pwc-archive/papers-with-abstracts huggingface dataset:
ConvPath: A Software Tool for Lung Adenocarcinoma Digital Pathological Image Analysis Aided by Convolutional Neural Network
Authors: Wang Shidan, Wang Tao, Yang Lin, Yi Faliu, Luo Xin, Yang Yikun, Gazdar Adi, Fujimoto Junya, Wistuba Ignacio I., Yao Bo, Lin ShinYi, Xie Yang, Mao Yousheng, Xiao Guanghua
Date: 2018-09-20 00:00:00
Tasks: Classification, General Classification

The spatial distributions of different types of cells could reveal a cancer
cell growth pattern, its relationships with the tumor microenvironment and the
immune response of the body, all of which represent key hallmarks of cancer.
However, manually recognizing and localizing all the cells in pathology slides
are almost impossible. In this study, we developed an automated cell type
classification pipeline, ConvPath, which includes nuclei segmentation,
convolutional neural network-based tumor, stromal and lymphocytes
classification, and extraction of tumor microenvironment related features for
lung cancer pathology images. The overall classification accuracy is 92.9% and
90.1% in training and independent testing datasets, respectively. By
identifying cells and classifying cell types, this pipeline can convert a
pathology image into a spatial map of tumor, stromal and lymphocyte cells. From
this spatial map, we can extracted features that characterize the tumor
micro-environment. Based on these features, we developed an image feature-based
prognostic model and validated the model in two independent cohorts. The
predicted risk group serves as an independent prognostic factor, after
adjusting for clinical variables that include age, gender, smoking status, and
stage.
On Carr and Lee's correlation immunization strategy
Date: 2019-03-18 00:00:00

In their seminal work Carr and Lee (2008) show how to robustly price and
replicate a variety of claims written on the quadratic variation of a risky
asset under the assumption that the asset's volatility process is independent
of the Brownian motion that drives the asset's price. Additionally, they
propose a correlation immunization strategy that minimizes the pricing and
hedging error that results when the correlation between the risky asset's price
and volatility is nonzero. In this paper, we show that the correlation
immunization strategy is the only strategy among the class of strategies
discussed in Carr and Lee (2008) that results in real-valued hedging portfolios
when the correlation between the asset's price and volatility is nonzero.
Additionally, we perform a number of Monte Carlo experiments to test the
effectiveness of Carr and Lee's immunization strategy. Our results indicate
that the correlation immunization method is an effective means of reducing
pricing and hedging errors that result from nonzero correlation.
A natural upper bound to the accuracy of predicting protein stability changes upon mutations
Date: 2018-09-27 00:00:00
Tasks: Protein Design

Accurate prediction of protein stability changes upon single-site variations
(DDG) is important for protein design, as well as our understanding of the
mechanism of genetic diseases. The performance of high-throughput computational
methods to this end is evaluated mostly based on the Pearson correlation
coefficient between predicted and observed data, assuming that the upper bound
would be 1 (perfect correlation). However, the performance of these predictors
can be limited by the distribution and noise of the experimental data. Here we
estimate, for the first time, a theoretical upper-bound to the DDG prediction
performances imposed by the intrinsic structure of currently available DDG
data. Given a set of measured DDG protein variations, the theoretically best
predictor is estimated based on its similarity to another set of experimentally
determined DDG values. We investigate the correlation between pairs of measured
DDG variations, where one is used as a predictor for the other. We analytically
derive an upper bound to the Pearson correlation as a function of the noise and
distribution of the DDG data. We also evaluate the available datasets to
highlight the effect of the noise in conjunction with DDG distribution. We
conclude that the upper bound is a function of both uncertainty and spread of
the DDG values, and that with current data the best performance should be
between 0.7-0.8, depending on the dataset used; higher Pearson correlations
might be indicative of overtraining. It also follows that comparisons of
predictors using different datasets are inherently misleading.
Sequence Block based Compressed Sensing Multiuser Detection for 5G
Date: 2018-09-28 00:00:00
Tasks: Action Detection, Activity Detection, compressed sensing

