pwc-archive/papers-with-abstracts huggingface dataset:
Risk management of guaranteed minimum maturity benefits under stochastic mortality and regime-switching by Fourier space time-stepping framework
Authors: Wenlong Hu
Date: 2020-06-28 00:00:00
Tasks: Management

In this paper, we adopted a net liability model which assesses both market risk on the liability side and revenue risk on the asset side for a Guaranteed Minimum Maturity Benefit (GMMB) embedded in variable annuity (VA) contracts. Numeric solutions for net liabilities, fair rate of fees and Greeks of GMMB are obtained by a more accurate and fast Fourier Space time-stepping (FST) algorithm. Monte Carlo results are provided for comparative purpose. The unhedged and three statically hedged portfolios are introduced, and their performances are assessed by comparing the short term and long term portfolio's volatility. Recently, we noticed FST algorithm can only be used to hedge the gross liability of GMMB and it leads to an incorrect result when applying FST algorithm to the net liability model. We have modified the method we used in the hedging part and obtained the reliable result now. They will be reported in near future.
Development of an Algorithm for Identifying Changes in System Dynamics from Time Series
Authors: Ferdaus Kawsar, Mohammad Adibuzzaman
Date: 2020-06-28 00:00:00
Tasks: Time Series, Time Series Analysis

The development of an algorithm with related mathematical concepts and supporting hypothesis for detecting changes in system dynamics from time series along with empirical analysis and theoretical justification is presented. For the method, changes in the second largest eigenvalue of Markov Chain (SLEM) or mixing rate, is observed as an indicator of the changes in system dynamics. The Markov chain is created from empirical transition probabilities of a time series. The method is developed for the application of detecting hemorrhage from arterial blood pressure in anesthetized swine. The rationale of the change in the SLEM is investigated empirically with an artificial blood pressure model and, by studying correlations with other measures such as smoothness of time series, and density of the transition probability matrix of the Markov chain. The mathematical analysis shows that the change in the SLEM is a consequence of the change in the transition probabilities between different states and reflects information about the system dynamics.
Quantitative Statistical Robustness for Tail-Dependent Law Invariant Risk Measures
Authors: Wei Wang, Huifu Xu, Tiejun Ma
Date: 2020-06-28 00:00:00

When estimating the risk of a financial position with empirical data or Monte Carlo simulations via a tail-dependent law invariant risk measure such as the Conditional Value-at-Risk (CVaR), it is important to ensure the robustness of the statistical estimator particularly when the data contain noise. Kratscher et al. [1] propose a new framework to examine the qualitative robustness of estimators for tail-dependent law invariant risk measures on Orlicz spaces, which is a step further from earlier work for studying the robustness of risk measurement procedures by Cont et al. [2]. In this paper, we follow the stream of research to propose a quantitative approach for verifying the statistical robustness of tail-dependent law invariant risk measures. A distinct feature of our approach is that we use the Fortet-Mourier metric to quantify the variation of the true underlying probability measure in the analysis of the discrepancy between the laws of the plug-in estimators of law invariant risk measure based on the true data and perturbed data, which enables us to derive an explicit error bound for the discrepancy when the risk functional is Lipschitz continuous with respect to a class of admissible laws. Moreover, the newly introduced notion of Lipschitz continuity allows us to examine the degree of robustness for tail-dependent risk measures. Finally, we apply our quantitative approach to some well-known risk measures to illustrate our theory.
BER Analysis of RIS Assisted Multicast Communications with Network Coding
Authors: Vetrivel Chelian Thirumavalavan, PGS Velmurugan, Thiruvengadam S J
Date: 2020-06-28 00:00:00

