pwc-archive/papers-with-abstracts huggingface dataset:
The Co-Terminal Swap Market Model with Bergomi Stochastic Volatility
Date: 2018-08-24 00:00:00
Tasks: Management

In this article, we apply the forward variance modeling approach by L.Bergomi
to the co-terminal swap market model. We build an interest rate model for which
all the market price changes of hedging instruments, interest rate swaps and
European swaptions, are interpreted as the state variable variations, and no
diffusion parameter calibration procedure is required. The model provides quite
simple profit and loss (PnL) formula, with which we can easily understand where
a material PnL trend comes from when it appears, and consider how we should
modify the model parameters. The model has high flexibility to control the
model dynamics because parameter calibration is unnecessary and the model
parameters can be used solely for the purpose of the model dynamics control.
With the model, the position management of the exotic interest rate products,
e.g. Bermudan swaptions, can be carried out in a more sophisticated and
systematic manner. A numerical experiment is performed to show the
effectiveness of the approach for a Canary swaption, which is a special form of
a Bermudan swaption.
Bayesian Multi--Dipole Modeling in the Frequency Domain
Date: 2018-12-13 00:00:00

Background: Magneto- and Electro-encephalography record the electromagnetic
field generated by neural currents with high temporal frequency and good
spatial resolution, and are therefore well suited for source localization in
the time and in the frequency domain. In particular, localization of the
generators of neural oscillations is very important in the study of cognitive
processes in the healthy and in the pathological brain.
  New method: We introduce the use of a Bayesian multi-dipole localization
method in the frequency domain. Given the Fourier Transform of the data at one
or multiple frequencies and/or trials, the algorithm approximates numerically
the posterior distribution with Monte Carlo techniques.
  Results: We use synthetic data to show that the proposed method behaves well
under a wide range of experimental conditions, including low signal-to-noise
ratios and correlated sources. We use dipole clusters to mimic the effect of
extended sources. In addition, we test the algorithm on real MEG data to
confirm its feasibility.
  Comparison with existing method(s): Throughout the whole study, DICS (Dynamic
Imaging of Coherent Sources) is used systematically as a benchmark. The two
methods provide similar general pictures; the posterior distributions of the
Bayesian approach contain much richer information at the price of a higher
computational cost.
  Conclusions: The Bayesian method described in this paper represents a
reliable approach for localization of multiple dipoles in the frequency domain.
Effect of active case finding on dengue control: Implications from a mathematical model
Date: 2018-08-23 00:00:00

Dengue control in India is a challenging task due to complex healthcare
settings. In yesteryears, an amplification of dengue infections in India posed
the need for introspection of existing dengue control policies. Prior
understanding of the impacts of control interventions is necessary for their
future implementation. In this paper, we propose and analyze a compartmental
model of dengue to assess the impact of active case finding (ACF) on dengue
disease transmission. Currently, primary prevention of dengue is possible only
with vector control and personal protection from the bites of infected
mosquitoes. Although a few experimental studies are performed to assess ACF in
dengue disease, but this is the first attempt to represent and study the
dynamics of disease using ACF as a control strategy. Local and global dynamics
of the system are studied. We use sensitivity analysis to see the effects of
controllable parameters of the model on the basic reproduction number and total
number of infective population. We find that decrease in the biting rate of
mosquitoes, and increase in the rate of hospitalization and/or notification,
death rate of mosquitoes and ACF for asymptomatic and symptomatic individuals
play crucial role for the reduction of disease prevalence. We calibrate our
model to the yearly dengue cases in eight dengue endemic states of India. The
results of our study show that ACF of symptomatic individuals will have
significant effect on dengue case reduction but ACF of asymptomatic individuals
cannot be ignored. Our findings indicate that the healthcare organizations must
focus on ACF of symptomatic as well as asymptomatic individuals along with
personal protection and mosquitoes control to achieve rapid reduction of dengue
cases in India.
Complexity of products: the effect of data regularisation
Date: 2018-10-15 00:00:00