Compressed sensing based multiuser detection (CSMUD) is a promising candidate
to cope with the massive connectivity requirements of the massive machine type
communication (mMTC) in the fifth generation (5G) wireless communication
system. It facilitates grant-free non-orthogonal code division multiple access
(CDMA) to accommodate massive number of IoT devices. In non-orthogonal CDMA,
the users are assigned with non-orthogonal sequences which serve as their
signatures. However, the activity detection which is based on the correlation
between the spreading sequences, degrades with increase in the number of users,
especially in the lower SNR region. In this paper, to improve the performance
of the CSMUD, we propose a sequence block based CSMUD, in which block of
sequences is used as signature of the user instead of single sequence. A
sequence block based group orthogonal matching pursuit algorithm is proposed to
jointly detect the activity and data. The proposed scheme reduces the detection
error rate (DER) by a magnitude of two at SNR = 10 dB in a system where the
number of users are three times more than the number of available resources
with each user having activity probability of 0.1. The DER of the proposed
scheme is below 10^-2 even at activity probability of 0.16 for sequence length
of 20 and overloading factor of 300%. Furthermore, at SNR = 10 dB, the DER of
the proposed scheme outperforms the conventional scheme by a magnitude of one
for a system with overloading factor of 500%.
Protein token: a dynamic unit in protein interactions
Date: 2018-09-27 00:00:00

In this study, we introduced a new unit, named "protein token", as a dynamic
protein structural unit for protein-protein interactions. Unlike the
conventional structural units, protein token is not based on the sequential or
spatial arrangement of residues, but comprises remote residues involved in
cooperative conformational changes during protein interactions. Application of
protein token on Ras GTPases revealed various tokens present in the
superfamily. Distinct token combinations were found in H-Ras interacting with
its various regulators and effectors, directing to a possible clue for the
multiplexer property of Ras superfamily. Thus, this protein token theory may
provide a new approach to study protein-protein interactions in broad
applications.
An Adaptive Tabu Search Algorithm for Market Clearing Problem in Turkish Day-Ahead Market
Date: 2018-09-26 00:00:00

In this study, we focus on the market clearing problem of Turkish day-ahead
electricity market. We propose a mathematical model by extending the variety of
bid types for different price regions. The commercial solvers may not find any
feasible solution for the proposed problem in some instances within the given
time limits. Hence, we design an adaptive tabu search (ATS) algorithm to solve
the problem. ATS discretizes continuous search space arising from the flow
variables. Our method has adaptive radius and it achieves backtracking by a
commercial solver. Then, we compare the performance of ATS with a heuristic
decomposition method from the literature by using synthetic data sets. We
evaluate the performances of the algorithms with respect to their solution
times and surplus differences. ATS performs better in most of the sets.
Intervention Strategies for Epidemics: Does Ignoring Time Delay Lead to Incorrect Predictions?
Date: 2018-09-27 00:00:00

Our paper investigates distributions of exposed and infectious time periods
in an epidemic model and how applying a disease control strategy affects the
model's accuracy. While ordinary differential equations are widely used for
their simplicity, they incorporate an exponential distribution for time spent
exposed or infectious. This allows for a high probability of unrealistically
short exposed and infectious time periods. We propose that caution must be
taken when applying intervention methods to basic models in order to avoid
inaccurate predictions. Delay differential equations, which use a delta
distribution for exposed and infectious periods, can provide better realism but
are more difficult to use and analyze. We introduce a multi-infected
compartment model to interpolate between an ODE model with exponential
distributions and a DDE model with delta distributions in order to investigate
the effect these distributions have on the dynamics of the system when an
intervention method is also included. Using steady state stability and
bifurcation analysis, this paper considers when simpler infectious disease
models can be used versus when more realistic time periods must be
incorporated. We find that the placement of control measures on subpopulations
and the length of the time delay impacts the accuracy of the simpler models.
A Hybrid Neural Network Framework and Application to Radar Automatic Target Recognition
Date: 2018-09-27 00:00:00

