pwc-archive/papers-with-abstracts huggingface dataset:
Graph Based Power Flow Calculation for Energy Management System
Date: 2018-10-25 00:00:00
Tasks: energy management, Management

Power flow calculation in EMS is required to accommodate a large and complex
power system. To achieve a faster than real-time calculation, a graph based
power flow calculation is proposed in this paper. Graph database and graph
computing advantages in power system calculations are presented. A linear
solver for power flow application is formulated and decomposed in nodal
parallelism and hierarchical parallelism to fully utilize graph parallel
computing capability. Comparison of the algorithm with traditional sequential
programs shows significant benefits on computation efficiency. Case studies on
practical large-scale systems provide supporting evidence that the new
algorithm is promising for online computing for EMS.
Surplus sharing with coherent utility functions
Date: 2018-10-28 00:00:00

We use the theory of coherent measures to look at the problem of surplus
sharing in an insurance business. The surplus share of an insured is calculated
by the surplus premium in the contract. The theory of coherent risk measures
and the resulting capital allocation gives a way to divide the surplus between
the insured and the capital providers, i.e. the shareholders.
Understanding the Role of Data-Centric Social Context in Personalized Mobile Applications
Authors: Sarker Iqbal H.
Date: 2018-10-15 00:00:00

Context-awareness in personalized mobile applications is a growing area of
study. Social context is one of the most important sources of information in
human-activity based applications. In this paper, we mainly focus on social
relational context that represents the interpersonal relationship between
individuals, and the role or influence of such context on users' diverse phone
call activities in their real world life. Individuals different phone call
activities such as making a phone call to a particular person or responding an
incoming call may differ from person-to-person based on their interpersonal
relationships such as family, friend, or colleague. However, it is very
difficult to make the device understandable about such semantic relationships
between individuals and the relevant context-aware applications. To address
this issue, in this paper, we explore the data-centric social relational
context that can play a significant role in building context-aware personalized
mobile applications for various purposes in our real world life.
Vehicle Tracking Using Surveillance with Multimodal Data Fusion
Authors: Zhang Yue, Song Bin, Du Xiaojiang, Guizani Mohsen
Date: 2018-10-29 00:00:00
Tasks: vehicle detection

Vehicle location prediction or vehicle tracking is a significant topic within
connected vehicles. This task, however, is difficult if only a single modal
data is available, probably causing bias and impeding the accuracy. With the
development of sensor networks in connected vehicles, multimodal data are
becoming accessible. Therefore, we propose a framework for vehicle tracking
with multimodal data fusion. Specifically, we fuse the results of two
modalities, images and velocity, in our vehicle-tracking task. Images, being
processed in the module of vehicle detection, provide direct information about
the features of vehicles, whereas velocity estimation can further evaluate the
possible location of the target vehicles, which reduces the number of features
being compared, and decreases the time consumption and computational cost.
Vehicle detection is designed with a color-faster R-CNN, which takes both the
shape and color of the vehicles into consideration. Meanwhile, velocity
estimation is through the Kalman filter, which is a classical method for
tracking. Finally, a multimodal data fusion method is applied to integrate
these outcomes so that vehicle-tracking tasks can be achieved. Experimental
results suggest the efficiency of our methods, which can track vehicles using a
series of surveillance cameras in urban areas.
Visual Attention is Beyond One Single Saliency Map
Authors: Li Jian
Date: 2018-10-23 00:00:00

Of later years, numerous bottom-up attention models have been proposed on
different assumptions. However, the produced saliency maps may be different
from each other even from the same input image. We also observe that human
fixation map varies across time greatly. When people freely view an image, they
tend to allocate attention at salient regions of large scale at first, and then
search more and more detailed regions. In this paper, we argue that, for one
input image visual attention cannot be described by only one single saliency
map, and this mechanism should be modeled as a dynamic process. Under the
frequency domain paradigm, we proposed a global inhibition model to mimic this
process by suppressing the {\it non-saliency} in the input image; we also show
that the dynamic process is influenced by one parameter in the frequency
domain. Experiments illustrate that the proposed model is capable of predicting
human dynamic fixation distribution.
A Volumetric Convolutional Neural Network for Brain Tumor Segmentation
Authors: Sherman Ryan
Date: 2018-10-27 00:00:00
Tasks: Brain Tumor Segmentation, Segmentation, Tumor Segmentation

