does not require the manual
selection of the features used to detect drift. Our method first creates a
model that captures the re- lations between attributes and between events of
different time steps. This model is then used to score every event and trace.
These scores can be used to detect outlying cases and concept drift. Thanks to
the decomposability of the score we are able to perform detailed root-cause
analysis.
OffsetNet: Deep Learning for Localization in the Lung using Rendered Images
Authors: Jake Sganga, David Eng, Chauncey Graetzel, David Camarillo
Date: 2018-09-15 00:00:00
Tasks: Anatomy, Generative Adversarial Network, Position

Navigating surgical tools in the dynamic and tortuous anatomy of the lung's
airways requires accurate, real-time localization of the tools with respect to
the preoperative scan of the anatomy. Such localization can inform human
operators or enable closed-loop control by autonomous agents, which would
require accuracy not yet reported in the literature. In this paper, we
introduce a deep learning architecture, called OffsetNet, to accurately
localize a bronchoscope in the lung in real-time. After training on only 30
minutes of recorded camera images in conserved regions of a lung phantom,
OffsetNet tracks the bronchoscope's motion on a held-out recording through
these same regions at an update rate of 47 Hz and an average position error of
1.4 mm. Because this model performs poorly in less conserved regions, we
augment the training dataset with simulated images from these regions. To
bridge the gap between camera and simulated domains, we implement domain
randomization and a generative adversarial network (GAN). After training on
simulated images, OffsetNet tracks the bronchoscope's motion in less conserved
regions at an average position error of 2.4 mm, which meets conservative
thresholds required for successful tracking.
Geo-Text Data and Data-Driven Geospatial Semantics
Authors: Yingjie Hu
Date: 2018-09-15 00:00:00
Tasks: Articles

Many datasets nowadays contain links between geographic locations and natural
language texts. These links can be geotags, such as geotagged tweets or
geotagged Wikipedia pages, in which location coordinates are explicitly
attached to texts. These links can also be place mentions, such as those in
news articles, travel blogs, or historical archives, in which texts are
implicitly connected to the mentioned places. This kind of data is referred to
as geo-text data. The availability of large amounts of geo-text data brings
both challenges and opportunities. On the one hand, it is challenging to
automatically process this kind of data due to the unstructured texts and the
complex spatial footprints of some places. On the other hand, geo-text data
offers unique research opportunities through the rich information contained in
texts and the special links between texts and geography. As a result, geo-text
data facilitates various studies especially those in data-driven geospatial
semantics. This paper discusses geo-text data and related concepts. With a
focus on data-driven research, this paper systematically reviews a large number
of studies that have discovered multiple types of knowledge from geo-text data.
Based on the literature review, a generalized workflow is extracted and key
challenges for future work are discussed.
Towards Better Interpretability in Deep Q-Networks
Authors: Raghuram Mandyam Annasamy, Katia Sycara
Date: 2018-09-15 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning techniques have demonstrated superior performance
in a wide variety of environments. As improvements in training algorithms
continue at a brisk pace, theoretical or empirical studies on understanding
what these networks seem to learn, are far behind. In this paper we propose an
interpretable neural network architecture for Q-learning which provides a
global explanation of the model's behavior using key-value memories, attention
and reconstructible embeddings. With a directed exploration strategy, our model
can reach training rewards comparable to the state-of-the-art deep Q-learning
models. However, results suggest that the features extracted by the neural
network are extremely shallow and subsequent testing using out-of-sample
examples shows that the agent can easily overfit to trajectories seen during
training.
DocFace+: ID Document to Selfie Matching
Authors: Yichun Shi, Anil K. Jain
Date: 2018-09-15 00:00:00
Tasks: TAR

Numerous activities in our daily life require us to verify who we are by
showing our ID documents containing face images, such as passports and driver
licenses, to human operators. However, this process is slow, labor intensive
and unreliable. As such, an automated system for matching ID document photos to
live face images (selfies) in real time and with high accuracy is required. In
this paper, we propose DocFace+ to meet this objective. We first show that
gradient-based optimization methods converge slowly (due to the underfitting of
classifier weights) when many classes have very few samples, a characteristic
of existing ID-selfie datasets. To overcome this shortcoming, we propose a
method, called dynamic weight imprinting (DWI), to update the classifier
weights, which allows faster convergence and more generalizable
representations. Next, a pair of sibling networks with partially shared
parameters are trained to learn a unified face representation with
domain-specific parameters. Cross-validation on an ID-selfie dataset shows that
while a publicly available general face matcher (SphereFace) only achieves a
True Accept Rate (TAR) of 59.29+-1.55% at a False Accept Rate (FAR) of 0.1% on
the problem, DocFace+ improves the TAR to 97.51+-0.40%.
A study on the use of Boundary Equilibrium GAN for Approximate Frontalization of Unconstrained Faces to aid in Surveillance
Authors: Wazeer Zulfikar, Sebastin Santy, Sahith Dambekodi, Tirtharaj Dash
Date: 2018-09-14 00:00:00
Tasks: Face Generation, Generative Adversarial Network

