pwc-archive/papers-with-abstracts huggingface dataset:
WFRFT-aided Power-efficient Multi-beam Directional Modulation Schemes Based on Frequency Diverse Array
Date: 2019-08-13 00:00:00

The artificial noise (AN) aided multi-beam directional modulation (DM)
technology is capable of wireless physical layer secure (PLS) transmissions for
multiple desired receivers in free space. The application of AN, however, makes
it less power-efficient for such a DM system. To address this problem, the
weighted fractional Fourier transform (WFRFT) technology is employed in this
paper to achieve power-efficient multi-beam DM transmissions. Specifically, a
power-efficient multi-beam WFRFT-DM scheme with cooperative receivers and a
power-efficient multi-beam WFRFT-DM scheme with independent receivers are
proposed based on frequency diverse array (FDA), respectively. The bit error
rate (BER), secrecy rate, and robustness of the proposed multi-beam WFRFT-DM
schemes are analyzed. Simulations demonstrate that 1) the proposed multi-beam
WFRFT-DM schemes are more power-efficient than the conventional multi-beam
AN-DM scheme; 2) the transmission security can also be guaranteed even if the
eavesdroppers are located close to or the same as the desired receivers; and 3)
the proposed multi-beam WFRFT-DM schemes are capable of independent
transmissions for different desired receivers with different modulations.
Semigroup models for biochemical reaction networks
Authors: Dimitri Loutchko
Date: 2019-08-13 00:00:00

The catalytic reaction system (CRS) formalism by Hordijk and Steel is a versatile method to model autocatalytic biochemical reaction networks. It is particularly suited, and has been widely used, to study self-sustainment and self-generation properties. Its distinguishing feature is the explicit assignment of a catalytic function to chemicals that are part of the system. In this work, it is shown that the subsequent and simultaneous catalytic functions gives rise to an algebraic structure of a semigroup with the additional compatible operation of idempotent addition and a partial order. The aim of this article is to demonstrate that such semigroup models are a natural setup to describe and analyze self-sustaining CRS. The basic algebraic properties of the models are established and the notion of the function of any set of chemicals on the whole CRS is made precise. This leads to a natural discrete dynamics on the network, which results from iteratively considering the self-action on a set of chemicals by its own function. The fixed points of this dynamics are proven to correspond to self-sustaining sets of chemicals, which are functionally closed. Finally, as the main application, a theorem on the maximal self-sustaining set and a structure theorem on the lattice of functionally closed self-sustaining sets of chemicals are proven.
A Two-Ray Multipath Model for Frequency Diverse Array-Based Directional Modulation in MISOME Wiretap Channels
Date: 2019-08-13 00:00:00

A two-ray multipath model for frequency diverse array (FDA)-based directional
modulation (DM) is proposed in multi-input single-output multi-eavesdropper
(MISOME) wiretap channels for the first time. The excitation factors of the FDA
and the weighting coefficients of the inserted artificial noise (AN) are
jointly designed in a way which imposes no impact on the desired receiver while
simultaneously distorting the received signals of eavesdroppers. Secrecy rate
is analyzed for the proposed two-ray multipath FDA-based DM model. Numerical
simulations verify the capability of physical layer secure (PLS) transmissions
of the proposed FDA-DM model in two-ray multipath MISOME wiretap channels.
On Range Sidelobe Reduction for Dual-functional Radar-Communication Waveforms
Date: 2019-08-13 00:00:00

In this paper, we propose a novel waveform design for multi-input
multi-output (MIMO) dual-functional radar-communication systems by taking the
range sidelobe control into consideration. In particular, we focus on
optimizing the weighted summation of communication and radar metrics under
per-antenna power budget. While the formulated optimization problem is
non-convex, we develop a first-order descent algorithm by exploiting the
manifold structure of its feasible region, which finds a near-optimal solution
within a low computational overhead. Numerical results show that the proposed
waveform design outperforms the conventional techniques by improving the
communication rate while reducing the range sidelobe level.
The classification of term structure shapes in the two-factor Vasicek model -- a total positivity approach
Authors: Martin Keller-Ressel
Date: 2019-08-13 00:00:00

