pwc-archive/papers-with-abstracts huggingface dataset:
Reading Industrial Inspection Sheets by Inferring Visual Relations
Authors: Rahul Rohit, Chowdhury Arindam, Animesh, Mittal Samarth, Vig Lovekesh
Date: 2018-12-11 00:00:00
Tasks: Cloud Removal, Text Detection

The traditional mode of recording faults in heavy factory equipment has been
via hand marked inspection sheets, wherein a machine engineer manually marks
the faulty machine regions on a paper outline of the machine. Over the years,
millions of such inspection sheets have been recorded and the data within these
sheets has remained inaccessible. However, with industries going digital and
waking up to the potential value of fault data for machine health monitoring,
there is an increased impetus towards digitization of these hand marked
inspection records. To target this digitization, we propose a novel visual
pipeline combining state of the art deep learning models, with domain knowledge
and low level vision techniques, followed by inference of visual relationships.
Our framework is robust to the presence of both static and non-static
background in the document, variability in the machine template diagrams,
unstructured shape of graphical objects to be identified and variability in the
strokes of handwritten text. The proposed pipeline incorporates a capsule and
spatial transformer network based classifier for accurate text reading, and a
customized CTPN network for text detection in addition to hybrid techniques for
arrow detection and dialogue cloud removal. We have tested our approach on a
real world dataset of 50 inspection sheets for large containers and boilers.
The results are visually appealing and the pipeline achieved an accuracy of
87.1% for text detection and 94.6% for text reading.
Towards Ophthalmologist Level Accurate Deep Learning System for OCT Screening and Diagnosis
Authors: Haloi Mrinal
Date: 2018-12-12 00:00:00
Tasks: Transfer Learning

In this work, we propose an advanced AI based grading system for OCT images.
The proposed system is a very deep fully convolutional attentive classification
network trained with end to end advanced transfer learning with online random
augmentation. It uses quasi random augmentation that outputs confidence values
for diseases prevalence during inference. Its a fully automated retinal OCT
analysis AI system capable of pathological lesions understanding without any
offline preprocessing/postprocessing step or manual feature extraction. We
present a state of the art performance on the publicly available Mendeley OCT
dataset.
Stochastic Modelling and Dynamic Analysis of Cardiovascular System with Rotary Left Ventricular Assist Devices
Date: 2018-12-18 00:00:00

The left ventricular assist device (LVAD) has been used for end-stage heart
failure patients as a therapeutic option. The aortic valve plays a critical
role in heart failure and its treatment with LVAD. The cardiovascular-LVAD
model is often used to investigate the physiological demands required by
patients and predict the hemodynamic of the native heart supported with a LVAD.
As a bridge to recovery treatment, it is important to maintain appropriate and
active dynamics of the aortic valve and the cardiac output of the native heart,
which requires that the LVAD pump must be adjusted so that a proper balance
between the blood contributed through the aortic valve and the pump is
maintained. In this paper, our objective is to identify a critical value of the
pump power to ensure that the LVAD pump does not take over the pumping function
in the cardiovascular-pump system and share the ejected blood with left
ventricle to help the heart to recover. In addition, hemodynamic often involves
variability due to patients heterogeneity and the stochastic nature of
cardiovascular system. The variability poses significant challenges to
understand dynamic behaviors of the aortic valve and cardiac output. A
generalized polynomial chaos (gPC) expansion is used in this work to develop a
stochastic cardiovascular-pump model for efficient uncertainty propagation,
from which it is possible to rapidly calculate the variance in the aortic valve
opening duration and the cardiac output in the presence of variability. The
simulation results show that the gPC based cardiovascular-pump model is a
reliable platform that can provide useful information to understand the effect
of LVAD pump on the hemodynamic of the heart.
Music and musical sonification for the rehabilitation of Parkinsonian dysgraphia: Conceptual framework
Date: 2018-12-18 00:00:00
Tasks: Rhythm

