pwc-archive/papers-with-abstracts huggingface dataset:
CAMIRADA: Cancer microRNA association discovery algorithm, a case study on breast cancer
Date: 2019-02-27 00:00:00

In recent studies, non-coding protein RNAs have been identified as microRNA
that can be used as biomarkers for early diagnosis and treatment of cancer,
that decrease mortality in cancer. A microRNA may target hundreds or thousands
of genes and a gene may regulate several microRNAs, so determining which
microRNA is associated with which cancer is a big challenge. Many computational
methods have been performed to detect micoRNAs association with cancer, but
more effort is needed with higher accuracy. Increasing research has shown that
relationship between microRNAs and TFs play a significant role in the diagnosis
of cancer. Therefore, we developed a new computational framework (CAMIRADA) to
identify cancer-related microRNAs based on the relationship between microRNAs
and disease genes (DG) in the protein network, the functional relationships
between microRNAs and Transcription Factors (TF) on the co-expression network,
and the relationship between microRNAs and the Differential Expression Gene
(DEG) on co-expression network. The CAMIRADA was applied to assess breast
cancer data from two HMDD and miR2Disease databases. In this study, the AUC for
the 65 microRNAs of the top of the list was 0.95, which was more accurate than
the similar methods used to detect microRNAs associated with the cancer artery.
Feature Selection for Transient Stability Assessment Based on Improved Maximal Relevance and Minimal Redundancy Criterion
Date: 2019-01-23 00:00:00
Tasks: Classification, feature selection

A new feature selection method based on an improved maximal relevance and
minimal redundancy (mRMR) criterion was proposed for power system transient
stability assessment. First, the standard mRMR was improved by introducing a
weight coefficient in the evaluation criteria to refine the measurement of the
features correlation and redundancy. Then, the possible real-time information
provided by phasor measurement unit (PMU) considered, a group of system-level
classification features were extracted from the power system operation
parameters to build the original feature set, and the improved mRMR was
employed to evaluate the classification capability of the original features for
feature selection. A group of nested candidate feature subsets were obtained by
using the incremental search technique, and each candidate feature subset was
tested by a support vector machine classifier to find the optimal feature
subset with the highest classification accuracy. The effectiveness of the
proposed method was validated by the simulation results on the New England
39-bus system and IEEE 50-generator test system.
Generosity, selfishness and exploitation as optimal greedy strategies for resource sharing
Date: 2019-10-28 00:00:00

Resource sharing outside the kinship bonds is rare. Besides humans, it occurs
in chimpanzee, wild dogs and hyenas as well as in vampire bats. Resource
sharing is an instance of animal cooperation, where an animal gives away part
of the resources that it owns for the benefit of a recipient. Taking
inspiration from blood-sharing in vampire bats, here show the emergence of
generosity in a Markov game, which couples the resource sharing between two
players with the gathering task of that resource. At variance with the
classical evolutionary models for cooperation, the optimal strategies of this
game can be potentially learned by animals during their life-time. The players
act greedily, that is, they try to individually maximize only their personal
income. Nonetheless, the analytical solution of the model shows that three non
trivial optimal behaviours emerge depending on conditions. Besides the obvious
case when players are selfish in their choice of resource division, there are
conditions under which both players are generous. Moreover, we also found a
range of situations in which one selfish player exploits another generous
individual, for the satisfaction of both players. Our results show that
resource sharing is favoured by three factors: a long time horizon over which
the players try to optimize their own game, the similarity among players in
their ability of performing the resource-gathering task, as well as by the
availability of resources in the environment. These concurrent requirements
lead to identify necessary conditions for the emergence of generosity.
Asymptotics for volatility derivatives in multi-factor rough volatility models
Authors: Chloe Lacombe, Aitor Muguruza, Henry Stone
Date: 2019-03-07 00:00:00