In this paper, Recofigurable Intelligent Surface (RIS) assisted dual-hop multicast wireless communication network is proposed with two source nodes and two destination nodes. RIS boosts received signal strength through an intelligent software-controlled array of discrete phase-shifting metamaterials. The multicast communication from the source nodes is enabled using a Decode and Forward (DF) relay node. In the relay node, the Physical Layer Network Coding (PLNC) concept is applied and the PLNC symbol is transmitted to the destination nodes. The joint RIS-Multicast channels between source nodes and the relay node are modeled as the sum of two scaled non-central Chi-Square distributions. Analytical expressions are derived for Bit Error Rate (BER) at relay node and destination nodes using Moment Generating Function (MGF) approach and the results are validated using Monte-Carlo simulations. It is observed that the BER performance of the proposed RIS assisted network is a lot better than the conventional non-RIS channels links.
Design and Performance Analysis of a New STBC-MIMO LoRa System
Authors: Huan Ma, Guofa Cai, Yi Fang, Pingping Chen, Guojun Han
Date: 2020-06-28 00:00:00
Tasks: Diversity

LoRa is a modulation technology for low power wide area networks (LPWAN) with enormous potential in 5G era. However, the performance of LoRa system deteriorates seriously in fading-channel environments. To tackle this problem, in this paper we introduce multiple-input-multiple-output (MIMO) configuration employing space-time block coding (STBC) schemes into the LoRa system to formulate an STBC-MIMO LoRa system. Then, we investigate the theoretical performance of the proposed system over Rayleigh fading channels. To this end, we derive the distribution of the decision metric for the demodulator in the proposed system. Based on the above distribution, we propose the closed-form approximated bit error rate (BER) expression of the proposed system when perfect and imperfect channel information states (CSIs) are considered. In addition, we analyze the diversity order of the proposed system. The result demonstrates that the diversity order of the system in the imperfect CSI scenario with fixed channel estimate error variance is zero. However, in the imperfect CSI scenario with a decreasing channel estimate error variance and the perfect CSI scenario, the system can achieve full diversity. Finally, experimental results verify the accuracy of the theoretical analysis and the excellent performance of the proposed system. Due to such superiority, the proposed STBC-MIMO LoRa system can be considered as a good scheme for LPWAN.
Polymerase/nicking enzyme powered dual-template multi-cycled G-triplex machine for HIV-1 determination
Date: 2020-06-28 00:00:00

We proposed a dual-template multi-cycled DNA nanomachine driven by polymerase
nicking enzyme with high efficiency. The reaction system simply consists of two
templates (T1, T2) and two enzymes (KF polymerase, Nb.BbvCI). The two templates
are similar in structure (X-X-Y, Y-Y-C): primer recognition region, primer
analogue generation region, output region (3 to 5), and there is a nicking site
between each two regions. Output of T1 is the primer of T2 and G-rich fragment
(G3) is designed as the final products. In the presence of HIV-1, numerous of
G3 were generated owing to the multi-cycled amplification strategy and formed
into G-triplex ThT complex after the addition of thioflavin T (ThT), which
greatly enhanced the fluorescence intensity as signal reporter in the
label-free sensing strategy. A dynamic response range of 50 fM-2 nM for HIV-1
gene detection can be achieved through this multi-cycled G-triplex machine, and
benefit from the high efficiency amplification strategy, enzymatic reaction can
be completed within 45 minutes followed by fluorescence measurement. In
addition, analysis of other targets can be achieved by replacing the template
sequence. Thus there is a certain application potential for trace biomarker
analysis in this strategy.
Arbitrage concepts under trading restrictions in discrete-time financial markets
Authors: Claudio Fontana, Wolfgang J. Runggaldier
Date: 2020-06-28 00:00:00
Tasks: Portfolio Optimization

In a discrete-time setting, we study arbitrage concepts in the presence of convex trading constraints. We show that solvability of portfolio optimization problems is equivalent to absence of arbitrage of the first kind, a condition weaker than classical absence of arbitrage opportunities. We center our analysis on this characterization of market viability and derive versions of the fundamental theorems of asset pricing based on portfolio optimization arguments. By considering specifically a discrete-time setup, we simplify existing results and proofs that rely on semimartingale theory, thus allowing for a clear understanding of the foundational economic concepts involved. We exemplify these concepts, as well as some unexpected situations, in the context of one-period factor models with arbitrage opportunities under borrowing constraints.
Exploiting Diffuse Multipath in 5G SLAM
Authors: Yu Ge, Hyowon Kim, Fuxi Wen, Lennart Svensson, Sunwoo Kim, Henk Wymeersch
Date: 2020-06-28 00:00:00
Tasks: Simultaneous Localization and Mapping