Brain cancer can be very fatal, but chances of survival increase through
early detection and treatment. Doctors use Magnetic Resonance Imaging (MRI) to
detect and locate tumors in the brain, and very carefully analyze scans to
segment brain tumors. Manual segmentation is time consuming and tiring for
doctors, and it can be difficult for them to notice extremely small
abnormalities. Automated segmentations performed by computers offer quicker
diagnoses, the ability to notice small details, and more accurate
segmentations. Advances in deep learning and computer hardware have allowed for
high-performing automated segmentation approaches. However, several problems
persist in practice: increased training time, class imbalance, and low
performance. In this paper, I propose applying V-Net, a volumetric, fully
convolutional neural network, to segment brain tumors in MRI scans from the
BraTS Challenges. With this approach, I achieve a whole tumor dice score of
0.89 and train the network in a short time while addressing class imbalance
with the use of a dice loss layer. Then, I propose applying an existing
technique to improve automated segmentation performance in practice.
Quaternion Convolutional Neural Networks for Heterogeneous Image Processing
Authors: Parcollet Titouan, Morchid Mohamed, Linarès Georges
Date: 2018-10-31 00:00:00
Tasks: Decoder, Image Reconstruction

Convolutional neural networks (CNN) have recently achieved state-of-the-art
results in various applications. In the case of image recognition, an ideal
model has to learn independently of the training data, both local dependencies
between the three components (R,G,B) of a pixel, and the global relations
describing edges or shapes, making it efficient with small or heterogeneous
datasets. Quaternion-valued convolutional neural networks (QCNN) solved this
problematic by introducing multidimensional algebra to CNN. This paper proposes
to explore the fundamental reason of the success of QCNN over CNN, by
investigating the impact of the Hamilton product on a color image
reconstruction task performed from a gray-scale only training. By learning
independently both internal and external relations and with less parameters
than real valued convolutional encoder-decoder (CAE), quaternion convolutional
encoder-decoders (QCAE) perfectly reconstructed unseen color images while CAE
produced worst and gray-scale versions.
When Not to Classify: Detection of Reverse Engineering Attacks on DNN Image Classifiers
Authors: Wang Yujia, Miller David J., Kesidis George
Date: 2018-10-31 00:00:00

This paper addresses detection of a reverse engineering (RE) attack targeting
a deep neural network (DNN) image classifier; by querying, RE's aim is to
discover the classifier's decision rule. RE can enable test-time evasion
attacks, which require knowledge of the classifier. Recently, we proposed a
quite effective approach (ADA) to detect test-time evasion attacks. In this
paper, we extend ADA to detect RE attacks (ADA-RE). We demonstrate our method
is successful in detecting "stealthy" RE attacks before they learn enough to
launch effective test-time evasion attacks.
MAMMO: A Deep Learning Solution for Facilitating Radiologist-Machine Collaboration in Breast Cancer Diagnosis
Authors: Kyono Trent, Gilbert Fiona J., van der Schaar Mihaela
Date: 2018-10-30 00:00:00
Tasks: Diagnostic, Multi-Task Learning