We present small-time implied volatility asymptotics for Realised Variance (RV) and VIX options for a number of (rough) stochastic volatility models via large deviations principle. We provide numerical results along with efficient and robust numerical recipes to compute the rate function; the backbone of our theoretical framework. Based on our results, we further develop approximation schemes for the density of RV, which in turn allows to express the volatility swap in close-form. Lastly, we investigate different constructions of multi-factor models and how each of them affects the convexity of the implied volatility smile. Interestingly, we identify the class of models that generate non-linear smiles around-the-money.
Performance of Multiple Antennas Selection in SIMO Systems with BPSK/QPSK Modulations
Date: 2019-07-13 00:00:00

This paper studies the performance of single-input multiple-output (SIMO)
systems under receive antenna selection (RAS) and BPSK/QPSK modulations. At the
receiver, a subset of branches are selected and combined using maximal-ratio
combining (MRC) to maximize the instantaneous Signal to Noise Ratio (SNR). By
assuming independent and identical distributed (i.i.d.) Rayleigh flat fading, a
closed-form expression, with considerably high precision, is developed to
approximate the average input-output mutual information, also termed as
symmetric capacity, of the whole system. Later, this approximated expression is
further utilized to investigate the efficient capacity and energy efficiency of
the SIMO system under BPSK/QPSK modulations and RAS. Besides analytical
derivations, simulations are provided to demonstrate the approximation
precision, feasibility and validity of the derived results.
Linear Predictability in MRI Reconstruction: Leveraging Shift-Invariant Fourier Structure for Faster and Better Imaging
Date: 2019-06-18 00:00:00
Tasks: MRI Reconstruction

Over the past several decades, many different types of computational imaging
approaches have been proposed for improving MRI. In this paper, we provide an
overview of methods that assume that MRI Fourier data is linearly predictable.
Linear prediction is well known in signal processing and may be most
recognizable for its usefulness in speech processing and spectrum estimation
applications. In MRI, linear predictability implies that data can be sampled
below the conventional Nyquist rate, since unmeasured data may be imputed as a
shift-invariant linear combination of measured samples. Linear predictive
methods include some of the earliest methods in the computational MRI
reconstruction field, some of the most widely utilized computational MRI
methods in modern clinical practice, and some of the most flexible and
versatile modern computational imaging approaches that are enabling
unprecedented new styles of data acquisition. In addition, the concept of
linear predictability can be used to unify a number of more classical MRI
reconstruction constraints, but without needing to make the strong assumptions
of classical constrained reconstruction methods.
Economic resilience from input-output susceptibility improves predictions of economic growth and recovery
Date: 2019-02-27 00:00:00

Modern macroeconomic theories were unable to foresee the last Great Recession
and could neither predict its prolonged duration nor the recovery rate. They
are based on supply-demand equilibria that do not exist during recessionary
shocks. Here we focus on resilience as a nonequilibrium property of networked
production systems and develop a linear response theory for input-output
economics. By calibrating the framework to data from 56 industrial sectors in
43 countries between 2000 and 2014, we find that the susceptibility of
individual industrial sectors to economic shocks varies greatly across
countries, sectors, and time. We show that susceptibility-based predictions
that take sector- and country-specific recovery into account, outperform--by
far--standard econometric growth-models. Our results are analytically rigorous,
empirically testable, and flexible enough to address policy-relevant scenarios.
We illustrate the latter by estimating the impact of recently imposed tariffs
on US imports (steel and aluminum) on specific sectors across European
countries.
Kernel Based Estimation of Spectral Risk Measures
Authors: Suparna Biswas, Rituparna Sen
Date: 2019-03-08 00:00:00