We provide a full classification of all attainable term structure shapes in the two-factor Vasicek model of interest rates. In particular, we show that the shapes normal, inverse, humped, dipped and hump-dip are always attainable. In certain parameter regimes up to four additional shapes can be produced. Our results apply to both forward and yield curves and show that the correlation and the difference in mean-reversion speeds of the two factor processes play a key role in determining the scope of attainable shapes. The key mathematical tool is the theory of total positivity, pioneered by Samuel Karlin and others in the 1950ies.
Estimating & Mitigating the Impact of Acoustic Environments on Machine-to-Machine Signalling
Date: 2019-08-13 00:00:00
Tasks: Decoder

The advance of technology for transmitting Data-over-Sound in various IoT and
telecommunication applications has led to the concept of machine-to-machine
over-the-air acoustic signalling. Reverberation can have a detrimental effect
on such machine-to-machine signals while decoding. Various methods have been
studied to combat the effects of reverberation in speech and audio signals, but
it is not clear how well they generalise to other sound types. We look at
extending these models to facilitate machine-to-machine acoustic signalling.
This research investigates dereverberation techniques to shortlist a
single-channel reverberation suppression method through a pilot test. In order
to apply the chosen dereverberation method a novel method of estimating
acoustic parameters governing reverberation is proposed. The performance of the
final algorithm is evaluated on quality metrics as well as the performance of a
real machine-to-machine decoder. We demonstrate a dramatic reduction in error
rate for both audible and ultrasonic signals.
Critical Decisions for Asset Allocation via Penalized Quantile Regression
Authors: Giovanni Bonaccolto
Date: 2019-08-13 00:00:00
Tasks: quantile regression, regression

We extend the analysis of investment strategies derived from penalized quantile regression models, introducing alternative approaches to improve state\textendash of\textendash art asset allocation rules. First, we use a post\textendash penalization procedure to deal with overshrinking and concentration issues. Second, we investigate whether and to what extent the performance changes when moving from convex to nonconvex penalty functions. Third, we compare different methods to select the optimal tuning parameter which controls the intensity of the penalization. Empirical analyses on real\textendash world data show that these alternative methods outperform the simple LASSO. This evidence becomes stronger when focusing on the extreme risk, which is strictly linked to the quantile regression method.
Distributed Estimation in the Presence of Strategic Data Sources
Date: 2019-08-13 00:00:00

Distributed estimation that recruits potentially large groups of humans to
collect data about a phenomenon of interest has emerged as a paradigm
applicable to a broad range of detection and estimation tasks. However, it also
presents a number of challenges especially with regard to user participation
and data quality, since the data resources may be strategic human agents
instead of physical sensors. We consider a static estimation problem in which
an estimator collects data from self-interested agents. Since it incurs cost to
participate, mechanisms to incentivize the agents to collect and transmit data
of desired quality are needed. Agents are strategic in the sense that they can
take measurement with different levels of accuracy by expending different
levels of effort. They may also misreport their information in order to obtain
greater compensation, if possible. With both the measurements from the agents
and their accuracy unknown to the estimator, we design incentive mechanisms
that encourage desired behavior from strategic agents. Specifically, we solve
an optimization problem at the estimator which minimizes the expected total
compensation to the agents while guaranteeing a specified quality of the global
estimate.
Boolean constraint satisfaction problems for reaction networks
Date: 2019-08-13 00:00:00