5G millimeter wave (mmWave) signals can be used to jointly localize the receiver and map the propagation environment in vehicular networks, which is a typical simultaneous localization and mapping (SLAM) problem. Mapping the environment is challenging, due to measurements comprising both specular and diffuse multipath components, and diffuse multipath is usually considered as a perturbation. We here propose a novel method to utilize all available multipath signals from each landmark for mapping and incorporate this into a Poisson multi-Bernoulli mixture for the 5G SLAM problem. Simulation results demonstrate the efficacy of the proposed scheme.
RISE Controller Tuning and System Identification Through Machine Learning for Human Lower Limb Rehabilitation via Neuromuscular Electrical Stimulation
Authors: Héber H. Arcolezi, Willian R. B. M. Nunes, Rafael A. de Araujo, Selene Cerna, Marcelo A. A. Sanches, Marcelo C. M. Teixeira, Aparecido A. de Carvalho
Date: 2020-06-28 00:00:00

Neuromuscular electrical stimulation (NMES) has been effectively applied in many rehabilitation treatments of individuals with spinal cord injury (SCI). In this context, we introduce a novel, robust, and intelligent control-based methodology to closed-loop NMES systems. Our approach utilizes a robust control law to guarantee system stability and machine learning tools to optimize both the controller parameters and system identification. Regarding the latter, we introduce the use of past rehabilitation data to build more realistic data-driven identified models. Furthermore, we apply the proposed methodology for the rehabilitation of lower limbs using a control technique named the robust integral of the sign of the error (RISE), an offline improved genetic algorithm optimizer, and neural network models. Although in the literature, the RISE controller presented good results on healthy subjects, without any fine-tuning method, a trial and error approach would quickly lead to muscle fatigue for individuals with SCI. In this paper, for the first time, the RISE controller is evaluated with two paraplegic subjects in one stimulation session and with seven healthy individuals in at least two and at most five sessions. The results showed that the proposed approach provided a better control performance than empirical tuning, which can avoid premature fatigue on NMES-based clinical procedures.
A Real-Time Dispatching Strategy for Shared Automated Electric Vehicles with Performance Guarantees
Authors: Li Li, Theodoros Pantelidis, Joseph Y. J. Chow, Saif Eddin Jabari
Date: 2020-06-28 00:00:00
Tasks: Scheduling

Real-time vehicle dispatching operations in traditional car-sharing systems is an already computationally challenging scheduling problem. Electrification only exacerbates the computational difficulties as charge level constraints come into play. To overcome this complexity, we employ an online minimum drift plus penalty (MDPP) approach for SAEV systems that (i) does not require a priori knowledge of customer arrival rates to the different parts of the system (i.e. it is practical from a real-world deployment perspective), (ii) ensures the stability of customer waiting times, (iii) ensures that the deviation of dispatch costs from a desirable dispatch cost can be controlled, and (iv) has a computational time-complexity that allows for real-time implementation. Using an agent-based simulator developed for SAEV systems, we test the MDPP approach under two scenarios with real-world calibrated demand and charger distributions: 1) a low-demand scenario with long trips, and 2) a high-demand scenario with short trips. The comparisons with other algorithms under both scenarios show that the proposed online MDPP outperforms all other algorithms in terms of both reduced customer waiting times and vehicle dispatching costs.
Masking the general population might attenuate COVID-19 outbreaks
Date: 2020-06-28 00:00:00