With an aging and growing population, the number of women requiring either
screening or symptomatic mammograms is increasing. To reduce the number of
mammograms that need to be read by a radiologist while keeping the diagnostic
accuracy the same or better than current clinical practice, we develop Man and
Machine Mammography Oracle (MAMMO) - a clinical decision support system capable
of triaging mammograms into those that can be confidently classified by a
machine and those that cannot be, thus requiring the reading of a radiologist.
The first component of MAMMO is a novel multi-view convolutional neural network
(CNN) with multi-task learning (MTL). MTL enables the CNN to learn the
radiological assessments known to be associated with cancer, such as breast
density, conspicuity, suspicion, etc., in addition to learning the primary task
of cancer diagnosis. We show that MTL has two advantages: 1) learning refined
feature representations associated with cancer improves the classification
performance of the diagnosis task and 2) issuing radiological assessments
provides an additional layer of model interpretability that a radiologist can
use to debug and scrutinize the diagnoses provided by the CNN. The second
component of MAMMO is a triage network, which takes as input the radiological
assessment and diagnostic predictions of the first network's MTL outputs and
determines which mammograms can be correctly and confidently diagnosed by the
CNN and which mammograms cannot, thus needing to be read by a radiologist.
Results obtained on a private dataset of 8,162 patients show that MAMMO reduced
the number of radiologist readings by 42.8% while improving the overall
diagnostic accuracy in comparison to readings done by radiologists alone. We
analyze the triage of patients decided by MAMMO to gain a better understanding
of what unique mammogram characteristics require radiologists' expertise.
Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning
Authors: Achi Hanadi El, Belousova Tatiana, Chen Lei, Wahed Amer, Wang Iris, Hu Zhihong, Kanaan Zeyad, Rios Adan, Nguyen Andy N. D.
Date: 2018-10-30 00:00:00
Tasks: Deep Learning, Diagnostic, Prediction, whole slide images

Recent studies have shown promising results in using Deep Learning to detect
malignancy in whole slide imaging. However, they were limited to just
predicting positive or negative finding for a specific neoplasm. We attempted
to use Deep Learning with a convolutional neural network algorithm to build a
lymphoma diagnostic model for four diagnostic categories: benign lymph node,
diffuse large B cell lymphoma, Burkitt lymphoma, and small lymphocytic
lymphoma. Our software was written in Python language. We obtained digital
whole slide images of Hematoxylin and Eosin stained slides of 128 cases
including 32 cases for each diagnostic category. Four sets of 5 representative
images, 40x40 pixels in dimension, were taken for each case. A total of 2,560
images were obtained from which 1,856 were used for training, 464 for
validation and 240 for testing. For each test set of 5 images, the predicted
diagnosis was combined from prediction of 5 images. The test results showed
excellent diagnostic accuracy at 95% for image-by-image prediction and at 10%
for set-by-set prediction. This preliminary study provided a proof of concept
for incorporating automated lymphoma diagnostic screen into future pathology
workflow to augment the pathologists' productivity.
Attention-Mechanism-based Tracking Method for Intelligent Internet of Vehicles
Authors: Kang Xu, Song Bin, Guo Jie, Du Xiaojiang, Guizani Mohsen
Date: 2018-10-29 00:00:00

Vehicle tracking task plays an important role on the internet of vehicles and
intelligent transportation system. Beyond the traditional GPS sensor, the image
sensor can capture different kinds of vehicles, analyze their driving situation
and can interact with them. Aiming at the problem that the traditional
convolutional neural network is vulnerable to background interference, this
paper proposes vehicle tracking method based on human attention mechanism for
self-selection of deep features with an inter-channel fully connected layer. It
mainly includes the following contents: 1) A fully convolutional neural network
fused attention mechanism with the selection of the deep features for
convolution. 2) A separation method for template and semantic background region
to separate target vehicles from the background in the initial frame
adaptively. 3) A two-stage method for model training using our traffic dataset.
The experimental results show that the proposed method improves the tracking
accuracy without an increase in tracking time. Meanwhile, it strengthens the
robustness of algorithm under the condition of the complex background region.
The success rate of the proposed method in overall traffic datasets is higher
than Siamese network by about 10 percent, and the overall precision is higher
than Siamese network by 8 percent.
Receiver Design for Faster-than-Nyquist Signaling: Deep-learning-based Architectures
Authors: Peiyang Song, Fengkui Gong, Qiang Li, Guo Li, Haiyang Ding
Date: 2018-11-07 00:00:00