Deep neural networks (DNNs) have found applications in diverse signal
processing (SP) problems. Most efforts either directly adopt the DNN as a
black-box approach to perform certain SP tasks without taking into account of
any known properties of the signal models, or insert a pre-defined SP operator
into a DNN as an add-on data processing stage. This paper presents a novel
hybrid-NN framework in which one or more SP layers are inserted into the DNN
architecture in a coherent manner to enhance the network capability and
efficiency in feature extraction. These SP layers are properly designed to make
good use of the available models and properties of the data. The network
training algorithm of hybrid-NN is designed to actively involve the SP layers
in the learning goal, by simultaneously optimizing both the weights of the DNN
and the unknown tuning parameters of the SP operators. The proposed hybrid-NN
is tested on a radar automatic target recognition (ATR) problem. It achieves
high validation accuracy of 96\% with 5,000 training images in radar ATR.
Compared with ordinary DNN, hybrid-NN can markedly reduce the required amount
of training data and improve the learning performance.
Kalman inversion stress microscopy
Date: 2018-09-28 00:00:00

Although mechanical cues are crucial to tissue morphogenesis and development,
the tissue mechanical stress field remains poorly characterized. Given traction
force timelapse movies, as obtained by traction force microscopy of in vitro
cellular sheets, we show that the tissue stress field can be estimated by
Kalman filtering. After validation using numerical data, we apply Kalman
inversion stress microscopy to experimental data. We combine the inferred
stress field with velocity and cell shape measurements to quantify the rheology
of epithelial cell monolayers in physiological conditions, found to be close to
that of an elastic and active material.
Change of Measure in the Heston Model given a violated Feller Condition
Authors: Sascha Desmettre
Date: 2018-09-28 00:00:00

When dealing with Heston's stochastic volatility model, the change of measure from the subjective measure P to the objective measure Q is usually investigated under the assumption that the Feller condition is satisfied. This paper closes this gap in the literature by deriving sufficient conditions for the existence of an equivalent (local) martingale measure in the Heston model when the Feller condition is violated. We also supplement the existing literature by the case of a finite lifetime of the Laplace transform of the integrated volatility process. Moreover, we deduce conditions for the stock price process in the Heston model being a true martingale, regardless if the Feller condition is satisfied or not.
Autoamplification and competition drive symmetry breaking: Initiation of centriole duplication by the PLK4-STIL network
Authors: Marcin Leda, Andrew J. Holland, Andrew B. Goryachev
Date: 2018-09-28 00:00:00

Symmetry breaking, a central principle of physics, has been hailed as the driver of self-organization in biological systems in general and biogenesis of cellular organelles in particular, but the molecular mechanisms of symmetry breaking only begin to become understood. Centrioles, the structural cores of centrosomes and cilia, must duplicate every cell cycle to ensure their faithful inheritance through cellular divisions. Work in model organisms identified conserved proteins required for centriole duplication and found that altering their abundance affects centriole number. However, the biophysical principles that ensure that, under physiological conditions, only a single procentriole is produced on each mother centriole remain enigmatic. Here we propose a mechanistic biophysical model for the initiation of procentriole formation in mammalian cells. The model faithfully recapitulates the experimentally observed transition from PLK4 uniformly distributed around the mother centriole, the "ring", to a unique PLK4 focus, the "spot", that triggers the assembly of a new procentriole. This symmetry breaking requires a dual positive feedback based on autocatalytic activation of PLK4 and enhanced centriolar anchoring of PLK4-STIL complexes by phosphorylated STIL. We find that, contrary to previous proposals, in situ degradation of active PLK4 is insufficient to break symmetry. Instead, the model predicts that competition between transient PLK4 activity maxima for PLK4-STIL complexes explains both the instability of the PLK4 ring and formation of the unique PLK4 spot. In the model, strong competition at physiologically normal parameters robustly produces a single procentriole, while increasing overexpression of PLK4 and STIL weakens the competition and causes progressive addition of procentrioles in agreement with experimental observations.
An inverse scattering approach for geometric body generation: a machine learning perspective
Authors: Li Jinhong, Liu Hongyu, Tsui Wing-Yan, Wang Xianchao
Date: 2018-09-17 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we are concerned with the 2D and 3D geometric shape generation
by prescribing a set of characteristic values of a specific geometric body. One
of the major motivations of our study is the 3D human body generation in
various applications. We develop a novel method that can generate the desired
body with customized characteristic values. The proposed method follows a
machine-learning flavour that generates the inferred geometric body with the
input characteristic parameters from a training dataset. One of the critical
ingredients and novelties of our method is the borrowing of inverse scattering
techniques in the theory of wave propagation to the body generation. This is
done by establishing a delicate one-to-one correspondence between a geometric
body and the far-field pattern of a source scattering problem governed by the
Helmholtz system. It in turn enables us to establish a one-to-one
correspondence between the geometric body space and the function space defined
by the far-field patterns. Hence, the far-field patterns can act as the shape
generators. The shape generation with prescribed characteristic parameters is
achieved by first manipulating the shape generators and then reconstructing the
corresponding geometric body from the obtained shape generator by a stable
multiple-frequency Fourier method. Our method is easy to implement and produces
more efficient and stable body generations. We provide both theoretical
analysis and extensive numerical experiments for the proposed method. The study
is the first attempt to introduce inverse scattering approaches in combination
with machine learning to the geometric body generation and it opens up many
opportunities for further developments.
The Generalized Sinusoidal Frequency Modulated Waveform for Active Sonar Systems
Date: 2018-09-28 00:00:00
Tasks: Sensitivity