Face frontalization is the process of synthesizing frontal facing views of
faces given its angled poses. We implement a generative adversarial network
(GAN) with spherical linear interpolation (Slerp) for frontalization of
unconstrained facial images. Our special focus is intended towards the
generation of approximate frontal faces of the side posed images captured from
surveillance cameras. Specifically, the present work is a comprehensive study
on the implementation of an auto-encoder based Boundary Equilibrium GAN (BEGAN)
to generate frontal faces using an interpolation of a side view face and its
mirrored view. To increase the quality of the interpolated output we implement
a BEGAN with Slerp. This approach could produce a promising output along with a
faster and more stable training for the model. The BEGAN model additionally has
a balanced generator-discriminator combination, which prevents mode collapse
along with a global convergence measure. It is expected that such an
approximate face generation model would be able to replace face composites used
in surveillance and crime detection.
Non-iterative recomputation of dense layers for performance improvement of DCNN
Authors: Yimin Yang, Q. M. Jonathan Wu, Xiexing Feng, Thangarajah Akilan
Date: 2018-09-14 00:00:00
Tasks: Object Recognition

An iterative method of learning has become a paradigm for training deep
convolutional neural networks (DCNN). However, utilizing a non-iterative
learning strategy can accelerate the training process of the DCNN and
surprisingly such approach has been rarely explored by the deep learning (DL)
community. It motivates this paper to introduce a non-iterative learning
strategy that eliminates the backpropagation (BP) at the top dense or fully
connected (FC) layers of DCNN, resulting in, lower training time and higher
performance. The proposed method exploits the Moore-Penrose Inverse to pull
back the current residual error to each FC layer, generating well-generalized
features. Then using the recomputed features, i.e., the new generalized
features the weights of each FC layer is computed according to the
Moore-Penrose Inverse. We evaluate the proposed approach on six widely accepted
object recognition benchmark datasets: Scene-15, CIFAR-10, CIFAR-100, SUN-397,
Places365, and ImageNet. The experimental results show that the proposed method
obtains significant improvements over 30 state-of-the-art methods.
Interestingly, it also indicates that any DCNN with the proposed method can
provide better performance than the same network with its original training
based on BP.
The Generic Holdout: Preventing False-Discoveries in Adaptive Data Science
Authors: Preetum Nakkiran, Jarosław Błasiok
Date: 2018-09-14 00:00:00
Tasks: Holdout Set

Adaptive data analysis has posed a challenge to science due to its ability to
generate false hypotheses on moderately large data sets. In general, with
non-adaptive data analyses (where queries to the data are generated without
being influenced by answers to previous queries) a data set containing $n$
samples may support exponentially many queries in $n$. This number reduces to
linearly many under naive adaptive data analysis, and even sophisticated
remedies such as the Reusable Holdout (Dwork et. al 2015) only allow
quadratically many queries in $n$.
  In this work, we propose a new framework for adaptive science which
exponentially improves on this number of queries under a restricted yet
scientifically relevant setting, where the goal of the scientist is to find a
single (or a few) true hypotheses about the universe based on the samples. Such
a setting may describe the search for predictive factors of some disease based
on medical data, where the analyst may wish to try a number of predictive
models until a satisfactory one is found.
  Our solution, the Generic Holdout, involves two simple ingredients: (1) a
partitioning of the data into a exploration set and a holdout set and (2) a
limited exposure strategy for the holdout set. An analyst is free to use the
exploration set arbitrarily, but when testing hypotheses against the holdout
set, the analyst only learns the answer to the question: "Is the given
hypothesis true (empirically) on the holdout set?" -- and no more information,
such as "how well" the hypothesis fits the holdout set. The resulting scheme is
immediate to analyze, but despite its simplicity we do not believe our method
is obvious, as evidenced by the many violations in practice.
  Our proposal can be seen as an alternative to pre-registration, and allows
researchers to get the benefits of adaptive data analysis without the problems
of adaptivity.
Ensemble Clustering for Graphs
Authors: Valérie Poulin, François Théberge
Date: 2018-09-14 00:00:00
Tasks: Clustering, Graph Clustering