The effect of masking the general population on a COVID-19 epidemic is
estimated by computer simulation using two separate state-of-the-art web-based
softwares, one of them calibrated for the SARS-CoV-2 virus. The questions
addressed are these: 1. Can mask use by the general population limit the spread
of SARS-CoV-2 in a country? 2. What types of masks exist, and how elaborate
must a mask be to be effective against COVID-19? 3. Does the mask have to be
applied early in an epidemic? 4. A brief general discussion of masks and some
possible future research questions regarding masks and SARS-CoV-2. Results are
as follows: (1) The results indicate that any type of mask, even simple
home-made ones, may be effective. Masks use seems to have an effect in lowering
new patients even the protective effect of each mask (here dubbed "one-mask
protection") is low. Strict adherence to mask use does not appear to be
critical. However, increasing the one-mask protection to > 50% was found to be
advantageous. Masks seemed able to reduce overflow of capacity, e.g. of
intensive care. As the default parameters of the software included another
intervention, it seems possible to combine mask and other interventions. (2)
Masks do seem to reduce the number of new cases even if introduced at a late
stage in an epidemic. However, early implementation helps reduce the cumulative
and total number of cases. (3) The simulations suggest that it might be
possible to eliminate a COVID-19 outbreak by widespread mask use during a
limited period. The results from these simulations are encouraging, but do not
necessarily represent the real-life situation, so it is suggested that clinical
trials of masks are now carried out while continuously monitoring effects and
side-effects.
How many can you infect? Simple (and naive) methods of estimating the reproduction number
Date: 2020-06-28 00:00:00

This is a pedagogical paper on estimating the number of people that can be
infected by one infectious person during an epidemic outbreak, known as the
reproduction number. Knowing the number is crucial for developing policy
responses. There are generally two types of such a number, i.e., basic and
effective (or instantaneous). While basic reproduction number is the average
expected number of cases directly generated by one case in a population where
all individuals are susceptible, effective reproduction number is the number of
cases generated in the current state of a population. In this paper, we exploit
the deterministic susceptible-infected-removed (SIR) model to estimate them
through three different numerical approximations. We apply the methods to the
pandemic COVID-19 in Italy to provide insights into the spread of the disease
in the country. We see that the effect of the national lockdown in slowing down
the disease exponential growth appeared about two weeks after the
implementation date. We also discuss available improvements to the simple (and
naive) methods that have been made by researchers in the field.
  Authors of this paper are members of the SimcovID (Simulasi dan Pemodelan
COVID-19 Indonesia) collaboration.
Pyroptosis: Physiological roles in viral infection
Authors: Nelson Duran, Wagner J. Favaro
Date: 2020-06-29 00:00:00

The current review focuses on important aspects of pyroptosis, such as morphological features of inflammasomes, as well as on knowledge about coronavirus infection mechanisms and the association between SARS-CoV-2 infection and cell pyroptosis. The application of immunomodulation therapies to Covid-19 patients is the most promising treatment under investigation nowadays. The possibilities of assistance to help protecting patients from infections are also addressed in the present study.
Treatment Effects in Interactive Fixed Effects Models with a Small Number of Time Periods
Authors: Brantly Callaway, Sonia Karami
Date: 2020-06-29 00:00:00

This paper considers identifying and estimating the Average Treatment Effect on the Treated (ATT) when untreated potential outcomes are generated by an interactive fixed effects model. That is, in addition to time-period and individual fixed effects, we consider the case where there is an unobserved time invariant variable whose effect on untreated potential outcomes may change over time and which can therefore cause outcomes (in the absence of participating in the treatment) to follow different paths for the treated group relative to the untreated group. The models that we consider in this paper generalize many commonly used models in the treatment effects literature including difference in differences and individual-specific linear trend models. Unlike the majority of the literature on interactive fixed effects models, we do not require the number of time periods to go to infinity to consistently estimate the ATT. Our main identification result relies on having the effect of some time invariant covariate (e.g., race or sex) not vary over time. Using our approach, we show that the ATT can be identified with as few as three time periods and with panel or repeated cross sections data.
Robust Product Markovian Quantization
Authors: Ralph Rudd, Thomas A. McWalter, Joerg Kienitz, Eckhard Platen
Date: 2020-06-29 00:00:00
Tasks: Quantization