Music has been shown to enhance motor control in patients with Parkinson's
disease (PD). Notably, musical rhythm is perceived as an external auditory cue
that helps PD patients to better control movements. The rationale of such
effects is that motor control based on auditory guidance would activate a
compensatory brain network that minimizes the recruitment of the defective
pathway involving the basal ganglia. Would associating music to movement
improve its perception and control in PD? Musical sonification consists in
modifying in real-time the playback of a preselected music according to some
movement parameters. The validation of such a method is underway for
handwriting in PD patients. When confirmed, this study will strengthen the
clinical interest of musical sonification in motor control and (re)learning in
PD.
COGEVIS: A New Scale to Evaluate Cognition in Patients with Visual Deficiency
Date: 2018-12-18 00:00:00
Tasks: Specificity

We evaluated the cognitive status of visually impaired patients referred to
low vision rehabilitation (LVR) based on a standard cognitive battery and a new
evaluation tool, named the COGEVIS, which can be used to assess patients with
severe visual deficits. We studied patients aged 60 and above, referred to the
LVR Hospital in Paris. Neurological and cognitive evaluations were performed in
an expert memory center. Thirty-eight individuals, 17 women and 21 men with a
mean age of 70.3 $\pm$ 1.3 years and a mean visual acuity of 0.12 $\pm$ 0.02,
were recruited over a one-year period. Sixty-three percent of participants had
normal cognitive status. Cognitive impairment was diagnosed in 37.5% of
participants. The COGEVIS score cutoff point to screen for cognitive impairment
was 24 (maximum score of 30) with a sensitivity of 66.7% and a specificity of
95%. Evaluation following 4 months of visual rehabilitation showed an
improvement of Instrumental Activities of Daily Living (p = 0 004), National
Eye Institute Visual Functioning Questionnaire (p = 0 035), and
Montgomery-{\AA}sberg Depression Rating Scale (p = 0 037). This study
introduces a new short test to screen for cognitive impairment in visually
impaired patients.
Emergence of stylized facts during the opening of stock markets
Date: 2018-12-18 00:00:00

Financial markets show a number of non-stationarities, ranging from
volatility fluctuations over ever changing technical and regulatory market
conditions to seasonalities. On the other hand, financial markets show various
stylized facts which are remarkably stable. It is thus an intriguing question
to find out how these stylized facts emerge. As a first example, we here
investigate how the bid-ask-spread between best sell and best buy offer for
stocks develops during the trading day. For rescaled and properly smoothed data
we observe collapsing curves for many different NASDAQ stocks, with a slow
power law decline of the spread during the whole trading day. This effect
emerges robustly after a highly fluctuating opening period. Some so called
large-tick stocks behave differently because of technical boundaries. Their
spread closes to one tick shortly after the market opening. We use our findings
for identifying the duration of the market opening which we find to vary
largely from stock to stock.
Change of Measure in Midcurve Pricing
Authors: K. E. Feldman
Date: 2018-12-10 00:00:00

We derive measure change formulae required to price midcurve swaptions in the forward swap annuity measure with stochastic annuities' ratios. We construct the corresponding linear and exponential terminal swap rate pricing models and show how they capture the midcurve swaption correlation skew.
ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention
Date: 2019-02-15 00:00:00
Proceeding: arXiv:1812.07422 2018 12
Tasks: Atrial Fibrillation Detection, Specificity