This Thesis presents research at the boundary between Statistical Physics and
Biology. First, we have devised a class of Boolean constraint satisfaction
problems (CSP) whose solutions describe the feasible operational states of a
chemical reaction network. After developing statistical mechanics techniques to
generate solutions and studying the properties of the solution space for both
ensembles and individual instances of random reaction networks, we have applied
this framework to the metabolic network of the bacterium E.Coli. Results
highlight, on one hand, a complex organization of operational states into
"modules" involving different biochemically-defined pathways, and, on the
other, a high degree of cross-talk between modules. In summary, we propose that
this class of CSPs may provide novel and useful quantitative information
linking structure to function in cellular reaction networks.
RTF-steered binaural MVDR beamforming incorporating multiple external microphones
Date: 2019-08-13 00:00:00

The binaural minimum-variance distortionless-response (BMVDR) beamformer is a
well-known noise reduction algorithm that can be steered using the relative
transfer function (RTF) vector of the desired speech source. Exploiting the
availability of an external microphone that is spatially separated from the
head-mounted microphones, an efficient method has been recently proposed to
estimate the RTF vector in a diffuse noise field. When multiple external
microphones are available, different RTF vector estimates can be obtained by
using this method for each external microphone. In this paper, we propose
several procedures to combine these RTF vector estimates, either by selecting
the estimate corresponding to the highest input SNR, by averaging the estimates
or by combining the estimates in order to maximize the output SNR of the BMVDR
beamformer. Experimental results for a moving speaker and diffuse noise in a
reverberant environment show that the output SNR-maximizing combination yields
the largest binaural SNR improvement and also outperforms the state-of-the art
covariance whitening method.
Intelligent Reflecting Surface Aided MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer
Date: 2020-02-18 00:00:00

An intelligent reflecting surface (IRS) is invoked for enhancing the energy
harvesting performance of a simultaneous wireless information and power
transfer (SWIPT) aided system. Specifically, an IRS-assisted SWIPT system is
considered, where a multi-antenna aided base station (BS) communicates with
several multi-antenna assisted information receivers (IRs), while guaranteeing
the energy harvesting requirement of the energy receivers (ERs). To maximize
the weighted sum rate (WSR) of IRs, the transmit precoding (TPC) matrices of
the BS and passive phase shift matrix of the IRS should be jointly optimized.
To tackle this challenging optimization problem, we first adopt the classic
block coordinate descent (BCD) algorithm for decoupling the original
optimization problem into several subproblems and alternatively optimize the
TPC matrices and the phase shift matrix. For each subproblem, we provide a
low-complexity iterative algorithm, which is guaranteed to converge to the
Karush-Kuhn-Tucker (KKT) point of each subproblem. The BCD algorithm is
rigorously proved to converge to the KKT point of the original problem. We also
conceive a feasibility checking method to study its feasibility. Our extensive
simulation results confirm that employing IRSs in SWIPT beneficially enhances
the system performance and the proposed BCD algorithm converges rapidly, which
is appealing for practical applications.
Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics
Date: 2020-04-16 00:00:00
Tasks: Anatomy, Uncertainty Quantification

Standard approaches for uncertainty quantification in cardiovascular modeling
pose challenges due to the large number of uncertain inputs and the significant
computational cost of realistic three-dimensional simulations. We propose an
efficient uncertainty quantification framework utilizing a multilevel
multifidelity Monte Carlo estimator to improve the accuracy of hemodynamic
quantities of interest while maintaining reasonable computational cost. This is
achieved by leveraging three cardiovascular model fidelities, each with varying
spatial resolution to rigorously quantify the variability in hemodynamic
outputs. We employ two low-fidelity models to construct several different
estimators. Our goal is to investigate and compare the efficiency of estimators
built from combinations of these low-fidelity and high-fidelity models. We
demonstrate this framework on healthy and diseased models of aortic and
coronary anatomy, including uncertainties in material property and boundary
condition parameters. We seek to demonstrate that for this application it is
possible to accelerate the convergence of the estimators by utilizing a MLMF
paradigm. Therefore, we compare our approach to Monte Carlo and multilevel
Monte Carlo estimators based only on three-dimensional simulations. We
demonstrate significant reduction in total computational cost with the MLMF
estimators. We also examine the differing properties of the MLMF estimators in
healthy versus diseased models, as well as global versus local quantities of
interest. As expected, global quantities and healthy models show larger
reductions than local quantities and diseased model, as the latter rely more
heavily on the highest fidelity model evaluations. In all cases, our workflow
coupling Dakota's MLMF estimators with the SimVascular cardiovascular modeling
framework makes uncertainty quantification feasible for constrained
computational budgets.
Compact Finite Difference Scheme with Hermite Interpolation for Pricing American Put Options Based on Regime Switching Model
Authors: Chinonso Nwankwo, Weizhong Dai, Ruihua Liu
Date: 2019-08-14 00:00:00