Among several developments, the field of Economic Complexity (EC) has notably
seen the introduction of two new techniques. One is the Bootstrapped Selective
Predictability Scheme (SPSb), which can provide quantitative forecasts of the
Gross Domestic Product of countries. The other, Hidden Markov Model (HMM)
regularisation, denoises the datasets typically employed in the literature. We
contribute to EC along three different directions. First, we prove the
convergence of the SPSb algorithm to a well-known statistical learning
technique known as Nadaraya-Watson Kernel regression. The latter has
significantly lower time complexity, produces deterministic results, and it is
interchangeable with SPSb for the purpose of making predictions. Second, we
study the effects of HMM regularization on the Product Complexity and logPRODY
metrics, for which a model of time evolution has been recently proposed. We
find confirmation for the original interpretation of the logPRODY model as
describing the change in the global market structure of products with new
insights allowing a new interpretation of the Complexity measure, for which we
propose a modification. Third, we explore new effects of regularisation on the
data. We find that it reduces noise, and observe for the first time that it
increases nestedness in the export network adjacency matrix.
Can we leverage rating patterns from traditional users to enhance recommendations for children?
Authors: Azpiazu Ion Madrazo, Green Michael, Anuyah Oghenemaro, Pera Maria Soledad
Date: 2018-08-24 00:00:00

Recommender algorithms performance is often associated with the availability
of sufficient historical rating data. Unfortunately, when it comes to children,
this data is seldom available. In this paper, we report on an initial analysis
conducted to examine the degree to which data about traditional users, i.e.,
adults, can be leveraged to enhance the recommendation process for children.
Aperiodic Array Synthesis for Multi-User MIMO Applications
Date: 2018-08-24 00:00:00

This paper demonstrates the advantages of aperiodic arrays in multi-user
multiple-input multiple-output systems for future mobile communication
applications. We propose a novel aperiodic array synthesis method which account
for the statistics of the propagation channel and the adaptive beamforming
algorithm. Clear performance gains in line-of-sight dominated propagation
environments are achieved in terms of the signal-to-interference-plus-noise
ratio, the sum rate capacity, as well as the spread of the amplifier output
power as compared to their regular counterparts. We also show that the
performance is not sacrificed in rich scattering environments. Hence, aperiodic
array layouts can provide performance gains in millimeter-wave applications
with a dominating line-of-sight component.
Random Matrix Theory Model for Mean Notch Depth of the Diagonally Loaded MVDR Beamformer for a Single Interferer Case
Date: 2018-08-25 00:00:00

Adaptive beamformers (ABFs) suppress interferers by placing a notch in the
beampattern at the interferer direction. This suppres- sion improves detection
of a weaker signals in the presence of strong interferers. Hence the notch
depth plays a crucial role in determining the adaptive gain obtained from using
ABF over conventional beam- forming. This research derives models for the mean
notch depth of a diagonally loaded MVDR ABF for a single interferer case. The
model describes the mean notch depth as a function of number of snapshots, the
number of sensors in the array, the interferer to noise ratio (INR) level, the
interferer direction and the diagonal loading level. The derivation uses random
matrix theory results on the be- havior of the eigenvectors of sample
covariance matrix. The notch depth predicted by the model is shown to be in
close agreement with simulation results over a range of INRs and snapshots.
Molecular dynamics simulations reveal the role of ceramicine B as novel PPAR{\gamma} partial agonist against type 2 diabetes
Date: 2018-08-25 00:00:00
Tasks: Molecular Docking

Peroxisome proliferator-activated receptors gamma (PPAR{\gamma}) are
ligand-activated controllers of various metabolic actions and insulin
sensitivity. PPAR{\gamma} is thus considered as an important target to treat
type 2 diabetes. Available PPAR{\gamma} drugs (full agonists) have robust
insulin-sensitizing properties but are accompanied by severe side effects
leading to complicated health problems. Here, we have used molecular docking
and a molecular dynamics simulation study to find a novel PPAR{\gamma} ligand
from a natural product. Our study suggests that the inhibition of ceramicine B
in the PPAR{\gamma} ligand-binding domain (LBD) could act as a partial agonist
and block cdk5-mediated phosphorylation. This result may provide an opportunity
for the development of new anti-diabetic drugs by targeting PPAR{\gamma} while
avoiding the side effects associated with full agonists.
Antenna Array Based Positional Modulation with a Two-Ray Multi-Path Model
Date: 2018-08-25 00:00:00