Pulse Compression (PC) active sonar waveforms provide a significant
improvement in range resolution over single frequency sinusoidal waveforms also
known as Continuous Wave (CW) waveforms. Since their inception in the 1940's, a
wide variety of PC waveforms have been designed using either Frequency
Modulation (FM), phase coding, or frequency hopping to suite particular sonar
applications. The Sinusoidal FM (SFM) waveform modulates its Instantaneous
Frequency (IF) by a single frequency sinusoid to achieve high Doppler
sensitivity which also aids in suppressing reverberation. This allows the SFM
waveform to resolve target velocities. While the SFM's resolution in range is
inversely proportional to its bandwidth, the SFM's Auto-Correlation Function
(ACF) contains many large sidelobes. The periodicity of the SFM's IF creates
these sidelobes and impairs the SFM's ability to clearly distinguish multiple
targets in range. This dissertation describes a generalization of the SFM
waveform, referred to as the Generalized SFM (GSFM) waveform, that modifies the
IF to resemble the time/voltage characteristic of a FM chirp waveform. As a
result of this modification, the Doppler sensitivity of the SFM is preserved
while substantially reducing the high range sidelobes producing a waveform
whose Ambiguity Function (AF) approaches a thumbtack shape. This dissertation
describes the properties of the GSFM's thumbtack AF shape, compares it to other
well known waveforms with a similar AF shape, and additionally considers some
of the practical considerations of active sonar systems including transmitting
the GSFM on piezoelectric transducers and the GSFM's ability to suppress
reverberation. Lastly, this dissertation also describes designing a family of
in-band nearly orthogonal waveforms with potential applications to Continuous
Active Sonar (CAS).
Exact Solutions for Optimal Investment Strategies and Indifference Prices under Non-Differentiable Preferences
Date: 2018-09-28 00:00:00

We propose an algorithm to calculate the exact solution for utility
optimization problems on finite state spaces under a class of
non-differentiable preferences. We prove that optimal strategies must lie on a
discrete grid in the plane, and this allows us to reduce the dimension of the
problem and define a very efficient method to obtain those strategies. We also
show how fast approximations for the value function can be obtained with an a
priori specified error bound and we use these to replicate results for
investment problems with a known closed-form solution. These results show the
efficiency of our approach, which can then be used to obtain numerical
solutions for problems for which no explicit formulas are known.
Resonant Beam Communications with Photovoltaic Receiver for Optical Data and Power Transfer
Date: 2020-04-18 00:00:00