Spectral risk measures (SRMs) belong to the family of coherent risk measures. A natural estimator for the class of SRMs has the form of L-statistics. Various authors have studied and derived the asymptotic properties of the empirical estimator of SRM. We propose a kernel based estimator of SRM. We investigate the large sample properties of general L-statistics based on i.i.d and dependent observations and apply them to our estimator. We prove that it is strongly consistent and asymptotically normal. We compare the finite sample performance of our proposed kernel estimator with that of several existing estimators for different SRMs using Monte Carlo simulation. We observe that our proposed kernel estimator outperforms all the estimators. Based on our simulation study we have estimated the exponential SRM of four future indices-that is Nikkei 225, Dax, FTSE 100, and Hang Seng. We also discuss the use of SRM in setting initial margin requirements of clearinghouses. Finally we perform a backtesting exercise of SRM.
A clinical update on Antibiotic Resistance Gram-negative bacteria in Malaysia- a review
Authors: Fazlul Mkk, Shah Samiur Rashid, Nazmul MHM, Zaidul I. S. M, Roesnita Baharudin, Aizi Nor Mazila Ramli
Date: 2019-02-13 00:00:00

Antibiotics are the wonder discoveries to combat microbes. For decades, multiple varieties of antibiotics have been used for therapeutic purposes in hospital settings and communities throughout the world. Unfortunately, bacteria have become resistant to commonly prescribed antibiotics. This review aims to explore the development, challenges, and the current state of antibiotic resistance available literature in Malaysia. This review aims to explore the development, challenges, and the current state of antibiotic resistance available literature in Malaysia. This review reiterates the antibiotic resistance among the gram-negative bacteria is increasing and they are becoming resistant to nearly all groups of antibiotics. The antibiotic treatments are minimal and hard to treat in multi-drug resistance bacterial infection, resulting in morbidity and mortality. The prevalence rate of antibiotic resistance from the literature suggests that educating patients and the public is essential to prevent and control the spread of antibiotic resistance. In particular, there is an urgent need for a surveillance system of regular monitoring on the microbiomes, the discovery of novel antibiotics and therapeutic application of antibiotics are mandatory.
Optimization methods for MR image reconstruction (long version)
Date: 2019-06-13 00:00:00
Tasks: compressed sensing, Image Reconstruction

The development of compressed sensing methods for magnetic resonance (MR)
image reconstruction led to an explosion of research on models and optimization
algorithms for MR imaging (MRI). Roughly 10 years after such methods first
appeared in the MRI literature, the U.S. Food and Drug Administration (FDA)
approved certain compressed sensing methods for commercial use, making
compressed sensing a clinical success story for MRI. This review paper
summarizes several key models and optimization algorithms for MR image
reconstruction, including both the type of methods that have FDA approval for
clinical use, as well as more recent methods being considered in the research
community that use data-adaptive regularizers. Many algorithms have been
devised that exploit the structure of the system model and regularizers used in
MRI; this paper strives to collect such algorithms in a single survey. Many of
the ideas used in optimization methods for MRI are also useful for solving
other inverse problems.
Joint Beamforming and Dynamic Relay Positioning for mmWave Urban Communications
Date: 2020-02-18 00:00:00

The use of millimeter wave (mmWave) spectrum for commercial wireless
communications is expected to offer data rates in the order of
Gigabits-per-second, thus able to support future applications such as
Vehicle-to-Vehicle or Vehicle-to-Infrastructure communication. However,
especially in urban settings, mmWave signal propagation is sensitive to
blockage by surrounding objects, resulting in significant signal attenuation.
One approach to mitigate the effect of attenuation is through multi-hop
communication with the help of relays. Leveraging the unique characteristics of
the mmWave medium, we consider a single-source/destination $2$-hop system,
where a cluster of spatially distributed and reconfigurable relays is used to
cooperatively amplify-and-forward the source signal to the destination. Our
system evolves in time slots, during which not only are optimal beamforming
weights centrally determined, but also future relay positions for the
subsequent time slot are optimally selected, jointly maximizing the expected
signal-to-interference+noise ratio at the destination. Optimal predictive relay
positioning is achieved by formulating a 2-stage stochastic programming
problem, which is efficiently approximated via a conditional
sample-average-approximation surrogate, and solved in a purely distributed
fashion across relays. The efficacy of the proposed near-optimal positioning
policy is corroborated by comparison against a randomized relay positioning
policy, clearly confirming its superiority.
On occupation times in the red of L\'evy risk models
Date: 2019-07-23 00:00:00