Traditional directional modulation (DM) designs are based on the assumption
that there is no multi-path effect between transmitters and receivers. One
problem with these designs is that the resultant systems will be vulnerable to
eavesdroppers which are aligned with or very close to the desired directions,
as the received modulation pattern at these positions is similar to the given
one. To solve the problem, a two-ray multi-path model is studied for positional
modulation and the coefficients design problem for a given array geometry and a
location-optimised antenna array is solved, where the multi-path effect is
exploited to generate a given modulation pattern at desired positions, with
scrambled values at positions around them.
A global assessment of tourism and recreation conservation threats to prioritise interventions
Date: 2018-08-25 00:00:00
Tasks: Management

We are increasingly using nature for tourism and recreation, an economic
sector now generating more than 10% of the global GDP and 10% of global total
employment. This growth though has come at a cost and we now have 5930 species
for which tourism and recreation are conservation threats. For the first time
we use global social media data to estimate where people go to experience
nature and determine how this tourism and recreation pressure overlap with the
distribution of threatened species. The more people seek interactions with
nature in an area, the larger the number of species threatened by those
interactions is. Clear crisis areas emerge where many species sensitive to
tourism are exposed to high tourism pressures and those are mainly coastal
marine regions. Our current tourism management approaches are not achieving
biodiversity conservation. The global increase in nature tourism and recreation
is set to continue and we need a global consistent response to mitigate its
biodiversity impact. Like with other extractive industries, we must prioritise
our efforts to diverge tourism away from crisis areas.
A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Driving
Date: 2018-08-25 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, BIG-bench Machine Learning, Deep Learning, Image Segmentation, Segmentation, Semantic Segmentation

Deep learning is a fast-growing machine learning approach to perceive and
understand large amounts of data. In this paper, general information about the
deep learning approach which is attracted much attention in the field of
machine learning is given in recent years and an application about semantic
image segmentation is carried out in order to help autonomous driving of
autonomous vehicles. This application is implemented with Fully Convolutional
Network (FCN) architectures obtained by modifying the Convolutional Neural
Network (CNN) architectures based on deep learning. Experimental studies for
the application are utilized 4 different FCN architectures named
FCN-AlexNet,FCN-8s, FCN-16s and FCN-32s. For the experimental studies, FCNs are
first trained separately and validation accuracies of these trained network
models on the used dataset is compared. In addition, image segmentation
inferences are visualized to take account of how precisely FCN architectures
can segment objects.
Efficient improvement of frequency-domain Kalman filter
Date: 2018-08-25 00:00:00
Tasks: Audio Signal Processing

The frequency-domain Kalman filter (FKF) has been utilized in many audio
signal processing applications due to its fast convergence speed and
robustness. However, the performance of the FKF in under-modeling situations
has not been investigated. This paper presents an analysis of the steady-state
behavior of the commonly used diagonalized FKF and reveals that it suffers from
a biased solution in under-modeling scenarios. Two efficient improvements of
the FKF are proposed, both having the benefits of the guaranteed optimal
steady-state behavior at the cost of a very limited increase of the
computational burden. The convergence behavior of the proposed algorithms is
also compared analytically. Computer simulations are conducted to validate the
improved performance of the proposed methods.
A Dichotomous Analysis of Unemployment Benefits
Authors: Xingwei Hu
Date: 2018-08-26 00:00:00
Tasks: Fairness