The vision and requirements of the sixth generation (6G) mobile communication
systems are expected to adopt freespace optical communication (FSO) and
wireless power transfer (WPT). The laser-based WPT or wireless information
transfer (WIT) usually faces the challenges of mobility and safety. We present
a mobile and safe resonant beam communication (RBCom) system, which can realize
high-rate simultaneous wireless information and power transfer (SWIPT). We
propose an analytical model to depict its carrier beam and information transfer
procedures. The numerical results show that RBCom can achieve more than 40 mW
charging power and 1:6 Gbit/s channel capacity with orthogonal frequency
division multiplexing (OFDM) scheme, which can be applied in future scenario
where power and high-rate data are simultaneously desired.
Single Snapshot Super-Resolution DOA Estimation for Arbitrary Array Geometries
Date: 2018-11-15 00:00:00
Tasks: Super-Resolution

We address the problem of search-free direction of arrival (DOA) estimation
for sensor arrays of arbitrary geometry under the challenging conditions of a
single snapshot and coherent sources. We extend a method of searchfree
super-resolution beamforming, originally applicable only for uniform linear
arrays, to arrays of arbitrary geometry. The infinite dimensional primal atomic
norm minimization problem in continuous angle domain is converted to a dual
problem. By exploiting periodicity, the dual function is then represented with
a trigonometric polynomial using a truncated Fourier series. A linear rule of
thumb is derived for selecting the minimum number of Fourier coefficients
required for accurate polynomial representation, based on the distance of the
farthest sensor from a reference point. The dual problem is then expressed as a
semidefinite program and solved efficiently. Finally, the searchfree DOA
estimates are obtained through polynomial rooting, and source amplitudes are
recovered through least squares. Simulations using circular and random planar
arrays show perfect DOA estimation in noise-free cases.
Millimeter Wave Beam Training: A Survey
Date: 2018-09-28 00:00:00
Tasks: Survey

The initial access is required to establish a connection between millimeter
wave access point (AP) and users. The large isotropic pathloss at high
frequencies can be mitigated by the use of highly directional antennas.
However, it complicated the initial access procedures and increased the
latency, defying one of the major low latency objective of 5G systems. Beam
training algorithms are developed for the AP as well as for the users to find
the desired beam quickly and reduce the initial access delay. But, these beam
training protocols have to be run very frequently due to outage events, user's
mobility and period of sleep cycles.
Influence of introducing high speed railways on intercity travel behavior in Vietnam
Date: 2018-09-29 00:00:00
Tasks: Demand Forecasting

It is one of hottest topics in Vietnam whether to construct a High Speed Rail
(HSR) system or not in near future. To analyze the impacts of introducing the
HSR on the intercity travel behavior, this research develops an integrated
intercity demand forecasting model to represent trip generation and frequency,
destination choice and travel mode choice behavior. For this purpose, a
comprehensive questionnaire survey with both Revealed Preference (RP)
information (an inter-city trip diary) and Stated Preference (SP) information
was conducted in Hanoi in 2011. In the SP part, not only HSR, but also Low Cost
Carrier is included in the choice set, together with other existing inter-city
travel modes. To make full use of the advantages of each type of data and to
overcome their disadvantages, RP and SP data are combined to describe the
destination choice and mode choice behavior, while trip generation and
frequency are represented by using the RP data. The model estimation results
show the inter-relationship between trip generation and frequency, destination
choice and travel mode choice, and confirm that those components should not
dealt with separately.
Design of Intra-body Nano-communication Network for Future Nano-medicine
Date: 2018-09-29 00:00:00

Intra-body communication is a method that utilizes the human body as a
broadcast biological medium for electromagnetic signals to inter-connect
wireless body sensors. Study of the collaboration between electromagnetic waves
and human cells has gained importance in recent years leading towards
developing and establishing new novel concept which is the idea of
nano-communications using nano-networks to form in-vivo communication that are
aimed to offer wireless communication between interior nano-sensors. The
emergent of this advanced unprecedented prospective approach of deploying the
in vivo communication concept in the health sector is considered as a key
potential technology that enhances healthcare delivery and enables the progress
of future applications and services.