Faster-than-Nyquist (FTN) is a promising paradigm to improve bandwidth utilization at the expense of additional intersymbol interference (ISI). In this paper, we apply state-of-the-art deep learning (DL) technology into receiver design for FTN signaling and propose two DL-based new architectures. Firstly, we propose an FTN signal detection based on DL and connect it with the successive interference cancellation (SIC) to replace traditional detection algorithms. Simulation results show that this architecture can achieve near-optimal performance in both uncoded and coded scenarios. Additionally, we propose a DL-based joint signal detection and decoding for FTN signaling to replace the complete baseband part in traditional FTN receivers. The performance of this new architecture has also been illustrated by simulation results. Finally, both the proposed DL-based receiver architecture has the robustness to signal to noise ratio (SNR). In a nutshell, DL has been proved to be a powerful tool for the FTN receiver design.
Uploading Brain into Computer: Whom to Upload First?
Authors: Feygin Yana B., Morris Kelly, Yampolskiy Roman V.
Date: 2018-10-27 00:00:00

The final goal of the intelligence augmentation process is a complete merger
of biological brains and computers allowing for integration and mutual
enhancement between computer's speed and memory and human's intelligence. This
process, known as uploading, analyzes human brain in detail sufficient to
understand its working patterns and makes it possible to simulate said brain on
a computer. As it is likely that such simulations would quickly evolve or be
modified to achieve superintelligence it is very important to make sure that
the first brain chosen for such a procedure is a suitable one. In this paper,
we attempt to answer the question: Whom to upload first?
Beyond the Leaderboard: Insight and Deployment Challenges to Address Research Problems
Authors: Mendrik Adrienne M., Aylward Stephen R.
Date: 2018-10-26 00:00:00
Tasks: Medical Image Analysis

In the medical image analysis field, organizing challenges with associated
workshops at international conferences began in 2007 and has grown to include
over 150 challenges. Several of these challenges have had a major impact in the
field. However, whereas well-designed challenges have the potential to unite
and focus the field on creating solutions to important problems, poorly
designed and documented challenges can equally impede a field and lead to
pursuing incremental improvements in metric scores with no theoretic or
clinical significance. This is supported by a critical assessment of challenges
at the international MICCAI conference. In this assessment the main observation
was that small changes to the underlying challenge data can drastically change
the ranking order on the leaderboard. Related to this is the practice of
leaderboard climbing, which is characterized by participants focusing on
incrementally improving metric results rather than advancing science or solving
the driving problem of a challenge. In this abstract we look beyond the
leaderboard of a challenge and instead look at the conclusions that can be
drawn from a challenge with respect to the research problem that it is
addressing. Research study design is well described in other research areas and
can be translated to challenge design when viewing challenges as research
studies on algorithm performance that address a research problem. Based on the
two main types of scientific research study design, we propose two main
challenge types, which we think would benefit other research areas as well: 1)
an insight challenge that is based on a qualitative study design and 2) a
deployment challenge that is based on a quantitative study design. In addition
we briefly touch upon related considerations with respect to statistical
significance versus practical significance, generalizability and data
saturation.
On the Three Properties of Stationary Populations and knotting with Non-Stationary Populations
Date: 2019-07-19 00:00:00

A population is considered stationary if the growth rate is zero and the age
structure is constant. It thus follows that a population is considered
non-stationary if either its growth rate is non-zero and/or its age structure
is non-constant. We propose three properties that are related to the stationary
population identity (SPI) of population biology by connecting it with
stationary populations and non-stationary populations which are approaching
stationarity. One of these important properties is that SPI can be applied to
partition a population into stationary and non-stationary components. These
properties provide deeper insights into cohort formation in real-world
populations and the length of the duration for which stationary and
non-stationary conditions hold. The new concepts are based on the time gap
between the occurrence of stationary and non-stationary populations within the
SPI framework that we refer to as Oscillatory SPI and the Amplitude of SPI.
This article will appear in Bulletin of Mathematical Biology (Springer)
Reframing the S\&P500 Network of Stocks along the \nth{21} Century
Date: 2018-12-16 00:00:00