Recursive marginal quantization (RMQ) allows the construction of optimal discrete grids for approximating solutions to stochastic differential equations in d-dimensions. Product Markovian quantization (PMQ) reduces this problem to d one-dimensional quantization problems by recursively constructing product quantizers, as opposed to a truly optimal quantizer. However, the standard Newton-Raphson method used in the PMQ algorithm suffers from numerical instabilities, inhibiting widespread adoption, especially for use in calibration. By directly specifying the random variable to be quantized at each time step, we show that PMQ, and RMQ in one dimension, can be expressed as standard vector quantization. This reformulation allows the application of the accelerated Lloyd's algorithm in an adaptive and robust procedure. Furthermore, in the case of stochastic volatility models, we extend the PMQ algorithm by using higher-order updates for the volatility or variance process. We illustrate the technique for European options, using the Heston model, and more exotic products, using the SABR model.
A Class of Random Binary Waveforms With Impulse-Like Autocorrelation
Authors: W. J. Szajnowski
Date: 2020-06-29 00:00:00

It is shown that a random binary process with impulse-like autocorrelation can be generated by randomizing the length of symbols occurring in a random Bernoulli process. Such randomization is achieved by random (or judiciously designed irregular) sampling of the output of a source that supplies a symmetric random binary waveform. One practical configuration implementing the proposed technique is a linear-feedback shift register driven by a spread-period clock generator.
Experimental measurements of a joint 5G-VLC communication for future vehicular networks
Authors: Dania Marabissi, Lorenzo Mucchi, Stefano Caputo, Francesca Nizzi, Tommaso Pecorella, Romano Fantacci, Tassadaq Nawaz, Marco Seminara, Jacopo Catani
Date: 2020-06-29 00:00:00

One of the main revolutionary features of 5G networks is the ultra-low latency that will enable new services such as those for the future smart vehicles. The 5G technology will be able to support extreme-low latency. Thanks to new technologies and the wide flexible architecture that integrates new spectra and access technologies. In particular, Visible Light Communication (VLC) is envisaged as a very promising technology for vehicular communications, since the information can flow by using the lights (as traffic-lights and car lights). This paper describes one of the first experiments on the joint use of 5G and VLC networks to provide real-time information to cars. The applications span from road safety to emergency alarm.
Prosodic Prominence and Boundaries in Sequence-to-Sequence Speech Synthesis
Date: 2020-06-29 00:00:00
Tasks: Sentence, Speech Synthesis

Recent advances in deep learning methods have elevated synthetic speech
quality to human level, and the field is now moving towards addressing prosodic
variation in synthetic speech.Despite successes in this effort, the
state-of-the-art systems fall short of faithfully reproducing local prosodic
events that give rise to, e.g., word-level emphasis and phrasal structure. This
type of prosodic variation often reflects long-distance semantic relationships
that are not accessible for end-to-end systems with a single sentence as their
synthesis domain. One of the possible solutions might be conditioning the
synthesized speech by explicit prosodic labels, potentially generated using
longer portions of text. In this work we evaluate whether augmenting the
textual input with such prosodic labels capturing word-level prominence and
phrasal boundary strength can result in more accurate realization of sentence
prosody. We use an automatic wavelet-based technique to extract such labels
from speech material, and use them as an input to a tacotron-like synthesis
system alongside textual information. The results of objective evaluation of
synthesized speech show that using the prosodic labels significantly improves
the output in terms of faithfulness of f0 and energy contours, in comparison
with state-of-the-art implementations.
Social Networks as a Mechanism for Discrimination
Authors: Chika O. Okafor
Date: 2020-06-26 00:00:00

I study labor markets in which firms hire via referrals. I develop an employment model showing that--despite initial equality in ability, employment, wages, and network structure--minorities receive fewer jobs through referral and lower expected wages, simply because their social group is smaller. This disparity, termed "social network discrimination," falls outside the dominant economics discrimination models--taste-based and statistical. Social network discrimination can be mitigated by minorities having more social ties or a "stronger-knit" network. I calibrate the model using a nationally-representative U.S. sample and estimate the lower-bound welfare gap caused by social network discrimination at over four percent, disadvantaging black workers.