We consider a system of coupled free boundary problems for pricing American put options with regime-switching. To solve this system, we first employ the logarithmic transformation to map the free boundary for each regime to multi-fixed intervals and then eliminate the first-order derivative in the transformed model by taking derivatives to obtain a system of partial differential equations which we call the asset-delta-gamma-speed equations. As such, the fourth-order compact finite difference scheme can be used for solving this system. The influence of other asset, delta, gamma, and speed options in the present regime is estimated based on Hermite interpolations. Finally, the numerical method is tested with several examples. Our results show that the scheme provides an accurate solution that is fast in computation as compared with other existing numerical methods.
Computational method for probability distribution on recursive relationships in financial applications
Authors: Jong Jun Park, Kyungsub Lee
Date: 2019-08-14 00:00:00

In quantitative finance, it is often necessary to analyze the distribution of the sum of specific functions of observed values at discrete points of an underlying process. Examples include the probability density function, the hedging error, the Asian option, and statistical hypothesis testing. We propose a method to calculate such a distribution, utilizing a recursive method, and examine it using various examples. The results of the numerical experiment show that our proposed method has high accuracy.
Can robust optimization offer improved portfolio performance?: An empirical study of Indian market
Authors: Shashank Oberoi, Mohammed Bilal Girach, Siddhartha P. Chakrabarty
Date: 2019-08-14 00:00:00

The emergence of robust optimization has been driven primarily by the necessity to address the demerits of the Markowitz model. There has been a noteworthy debate regarding consideration of robust approaches as superior or at par with the Markowitz model, in terms of portfolio performance. In order to address this skepticism, we perform empirical analysis of three robust optimization models, namely the ones based on box, ellipsoidal and separable uncertainty sets. We conclude that robust approaches can be considered as a viable alternative to the Markowitz model, not only in simulated data but also in a real market setup, involving the Indian indices of S&P BSE 30 and S&P BSE 100. Finally, we offer qualitative and quantitative justification regarding the practical usefulness of robust optimization approaches from the point of view of number of stocks, sample size and types of data.
Third person enforcement in a prisoner's dilemma game
Date: 2019-08-14 00:00:00

We theoretically study the effect of a third person enforcement on a one-shot
prisoner's dilemma game played by two persons, with whom the third person plays
repeated prisoner's dilemma games. We find that the possibility of the third
person's future punishment causes them to cooperate in the one-shot game.
Is being `Robust' beneficial?: A perspective from the Indian market
Authors: Mohammed Bilal Girach, Shashank Oberoi, Siddhartha P. Chakrabarty
Date: 2019-08-14 00:00:00
Tasks: Portfolio Optimization

The problem of data uncertainty has motivated the incorporation of robust optimization in various arenas, beyond the Markowitz portfolio optimization. This work presents the extension of the robust optimization framework for the minimization of downside risk measures, such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). We perform an empirical study of VaR and CVaR frameworks, with respect to their robust counterparts, namely, Worst-Case VaR and Worst-Case CVaR, using the market data as well as the simulated data. After discussing the practical usefulness of the robust optimization approaches from various standpoints, we infer various takeaways. The robust models in the case of VaR and CVaR minimization exhibit superior performance with respect to their base versions in the cases involving higher number of stocks and simulated setup respectively.
Modeling microstructure price dynamics with symmetric Hawkes and diffusion model using ultra-high-frequency stock data
Authors: Kyungsub Lee, Byoung Ki Seo
Date: 2019-08-14 00:00:00