We propose an ensemble clustering algorithm for graphs (ECG), which is based
on the Louvain algorithm and the concept of consensus clustering. We validate
our approach by replicating a recently published study comparing graph
clustering algorithms over artificial networks, showing that ECG outperforms
the leading algorithms from that study. We also illustrate how the ensemble
obtained with ECG can be used to quantify the presence of community structure
in the graph.
Events Beyond ACE: Curated Training for Events
Authors: Ryan Gabbard, Jay DeYoung, Marjorie Freedman
Date: 2018-09-14 00:00:00
Tasks: Active Learning, Event Argument Extraction

We explore a human-driven approach to annotation, curated training (CT), in
which annotation is framed as teaching the system by using interactive search
to identify informative snippets of text to annotate, unlike traditional
approaches which either annotate preselected text or use active learning. A
trained annotator performed 80 hours of CT for the thirty event types of the
NIST TAC KBP Event Argument Extraction evaluation. Combining this annotation
with ACE results in a 6% reduction in error and the learning curve of CT
plateaus more slowly than for full-document annotation. 3 NLP researchers
performed CT for one event type and showed much sharper learning curves with
all three exceeding ACE performance in less than ninety minutes, suggesting
that CT can provide further benefits when the annotator deeply understands the
system.
Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation
Authors: Deqing Sun, Xiaodong Yang, Ming-Yu Liu, Jan Kautz
Date: 2018-09-14 00:00:00
Tasks: Optical Flow Estimation

We investigate two crucial and closely related aspects of CNNs for optical
flow estimation: models and training. First, we design a compact but effective
CNN model, called PWC-Net, according to simple and well-established principles:
pyramidal processing, warping, and cost volume processing. PWC-Net is 17 times
smaller in size, 2 times faster in inference, and 11\% more accurate on Sintel
final than the recent FlowNet2 model. It is the winning entry in the optical
flow competition of the robust vision challenge. Next, we experimentally
analyze the sources of our performance gains. In particular, we use the same
training procedure of PWC-Net to retrain FlowNetC, a sub-network of FlowNet2.
The retrained FlowNetC is 56\% more accurate on Sintel final than the
previously trained one and even 5\% more accurate than the FlowNet2 model. We
further improve the training procedure and increase the accuracy of PWC-Net on
Sintel by 10\% and on KITTI 2012 and 2015 by 20\%. Our newly trained model
parameters and training protocols will be available on
https://github.com/NVlabs/PWC-Net
Brain decoding from functional MRI using long short-term memory recurrent neural networks
Authors: Hongming Li, Yong Fan
Date: 2018-09-14 00:00:00
Tasks: Brain Decoding, Functional Connectivity

Decoding brain functional states underlying different cognitive processes
using multivariate pattern recognition techniques has attracted increasing
interests in brain imaging studies. Promising performance has been achieved
using brain functional connectivity or brain activation signatures for a
variety of brain decoding tasks. However, most of existing studies have built
decoding models upon features extracted from imaging data at individual time
points or temporal windows with a fixed interval, which might not be optimal
across different cognitive processes due to varying temporal durations and
dependency of different cognitive processes. In this study, we develop a deep
learning based framework for brain decoding by leveraging recent advances in
sequence modeling using long short-term memory (LSTM) recurrent neural networks
(RNNs). Particularly, functional profiles extracted from task functional
imaging data based on their corresponding subject-specific intrinsic functional
networks are used as features to build brain decoding models, and LSTM RNNs are
adopted to learn decoding mappings between functional profiles and brain
states. We evaluate the proposed method using task fMRI data from the HCP
dataset, and experimental results have demonstrated that the proposed method
could effectively distinguish brain states under different task events and
obtain higher accuracy than conventional decoding models.
Identification of temporal transition of functional states using recurrent neural networks from functional MRI
Authors: Hongming Li, Yong Fan
Date: 2018-09-14 00:00:00
Tasks: Anomaly Detection, Change Point Detection, Functional Connectivity

Dynamic functional connectivity analysis provides valuable information for
understanding brain functional activity underlying different cognitive
processes. Besides sliding window based approaches, a variety of methods have
been developed to automatically split the entire functional MRI scan into
segments by detecting change points of functional signals to facilitate better
characterization of temporally dynamic functional connectivity patterns.
However, these methods are based on certain assumptions for the functional
signals, such as Gaussian distribution, which are not necessarily suitable for
the fMRI data. In this study, we develop a deep learning based framework for
adaptively detecting temporally dynamic functional state transitions in a
data-driven way without any explicit modeling assumptions, by leveraging recent
advances in recurrent neural networks (RNNs) for sequence modeling.
Particularly, we solve this problem in an anomaly detection framework with an
assumption that the functional profile of one single time point could be
reliably predicted based on its preceding profiles within stable functional
state, while large prediction errors would occur around change points of
functional states. We evaluate the proposed method using both task and
resting-state fMRI data obtained from the human connectome project and
experimental results have demonstrated that the proposed change point detection
method could effectively identify change points between different task events
and split the resting-state fMRI into segments with distinct functional
connectivity patterns.
Identification of multi-scale hierarchical brain functional networks using deep matrix factorization
Authors: Hongming Li, Xiaofeng Zhu, Yong Fan
Date: 2018-09-14 00:00:00
Tasks: Functional Connectivity