The complexity of the patterns associated with Atrial Fibrillation (AF) and
the high level of noise affecting these patterns have significantly limited the
current signal processing and shallow machine learning approaches to get
accurate AF detection results. Deep neural networks have shown to be very
powerful to learn the non-linear patterns in the data. While a deep learning
approach attempts to learn complex pattern related to the presence of AF in the
ECG, they can benefit from knowing which parts of the signal is more important
to focus during learning. In this paper, we introduce a two-channel deep neural
network to more accurately detect AF presented in the ECG signal. The first
channel takes in a preprocessed ECG signal and automatically learns where to
attend for detection of AF. The second channel simultaneously takes in the
preprocessed ECG signal to consider all features of entire signals. The model
shows via visualization that what parts of the given ECG signal are important
to attend while trying to detect atrial fibrillation. In addition, this
combination significantly improves the performance of the atrial fibrillation
detection (achieved a sensitivity of 99.53%, specificity of 99.26% and accuracy
of 99.40% on the MIT-BIH atrial fibrillation database with 5-s ECG segments.)
Substrate inhibition imposes fitness penalty at high protein stability
Date: 2018-12-18 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Proteins are only moderately stable. It has long been debated whether this
narrow range of stabilities is solely a result of neutral drift towards lower
stability or purifying selection against excess stability is also at work - for
which no experimental evidence was found so far. Here we show that mutations
outside the active site in the essential E. coli enzyme adenylate kinase result
in stability-dependent increase in substrate inhibition by AMP, thereby
impairing overall enzyme activity at high stability. Such inhibition caused
substantial fitness defects not only in the presence of excess substrate but
also under physiological conditions. In the latter case, substrate inhibition
caused differential accumulation of AMP in the stationary phase for the
inhibition prone mutants. Further, we show that changes in flux through Adk
could accurately describe the variation in fitness effects. Taken together,
these data suggest that selection against substrate inhibition and hence excess
stability may have resulted in a narrow range of optimal stability observed for
modern proteins.
Review of studies in field of the effects of nanotechnology on breast cancer
Date: 2018-11-20 00:00:00
Tasks: Articles, Descriptive

Cancer is the main cause of mortality at the developed countries and is the
second cause of mortality at the developing countries and breast cancer is the
most prevalent malignancy and the first cause of mortality among women of the
world. At the US, 28% of new cancer cases and 15% of mortality caused by that
in 2010 was caused by breast cancer. Moreover, breast cancer is the most common
cancer among Iranian women, which includes 24.4% of overall malignancies and
causes 3.3% of mortalities per 1.000 people. The disease is most spread in
Tehran and including 25.5% of all cancers. According to the mentioned, the
study has tried to assess the effect of nanotechnology in form of a review to
diagnose and treat breast cancer. The method applied in this study is
descriptive analytical method and library method has been used for data
collection purpose. In the data collection method, 45 articles relevant to
structure of types of nanoparticles and their uses in diagnosis, imaging and
medicine delivery systems and breast cancer treatment have been used. Although
there are still some challenges and limitations to use nanoparticles in
medication, it is hope that nanoparticles can make wonderful revolution not
only in oncology, but also in medication in near future. The most underlying
headlines of the present study include mineral nanoparticles, antibodies and
tumor imaging methods. Keywords: magnetic nanoparticles, breast cancer,
nanobody.
GPR-based Detection of Voids and Evaluation of Grouting Under Semi-rigid Basement
Date: 2018-11-21 00:00:00
Tasks: GPR

The void underneath semi-rigid base is a common defect in roads. There are
some difficulties in the detection and repair for this kind of hidden damage,
as well as in the evaluation of the effects of grouting treatment. For the
detection and maintenance of roads, it is essential to study the detection and
judging for voids underneath base and the evaluation of the spread of grout.
Through theoretical analysis, numerical simulation and analysis of real data,
this research generated the characteristics of under-base voids of different
types and dimensions on GPR images, proposed the detecting and
dimension-measuring methods for under-base voids, and studied the process and
effects of data analysis techniques.
  (1) The characteristics of under-base voids of different types (air-filled,
water-filled or grout-treated) and dimensions (height and horizontal
dimensions), on A-scan and B-scan GPR image respectively, were analyzed
theoretically.
  (2) Approaches for detecting voids and for estimating its height were
studied, focusing on voids with a height ranging from 0.01m to 0.3m.
  (3) The approach for estimating the horizontal dimension of voids was
studied, focusing on voids with a length ranging from 0.04m to 0.52m.
  (4) The data processing process was discussed. Also, the effects of different
data processing techniques were studied in terms of noise filtering and
attenuation compensation, and their influence on the image characteristics was
also discussed.
Class Augmented Semi-Supervised Learning for Practical Clinical Analytics on Physiological Signals
Authors: Ukil Arijit, Bandyopadhyay Soma, Puri Chetanya, Singh Rituraj, Pal Arpan
Date: 2018-11-29 00:00:00
Tasks: Anomaly Detection