Since the beginning of the new millennium, stock markets went through every
state from long-time troughs, trade suspensions to all-time highs. The
literature on asset pricing hence assumes random processes to be underlying the
movement of stock returns. Observed procyclicality and time-varying correlation
of stock returns tried to give the apparently random behavior some sort of
structure. However, common misperceptions about the co-movement of asset prices
in the years preceding the \emph{Great Recession} and the \emph{Global
Commodity Crisis}, is said to have even fueled the crisis' economic impact.
Here we show how a varying macroeconomic environment influences stocks'
clustering into communities. From a sample of 296 stocks of the S\&P 500 index,
distinct periods in between 2004 and 2011 are used to develop networks of
stocks. The Minimal Spanning Tree analysis of those time-varying networks of
stocks demonstrates that the crises of 2007-2008 and 2010-2011 drove the market
to clustered community structures in both periods, helping to restore the stock
market's ceased order of the pre-crises era. However, a comparison of the
emergent clusters with the \textit{General Industry Classification Standard}
conveys the impression that industry sectors do not play a major role in that
order.
Kinetic Euclidean Distance Matrices
Date: 2019-03-15 00:00:00

Euclidean distance matrices (EDMs) are a major tool for localization from
distances, with applications ranging from protein structure determination to
global positioning and manifold learning. They are, however, static objects
which serve to localize points from a snapshot of distances. If the objects
move, one expects to do better by modeling the motion. In this paper, we
introduce Kinetic Euclidean Distance Matrices (KEDMs)---a new kind of
time-dependent distance matrices that incorporate motion. The entries of KEDMs
become functions of time, the squared time-varying distances. We study two
smooth trajectory models---polynomial and bandlimited trajectories---and show
that these trajectories can be reconstructed from incomplete, noisy distance
observations, scattered over multiple time instants. Our main contribution is a
semidefinite relaxation (SDR), inspired by SDRs for static EDMs. Similarly to
the static case, the SDR is followed by a spectral factorization step; however,
because spectral factorization of polynomial matrices is more challenging than
for constant matrices, we propose a new factorization method that uses anchor
measurements. Extensive numerical experiments show that KEDMs and the new
semidefinite relaxation accurately reconstruct trajectories from noisy,
incomplete distance data and that, in fact, motion improves rather than
degrades localization if properly modeled. This makes KEDMs a promising tool
for problems in geometry of dynamic points sets.
Reconstruction-Cognizant Graph Sampling using Gershgorin Disc Alignment
Date: 2019-02-16 00:00:00
Tasks: Graph Sampling

Graph sampling with noise is a fundamental problem in graph signal processing
(GSP). Previous works assume an unbiased least square (LS) signal
reconstruction scheme and select samples greedily via expensive extreme
eigenvector computation. A popular biased scheme using graph Laplacian
regularization (GLR) solves a system of linear equations for its
reconstruction. Assuming this GLR-based scheme, we propose a
reconstruction-cognizant sampling strategy to maximize the numerical stability
of the linear system---\textit{i.e.}, minimize the condition number of the
coefficient matrix. Specifically, we maximize the eigenvalue lower bounds of
the matrix, represented by left-ends of Gershgorin discs of the coefficient
matrix. To accomplish this efficiently, we propose an iterative algorithm to
traverse the graph nodes via Breadth First Search (BFS) and align the left-ends
of all corresponding Gershgorin discs at lower-bound threshold $T$ using two
basic operations: disc shifting and scaling. We then perform binary search to
maximize $T$ given a sample budget $K$. Experiments on real graph data show
that the proposed algorithm can effectively promote large eigenvalue lower
bounds, and the reconstruction MSE is the same or smaller than existing
sampling methods for different budget $K$ at much lower complexity.
Nonparametric maximum likelihood methods for binary response models with random coefficients
Date: 2020-01-14 00:00:00

Single index linear models for binary response with random coefficients have
been extensively employed in many econometric settings under various parametric
specifications of the distribution of the random coefficients. Nonparametric
maximum likelihood estimation (NPMLE) as proposed by Cosslett (1983) and
Ichimura and Thompson (1998), in contrast, has received less attention in
applied work due primarily to computational difficulties. We propose a new
approach to computation of NPMLEs for binary response models that significantly
increase their computational tractability thereby facilitating greater
flexibility in applications. Our approach, which relies on recent developments
involving the geometry of hyperplane arrangements, is contrasted with the
recently proposed deconvolution method of Gautier and Kitamura (2013). An
application to modal choice for the journey to work in the Washington DC area
illustrates the methods.