We present a deep semi-nonnegative matrix factorization method for
identifying subject-specific functional networks (FNs) at multiple spatial
scales with a hierarchical organization from resting state fMRI data. Our
method is built upon a deep semi-nonnegative matrix factorization framework to
jointly detect the FNs at multiple scales with a hierarchical organization,
enhanced by group sparsity regularization that helps identify subject-specific
FNs without loss of inter-subject comparability. The proposed method has been
validated for predicting subject-specific functional activations based on
functional connectivity measures of the hierarchical multi-scale FNs of the
same subjects. Experimental results have demonstrated that our method could
obtain subject-specific multi-scale hierarchical FNs and their functional
connectivity measures across different scales could better predict
subject-specific functional activations than those obtained by alternative
techniques.
Non-Matrix Tactile Sensors: How Can Be Exploited Their Local Connectivity For Predicting Grasp Stability?
Authors: Brayan S. Zapata-Impata, Pablo Gil, Fernando Torres
Date: 2018-09-14 00:00:00

Tactile sensors supply useful information during the interaction with an
object that can be used for assessing the stability of a grasp. Most of the
previous works on this topic processed tactile readings as signals by
calculating hand-picked features. Some of them have processed these readings as
images calculating characteristics on matrix-like sensors. In this work, we
explore how non-matrix sensors (sensors with taxels not arranged exactly in a
matrix) can be processed as tactile images as well. In addition, we prove that
they can be used for predicting grasp stability by training a Convolutional
Neural Network (CNN) with them. We captured over 2500 real three-fingered
grasps on 41 everyday objects to train a CNN that exploited the local
connectivity inherent on the non-matrix tactile sensors, achieving 94.2%
F1-score on predicting stability.
Efficient Structured Surrogate Loss and Regularization in Structured Prediction
Authors: Heejin Choi
Date: 2018-09-14 00:00:00
Tasks: General Classification, Structured Prediction

In this dissertation, we focus on several important problems in structured
prediction. In structured prediction, the label has a rich intrinsic
substructure, and the loss varies with respect to the predicted label and the
true label pair. Structured SVM is an extension of binary SVM to adapt to such
structured tasks.
  In the first part of the dissertation, we study the surrogate losses and its
efficient methods. To minimize the empirical risk, a surrogate loss which upper
bounds the loss, is used as a proxy to minimize the actual loss. Since the
objective function is written in terms of the surrogate loss, the choice of the
surrogate loss is important, and the performance depends on it. Another issue
regarding the surrogate loss is the efficiency of the argmax label inference
for the surrogate loss. Efficient inference is necessary for the optimization
since it is often the most time-consuming step. We present a new class of
surrogate losses named bi-criteria surrogate loss, which is a generalization of
the popular surrogate losses. We first investigate an efficient method for a
slack rescaling formulation as a starting point utilizing decomposability of
the model. Then, we extend the algorithm to the bi-criteria surrogate loss,
which is very efficient and also shows performance improvements.
  In the second part of the dissertation, another important issue of
regularization is studied. Specifically, we investigate a problem of
regularization in hierarchical classification when a structural imbalance
exists in the label structure. We present a method to normalize the structure,
as well as a new norm, namely shared Frobenius norm. It is suitable for
hierarchical classification that adapts to the data in addition to the label
structure.
A Virtual Testbed for Critical Incident Investigation with Autonomous Remote Aerial Vehicle Surveying, Artificial Intelligence, and Decision Support
Authors: David L. Smyth, Sai Abinesh, Nazli B. Karimi, Brett Drury, Ihsan Ullah, Frank G. Glavin, Michael G. Madden
Date: 2018-09-14 00:00:00
Tasks: Anomaly Detection, Retrieval

Autonomous robotics and artificial intelligence techniques can be used to
support human personnel in the event of critical incidents. These incidents can
pose great danger to human life. Some examples of such assistance include:
multi-robot surveying of the scene; collection of sensor data and scene
imagery, real-time risk assessment and analysis; object identification and
anomaly detection; and retrieval of relevant supporting documentation such as
standard operating procedures (SOPs). These incidents, although often rare, can
involve chemical, biological, radiological/nuclear or explosive (CBRNE)
substances and can be of high consequence. Real-world training and deployment
of these systems can be costly and sometimes not feasible. For this reason, we
have developed a realistic 3D model of a CBRNE scenario to act as a testbed for
an initial set of assisting AI tools that we have developed.