This study examine the theoretical and empirical perspectives of the symmetric Hawkes model of the price tick structure. Combined with the maximum likelihood estimation, the model provides a proper method of volatility estimation specialized in ultra-high-frequency analysis. Empirical studies based on the model using the ultra-high-frequency data of stocks in the S\&P 500 are performed. The performance of the volatility measure, intraday estimation, and the dynamics of the parameters are discussed. A new approach of diffusion analogy to the symmetric Hawkes model is proposed with the distributional properties very close to the Hawkes model. As a diffusion process, the model provides more analytical simplicity when computing the variance formula, incorporating skewness and examining the probabilistic property. An estimation of the diffusion model is performed using the simulated maximum likelihood method and shows similar patterns to the Hawkes model.
Performance of tail hedged portfolio with third moment variation swap
Authors: Kyungsub Lee, Byoung Ki Seo
Date: 2019-08-14 00:00:00

The third moment variation of a financial asset return process is defined by the quadratic covariation between the return and square return processes. The skew and fat tail risk of an underlying asset can be hedged using a third moment variation swap under which a predetermined fixed leg and the floating leg of the realized third moment variation are exchanged. The probability density function of the hedged portfolio with the third moment variation swap was examined using a partial differential equation approach. An alternating direction implicit method was used for numerical analysis of the partial differential equation. Under the stochastic volatility and jump diffusion stochastic volatility models, the distributions of the hedged portfolio return are symmetric and have more Gaussian-like thin-tails.
Investigation of the impact of PTMs on the protein backbone conformation
Date: 2019-08-14 00:00:00

Post-Translational Modifications (PTMs) are known to play a critical role in
the regulation of the protein functions. Their impact on protein structures,
and their link to disorder regions have already been spotted on the past
decade. Nonetheless, the high diversity of PTMs types, and the multiple schemes
of protein modifications (multiple PTMs, of different types, at different time,
etc) make difficult the direct confrontation of PTM annotations and protein
structures data.We so analyzed the impact of the residue modifications on the
protein structures at local level. Thanks to a dedicated structure database,
namely PTM-SD, a large screen of PTMs have been done and analyze at a local
protein conformation levels using the structural alphabet Protein Blocks (PBs).
We investigated the relation between PTMs and the backbone conformation of
modified residues, of their local environment, and at the level of the complete
protein structure. The two main PTM types (N-glycosylation and phosphorylation)
have been studied in non-redundant datasets, and then, 4 different proteins
were focused, covering 3 types of PTMs: N-glycosylation in renin endopeptidase
and liver carboxylesterase, phosphorylation in cyclin-dependent kinase 2
(CDK2), and methylation in actin. We observed that PTMs could either stabilize
or destabilize the backbone structure, at a local and global scale, and that
these effects depend on the PTM types.
New Results on Parameter Estimation via Dynamic Regressor Extension and Mixing: Continuous and Discrete-time Cases
Date: 2019-08-14 00:00:00
Tasks: parameter estimation, regression

We present some new results on the dynamic regressor extension and mixing
parameter estimators for linear regression models recently proposed in the
literature. This technique has proven instrumental in the solution of several
open problems in system identification and adaptive control. The new results
include: (i) a unified treatment of the continuous and the discrete-time cases;
(ii) the proposal of two new extended regressor matrices, one which guarantees
a quantifiable transient performance improvement, and the other exponential
convergence under conditions that are strictly weaker than regressor
persistence of excitation; and (iii) an alternative estimator ensuring
parameter estimation in finite-time that retains its alertness to track
time-varying parameters. Simulations that illustrate our results are also
presented.