Computational analysis on physiological signals would provide immense impact
for enabling automated clinical analytics. However, the class imbalance issue
where negative or minority class instances are rare in number impairs the
robustness of the practical solution. The key idea of our approach is
intelligent augmentation of minority class examples to construct smooth,
unbiased decision boundary for robust semi-supervised learning. This solves the
practical class imbalance problem in anomaly detection task for computational
clinical analytics using physiological signals. We choose two critical cardiac
marker physiological signals: Heart sound or Phonocardiogram (PCG) and
Electrocardiogram (ECG) to validate our claim of robust anomaly detection of
clinical events under augmented class learning, where intelligent synthesis of
minority class instances attempt to balance the class distribution. We perform
extensive experiments on publicly available expert-labelled MIT-Physionet PCG
and ECG datasets that establish high performance merit of the proposed scheme,
and our scheme fittingly performs better than the state-of-the-art algorithms.
Experience: Understanding Long-Term Evolving Patterns of Shared Electric Vehicle Networks
Date: 2019-04-21 00:00:00
Tasks: Autonomous Vehicles

Due to the ever-growing concerns on air pollution and energy security, many
cities have started to update their taxi fleets with electric ones. Although
environmentally friendly, the rapid promotion of electric taxis raises problems
to both taxi drivers and governments, e.g., prolonged waiting/charging time,
unbalanced utilization of charging infrastructures and reduced taxi supply due
to the long charging time. In this paper, we make the first effort to
understand the long-term evolving patterns through a five-year study on one of
the largest electric taxi networks in the world, i.e., the Shenzhen electric
taxi network in China. In particular, we perform a comprehensive measurement
investigation called ePat to explore the evolving mobility and charging
patterns of electric vehicles. Our ePat is based on 4.8 TB taxi GPS data, 240
GB taxi transaction data, and metadata from 117 charging stations, during an
evolving process from 427 electric taxis in 2013 to 13,178 in 2018. Moreover,
ePat also explores the impacts of various contexts and benefits during the
evolving process. Our ePat as a comprehensive investigation of the electric
taxi network mobility and charging evolving has the potential to advance the
understanding of the evolving patterns of electric taxi networks and pave the
way for analyzing future shared autonomous vehicles.
A Hardware-Efficient Hybrid Beamforming Solution for mmWave MIMO Systems
Date: 2018-12-12 00:00:00
Tasks: POS

In millimeter wave (mmWave) communication systems, existing hybrid
beamforming solutions generally require a large number of high-resolution phase
shifters (PSs) to realize analog beamformers, which still suffer from high
hardware complexity and power consumption. Targeting at this problem, this
article introduces a novel hardware-efficient hybrid precoding/combining
architecture, which only employs a limited number of simple phase over-samplers
(POSs) and a switch (SW) network to achieve maximum hardware efficiency while
maintaining satisfactory spectral efficiency performance. The POS can be
realized by a simple circuit and simultaneously outputs several parallel
signals with different phases. With the aid of a simple switch network, the
analog precoder/combiner is implemented by feeding the signals with appropriate
phases to antenna arrays or RF chains. We analyze the design challenges of this
POS-SW-based hybrid beamforming architecture and present potential solutions to
the fundamental issues, especially the precoder/combiner design and the channel
estimation strategy. Simulation results demonstrate that this
hardware-efficient structure can achieve comparable spectral efficiency but
much higher energy efficiency than that of the traditional structures.
Correlated Non-Linear Phase Noise in Multi-Subcarrier Systems
Date: 2018-12-13 00:00:00