Using equal employment opportunity as a fairness hypothesis, real-time balanced-budget rule as a constraint, and policy stability as an objective, we derive a scientific formula for taxation policy and formulate a fair allocation for unemployment benefits. The general setting is a coalitional game in which a random subset of the players is selected to take a task, resulting in a random payoff; we attempt to divide the payoff among the players fairly. The formula describes a fair, debt-free, and asymptotic risk-free payroll tax rate for given unemployment and spending levels. Also robust to the choice of other objectives, the tax rate stimulates employment and boosts productivity within this fair and sustainable framework. With additional assumptions on productivity, the tax rate results in equality of outcome, reducing poverty and economic inequality. The fair division rule and the valuation approach could be applied to similar profit- or cost-sharing situations, including voting rights, health insurance, road sharing, and machine learning.
Evolutionary dynamics of cryptocurrency transaction networks: An empirical study
Date: 2018-08-26 00:00:00

Cryptocurrency is a well-developed blockchain technology application that is
currently a heated topic throughout the world. The public availability of
transaction histories offers an opportunity to analyze and compare different
cryptocurrencies. In this paper, we present a dynamic network analysis of three
representative blockchain-based cryptocurrencies: Bitcoin, Ethereum, and
Namecoin. By analyzing the accumulated network growth, we find that, unlike
most other networks, these cryptocurrency networks do not always densify over
time, and they are changing all the time with relatively low node and edge
repetition ratios. Therefore, we then construct separate networks on a monthly
basis, trace the changes of typical network characteristics (including degree
distribution, degree assortativity, clustering coefficient, and the largest
connected component) over time, and compare the three. We find that the degree
distribution of these monthly transaction networks cannot be well fitted by the
famous power-law distribution, at the same time, different currency still has
different network properties, e.g., both Bitcoin and Ethereum networks are
heavy-tailed with disassortative mixing, however, only the former can be
treated as a small world. These network properties reflect the evolutionary
characteristics and competitive power of these three cryptocurrencies and
provide a foundation for future research.
Advances in Computational Methods for Phylogenetic Networks in the Presence of Hybridization
Date: 2018-08-27 00:00:00

Phylogenetic networks extend phylogenetic trees to allow for modeling
reticulate evolutionary processes such as hybridization. They take the shape of
a rooted, directed, acyclic graph, and when parameterized with evolutionary
parameters, such as divergence times and population sizes, they form a
generative process of molecular sequence evolution. Early work on computational
methods for phylogenetic network inference focused exclusively on reticulations
and sought networks with the fewest number of reticulations to fit the data. As
processes such as incomplete lineage sorting (ILS) could be at play
concurrently with hybridization, work in the last decade has shifted to
computational approaches for phylogenetic network inference in the presence of
ILS. In such a short period, significant advances have been made on developing
and implementing such computational approaches. In particular, parsimony,
likelihood, and Bayesian methods have been devised for estimating phylogenetic
networks and associated parameters using estimated gene trees as data. Use of
those inference methods has been augmented with statistical tests for specific
hypotheses of hybridization, like the D-statistic. Most recently, Bayesian
approaches for inferring phylogenetic networks directly from sequence data were
developed and implemented. In this chapter, we survey such advances and discuss
model assumptions as well as methods' strengths and limitations. We also
discuss parallel efforts in the population genetics community aimed at
inferring similar structures. Finally, we highlight major directions for future
research in this area.
$\mathcal{R}_{0}$ fails to predict the outbreak potential in the presence of natural-boosting immunity
Date: 2018-08-27 00:00:00

Time varying susceptibility of host at individual level due to waning and
boosting immunity is known to induce rich long-term behavior of disease
transmission dynamics. Meanwhile, the impact of the time varying heterogeneity
of host susceptibility on the shot-term behavior of epidemics is not
well-studied, even though the large amount of the available epidemiological
data are the short-term epidemics. Here we constructed a parsimonious
mathematical model describing the short-term transmission dynamics taking into
account natural-boosting immunity by reinfection, and obtained the explicit
solution for our model. We found that our system show "the delayed epidemic",
the epidemic takes off after negative slope of the epidemic curve at the
initial phase of epidemic, in addition to the common classification in the
standard SIR model, i.e., "no epidemic" as $\mathcal{R}_{0}\leq1$ or normal
epidemic as $\mathcal{R}_{0}>1$. Employing the explicit solution we derived the
condition for each classification.
Transparent Tx and Rx Waveform Processing for 5G New Radio Mobile Communications
Date: 2018-08-27 00:00:00