In this paper, we obtain analytical expression for the distribution of the
occupation time in the red (below level $0$) up to an (independent) exponential
horizon for spectrally negative L\'{e}vy risk processes and refracted
spectrally negative L\'{e}vy risk processes. This result improves the existing
literature in which only the Laplace transforms are known. Due to the close
connection between occupation time and many other quantities, we provide a few
applications of our results including future drawdown, inverse occupation time,
Parisian ruin with exponential delay, and the last time at running maximum. By
a further Laplace inversion to our results, we obtain the distribution of the
occupation time up to a finite time horizon for refracted Brownian motion risk
process and refracted Cram\'{e}r-Lundberg risk model with exponential claims.
Control over Skies: Survivability, Coverage, and Mobility Laws for Hierarchical Aerial Base Stations
Authors: Vishal Sharma, Navuday Sharma, Mubashir Husain Rehmani, Haris Pervaiz
Date: 2019-03-09 00:00:00
Tasks: Management

Aerial Base Stations (ABSs) have gained significant importance in the next generation of wireless networks for accommodating mobile ground users and flash crowds with high convenience and quality. However, to achieve an efficient ABS network, many factors pertaining to ABS flight, governing laws and information transmissions must be studied. In this article, multi-drone communications are studied in three major aspects, survivability, coverage, and mobility laws, which optimize the multi-tier ABS network to avoid issues related to inter-cell interference, deficient energy, frequent handovers, and lifetime. The article includes simulation results of hierarchical ABS allocations for handling a set of users over a defined geographical area. Several open issues and challenges are presented to provide deep insights into the ABS network management and its utility framework.
Folded Graph Signals: Sensing with Unlimited Dynamic Range
Date: 2020-01-20 00:00:00
Tasks: Graph Sampling, Time Series, Time Series Analysis

Self-reset analog-to-digital converters (ADCs) are used to sample high
dynamic range signals resulting in modulo-operation based folded signal
samples. We consider the case where each vertex of a graph (e.g., sensors in a
network) is equipped with a self-reset ADC and senses a time series. Graph
sampling allows the graph time series to be represented by the signals at a
subset of sampled vertices and time instances. We investigate the problem of
recovering bandlimited continuous-time graph signals from folded signal
samples. We derive sufficient conditions to achieve successful recovery of the
graph signal from the folded signal samples, which can be achieved via integer
programming. To resolve the scalability issue of integer programming, we
propose a sparse optimization recovery method for graph signals satisfying
certain technical conditions. Such an approach requires a novel graph sampling
scheme that selects vertices with small signal variation. The proposed
algorithm exploits the inherent relationship among the graph vertices in both
the vertex and time domains to recover the graph signal. Simulations and
experiments on images validate the feasibility of our proposed approach.
Through-the-Wall Imaging Exploiting 2.4GHz Commodity Wi-Fi
Date: 2019-03-10 00:00:00

In this letter, we experimentally investigate a low-cost through-the-wall
imaging exploiting Wi-Fi signals in an indoor environment from the perspective
of holographic imaging. In our experiments, a pair of antennas in a synthetic
aperture mode is used to acquire signals produced by commodity Wi-Fi devices
and reflected from the scene in a synthetic aperture mode. The classical
filtered back propagation (FBP) algorithm is then employed to form the image
based on these signals. We use an IEEE 802.11n wireless router working at
2.4GHz with bandwidth of 20MHz. Selected experimental results are provided to
demonstrate the performance of the proposed Wi-Fi based imaging scheme.
Towards Ultra-Reliable Low-Latency Communications: Typical Scenarios, Possible Solutions, and Open Issues
Date: 2019-03-10 00:00:00