We explore correlated nonlinear phase noise (NLPN) in multi-subcarrier
systems. We derive an analytical model for predicting the covariance between
the NLPN affecting different subcarriers, and offer a simple algorithm which
uses the correlations for improved NLPN mitigation.
Average Age of Information in Wireless Powered Sensor Networks
Date: 2018-12-16 00:00:00
Tasks: energy management, Management

In this letter, we deal with the age of information (AoI) for a sensor
network with wireless power transfer (WPT) capabilities. Specifically, we study
a simple network topology, where a sensor node harvests energy from radio
frequency signals (transmitted by a dedicated energy source) to transmit
real-time status updates. The sensor node generates an update when its
capacitor/battery becomes fully charged and transmits by using all the
available energy without further energy management. The average AoI performance
of the considered greedy policy is derived in closed form and is a function of
the capacitor's size. The optimal value of the capacitor that maximizes the
freshness of the information, corresponds to a simple optimization problem
requiring a one-dimensional search. The derived theoretical results provide
useful performance bounds for practical WPT networks.
Joint Rate and Resource Allocation in Hybrid Digital-Analog Transmission over Fading Channels
Date: 2018-12-17 00:00:00

In hybrid digital-analog (HDA) systems, resource allocation has been utilized
to achieve desired distortion performance. However, existing studies on this
issue assume error-free digital transmission, which is not valid for fading
channels. With time-varying channel fading, the exact channel state information
is not available at the transmitter. Thus, random outage and resulting digital
distortion cannot be ignored. Moreover, rate allocation should be considered in
resource allocation, since it not only determines the amount of information for
digital transmission and that for analog transmission, but also affects the
outage probability. Based on above observations, in this paper, we attempt to
perform joint rate and resource allocation strategies to optimize system
distortion in HDA systems over fading channels. Consider a bandwidth expansion
scenario where a memoryless Gaussian source is transmitted in an HDA system
with the entropy-constrained scalar quantizer (ECSQ). Firstly, we formulate the
joint allocation problem as an expected system distortion minimization problem
where both analog and digital distortion are considered. Then, in the limit of
low outage probability, we decompose the problem into two coupled sub-problems
based on the block coordinate descent method, and propose an iterative gradient
algorithm to approach the optimal solution. Furthermore, we extend our work to
the multivariate Gaussian source scenario where a two-stage fast algorithm
integrating rounding and greedy strategies is proposed to optimize the joint
rate and resource allocation problem. Finally, simulation results demonstrate
that the proposed algorithms can achieve up to 2.3dB gains in terms of
signal-to-distortion ratio over existing schemes under the single Gaussian
source scenario, and up to 3.5dB gains under the multivariate Gaussian source
scenario.
On pricing rules and optimal strategies in general Kyle-Back models
Authors: Umut Çetin, Albina Danilova
Date: 2018-12-18 00:00:00

The folk result in Kyle-Back models states that the value function of the insider remains unchanged when her admissible strategies are restricted to absolutely continuous ones. In this paper we show that, for a large class of pricing rules used in current literature, the value function of the insider can be finite when her strategies are restricted to be absolutely continuous and infinite when this restriction is not imposed. This implies that the folk result doesn't hold for those pricing rules and that they are not consistent with equilibrium. We derive the necessary conditions for a pricing rule to be consistent with equilibrium and prove that, when a pricing rule satisfies these necessary conditions, the insider's optimal strategy is absolutely continuous, thus obtaining the classical result in a more general setting. This, furthermore, allows us to justify the standard assumption of absolute continuity of insider's strategies since one can construct a pricing rule satisfying the necessary conditions derived in the paper that yield the same price process as the pricing rules employed in the modern literature when insider's strategies are absolutely continuous.