Several different waveform processing techniques have been studied and
proposed for the 5G new radio (NR) physical layer, to support new mixed
numerology and asynchronous services. The evaluation and comparison of these
different techniques is commonly based on matched waveform processing in the
transmitter and receiver units. In this article, it is shown that different
techniques can be flexibly mixed, allowing to separately optimize
complexity-performance trade-offs for transmitter and receiver implementations.
Mixing of different waveform processing techniques is possible by setting
adequate requirements for transmitter and receiver baseband processing allowing
transparent design. The basic framework of how transmitter and receiver units
can be independently applied and evaluated in the context of transparent design
and an extensive set of examples of the achievable radio link performance with
unmatched transmitter and receiver waveform processing are provided. The
discussed approach and solutions simplify standardization, improve transparent
transmitter and receiver coexistence, and allow future-proof evolution path for
performance improvements in 5G NR physical layer hardware and algorithm design.
Feature selection for transient stability assessment based on kernelized fuzzy rough sets and memetic algorithm
Date: 2018-08-18 00:00:00
Tasks: Attribute, Classification, feature selection

A new feature selection method based on kernelized fuzzy rough sets (KFRS)
and the memetic algorithm (MA) is proposed for transient stability assessment
of power systems. Considering the possible real-time information provided by
wide-area measurement systems, a group of system-level classification features
are extracted from the power system operation parameters to build the original
feature set. By defining a KFRS-based generalized classification function as
the separability criterion, the memetic algorithm based on binary differential
evolution (BDE) and Tabu search (TS) is employed to obtain the optimal feature
subsets with the maximized classification capability. The proposed method may
avoid the information loss caused by the feature discretization process of the
rough-set based attribute selection, and comprehensively utilize the advantages
of BDE and TS to improve the solution quality and search efficiency. The
effectiveness of the proposed method is validated by the application results on
the New England 39-bus power system and the southern power system of Hebei
province.
2DR: Towards Fine-Grained 2-D RFID Touch Sensing
Date: 2018-08-16 00:00:00
Tasks: TAG

In this paper, we introduce 2DR, a single RFID tag which can seamlessly sense
two-dimensional human touch using off-the-shelf RFID readers. Instead of using
a two-dimensional tag array to sense human finger touch on a surface, 2DR only
uses one or two RFID chip(s), which reduces the manufacturing cost and makes
the tag more suitable for printing on flexible materials. The key idea behind
2DR is to design a custom-shape antenna and classify human finger touch based
on unique phase information using statistical learning. We printed 2DR tag on
FR-4 substrate and use off-the-shelf UHF-RFID readers (FCC frequency band) to
sense different touch activities. Experiments show great potential of our
design. Moreover, 2DR can be further extended to 3D by building stereoscopic
model.
Vanadium dioxide circuits emulate neurological disorders
Date: 2018-08-27 00:00:00

Information in the central nervous system (CNS) is conducted via electrical
signals known as action potentials and is encoded in time. Several neurological
disorders including depression, Attention Deficit Hyperactivity Disorder
(ADHD), originate in faulty brain signaling frequencies. Here, we present a
Hodgkin-Huxley model analog for a strongly correlated VO2 artificial neuron
system that undergoes an electrically-driven insulator-metal transition. We
demonstrate that tuning of the insulating phase resistance in VO2 threshold
switch circuits can enable direct mimicry of neuronal origins of disorders in
the central nervous system. The results introduce use of circuits based on
quantum materials as complementary to model animal studies for neuroscience,
especially when precise measurements of local electrical properties or
competing parallel paths for conduction in complex neural circuits can be a
challenge to identify onset of breakdown or diagnose early symptoms of disease.