Ultra-reliable low-latency communications (URLLC) has been considered as one
of the three new application scenarios in the \emph{5th Generation} (5G) \emph
{New Radio} (NR), where the physical layer design aspects have been specified.
With the 5G NR, we can guarantee the reliability and latency in radio access
networks. However, for communication scenarios where the transmission involves
both radio access and wide area core networks, the delay in radio access
networks only contributes to part of the \emph{end-to-end} (E2E) delay. In this
paper, we outline the delay components and packet loss probabilities in typical
communication scenarios of URLLC, and formulate the constraints on E2E delay
and overall packet loss probability. Then, we summarize possible solutions in
the physical layer, the link layer, the network layer, and the cross-layer
design, respectively. Finally, we discuss the open issues in prediction and
communication co-design for URLLC in wide area large scale networks.
Generalized Minkowski sets for the regularization of inverse problems
Date: 2019-03-10 00:00:00
Tasks: Additive models

Many works on inverse problems in the imaging sciences consider
regularization via one or more penalty functions or constraint sets. When the
models/images are not easily described using one or a few penalty
functions/constraints, additive model descriptions for regularization lead to
better imaging results. These include cartoon-texture decomposition,
morphological component analysis, and robust principal component analysis;
methods that typically rely on penalty functions. We propose a regularization
framework, based on the Minkowski set, that merges the strengths of additive
models and constrained formulations. We generalize the Minkowski set, such that
the model parameters are the sum of two components, each of which is
constrained to an intersection of sets. Furthermore, the sum of the components
is also an element of another intersection of sets. These generalizations allow
us to include multiple pieces of prior knowledge on each of the components, as
well as on the sum of components, which is necessary to ensure physical
feasibility of partial-differential-equation based parameters estimation
problems. We derive the projection operation onto the generalized Minkowski
sets and construct an algorithm based on the alternating direction method of
multipliers. We illustrate how we benefit from using more prior knowledge in
the form of the generalized Minkowski set using seismic waveform inversion and
video background-anomaly separation.
Effect of Mutual Coupling on the Performance of STCM-MIMO Systems
Date: 2019-03-10 00:00:00

Space-time coded massive (STCM) multiple-input multiple-output (MIMO) system
provides superior bit error rate (BER) performance compared with the
conventional space-time coding and massive MIMO techniques. The transmitter of
the STCM-MIMO system consists of a large antenna array. In a practical system,
the self-interference created by the signals transmitted by the elements of
this antenna array, known as mutual coupling (MC), degrades the performance of
the system. The MC effect is pronounced in communication systems with a large
antenna array. On the other hand, increasing the number of transmitting
antennas results in improved BER performance. Hence, there is a trade off in
selecting the optimum number of transmitting antennas in an STCM-MIMO system.
In order to take the impact of MC into account, we have derived an analytical
expression for the received signal to accurately model the STCM-MIMO system
under the existence of the MC effect. We present an algorithm to select the
optimal number of antennas to minimize mutual coupling and the system bit error
rate (BER). Through computer simulations, we investigate the BER performance of
the STCM-MIMO system for different numbers of array elements.
Price competition with uncertain quality and cost
Date: 2019-04-10 00:00:00

Consumers in many markets are uncertain about firms' qualities and costs, so
buy based on both the price and the quality inferred from it. Optimal pricing
depends on consumer heterogeneity only when firms with higher quality have
higher costs, regardless of whether costs and qualities are private or public.
If better quality firms have lower costs, then good quality is sold cheaper
than bad under private costs and qualities, but not under public. However, if
higher quality is costlier, then price weakly increases in quality under both
informational environments.
Real Polynomial Gram Matrices Without Real Spectral Factors
Date: 2019-03-11 00:00:00

It is well known that a non-negative definite polynomial matrix (a polynomial
Gramian) $G(t)$ can be written as a product of its polynomial spectral factors,
$G(t) = X(t)^H X(t)$. In this paper, we give a new algebraic characterization
of spectral factors when $G(t)$ is real-valued. The key idea is to construct a
representation set that is in bijection with the set of real polynomial
Gramians. We use the derived characterization to identify the set of all
complex polynomial matrices that generate real-valued Gramians, and we
formulate a conjecture that typical rank-deficient real polynomial Gramians
have real spectral factors.