pwc-archive/papers-with-abstracts huggingface dataset:
Lyapunov Analysis of Least Squares Based Direct Adaptive Control
Authors: Nursefa Zengin, Baris Fidan, Ladan Khoshnevisan
Date: 2020-07-16 00:00:00

Adaptive control strategies usually are designed based on gradient methods for the sake of simplicity in Lyapunov analysis. However, least squares (LS)-based parameter identifiers, with proper selection of design parameters, exhibit better transient performance than the gradient-based ones, from the aspects of convergence speed and robustness to measurement noise. On the other hand, most of the LS-based adaptive control procedures are designed via the indirect adaptive control approaches, due to the difficulty in integrating an LS-based adaptive law within the direct approaches starting with a certain Lyapunov-like cost function to be driven to (a neighborhood of) zero. In this paper, a formal constructive analysis framework is proposed to integrate the recursive LS-based parameter identification with direct adaptive control. To this end, a Lyapunov-like function is proposed for the analysis to achieve adaptive laws, which guarantee the exponential convergence of the parameters. Application of the proposed procedure in adaptive cruise control design is studied through Matlab/Simulink and CarSim simulations, validating the analytical results.
Real-time Framework for Trust Monitoring in aNetwork of Unmanned Aerial Vehicles
Authors: Mahsa Keshavarz, Alireza Shamsoshoara, Fatemeh Afghah, Jonathan Ashdown
Date: 2020-07-16 00:00:00

Unmanned aerial vehicles (UAVs) have been increasingly utilized in various civilian and military applications such as remote sensing, border patrolling, disaster monitoring, and communication coverage extension. However, there are still prone to several cyber attacks such as GPS spoofing attacks, distributed denial-of-service (DDoS) attacks, and man-in-the-middle attacks to obtain their collected information or to enforce the UAVs to perform their requested actions which may damage the UAVs or their surrounding environment or even endanger the safety of human in the operation field. In this paper, we propose a trust monitoring mechanism in which a centralized unit (e.g. the ground station) regularly observe the behavior of the UAVs in terms of their motion path, their consumed energy, as well as the number of their completed tasks and measure a relative trust score for the UAVs to detect any abnormal behaviors in a real-time manner. Our simulation results show that the trust model can detect malicious UAVs, which can be under various cyber-security attacks such as flooding attacks, man-in-the-middle attacks, GPS spoofing attack in real-time.
On Mitigating the Uncertainty in Renewable Generation in Distribution Microgrids
Date: 2020-07-16 00:00:00

In this article, we focus on the problem of mitigating the risk of not being
able to meet the power demand, due to the inherent uncertainty of renewable
energy generation sources in microgrids. We consider three different demand
scenarios, namely meeting short-time horizon power demand, a sustained energy
demand and a scenario where the power demand at a prescribed future time has to
be met with almost sure guarantee with power generation being stochastic and
following dynamics governed by geometric Brownian motion. For each of these
scenarios we provide solutions to meet the electrical demand. We present
results of numerical experiments to demonstrate the applicability of our
schemes.
True-Time-Delay Arrays for Fast Beam Training in Wideband Millimeter-Wave Systems
Authors: Veljko Boljanovic, Han Yan, Chung-Ching Lin, Soumen Mohapatra, Deukhyoun Heo, Subhanshu Gupta, Danijela Cabric
Date: 2020-07-17 00:00:00

The best beam steering directions are estimated through beam training, which is one of the most important and challenging tasks in millimeter-wave and sub-terahertz communications. Novel array architectures and signal processing techniques are required to avoid prohibitive beam training overhead associated with large antenna arrays and narrow beams. In this work, we leverage recent developments in true-time-delay (TTD) arrays with large delay-bandwidth products to accelerate beam training using frequency-dependent probing beams. We propose and study two TTD architecture candidates, including analog and hybrid analog-digital arrays, that can facilitate beam training with only one wideband pilot. We also propose a suitable algorithm that requires a single pilot to achieve high-accuracy estimation of angle of arrival. The proposed array architectures are compared in terms of beam training requirements and performance, robustness to practical hardware impairments, and power consumption. The findings suggest that the analog and hybrid TTD arrays achieve a sub-degree beam alignment precision with 66% and 25% lower power consumption than a fully digital array, respectively. Our results yield important design trade-offs among the basic system parameters, power consumption, and accuracy of angle of arrival estimation in fast TTD beam training.
High Altitude Platform Station based Super Macro Base Station (HAPS-SMBS) Constellations
Authors: Md Sahabul Alam, Gunes Karabulut Kurt, Halim Yanikomeroglu, Peiying Zhu, Ngoc Dũng Đào
Date: 2020-07-17 00:00:00

High altitude platform station (HAPS) systems have recently attracted renewed attention. While terrestrial and satellite technologies are well-established for providing connectivity services, they face certain shortcomings and challenges, which could be addressed by complementing them with HAPS systems. In this paper, we envision a HAPS as a super macro base station, which we refer to as HAPS-SMBS, to provide connectivity in a plethora of applications. Unlike a conventional HAPS, which targets broad coverage for remote areas or disaster recovery, we envision next-generation HAPS-SMBS to have the necessary capabilities to address the high capacity, low latency, and computing requirements especially for highly populated metropolitan areas. This article focuses mainly on the potential opportunities, target use cases, and challenges that we expect to be associated with the design and implementation of the HAPS-SMBS based future wireless access architecture.
Distinguishing level-1 phylogenetic networks on the basis of data generated by Markov processes
Authors: Elizabeth Gross, Leo van Iersel, Remie Janssen, Mark Jones, Colby Long, Yukihiro Murakami
Date: 2020-07-17 00:00:00

Phylogenetic networks can represent evolutionary events that cannot be described by phylogenetic trees. These networks are able to incorporate reticulate evolutionary events such as hybridization, introgression, and lateral gene transfer. Recently, network-based Markov models of DNA sequence evolution have been introduced along with model-based methods for reconstructing phylogenetic networks. For these methods to be consistent, the network parameter needs to be identifiable from data generated under the model. Here, we show that the semi-directed network parameter of a triangle-free, level-1 network model with any fixed number of reticulation vertices is generically identifiable under the Jukes-Cantor, Kimura 2-parameter, or Kimura 3-parameter constraints.
Pricing equity-linked life insurance contracts with multiple risk factors by neural networks
Authors: Karim Barigou, Lukasz Delong
Date: 2020-07-17 00:00:00

This paper considers the pricing of equity-linked life insurance contracts with death and survival benefits in a general model with multiple stochastic risk factors: interest rate, equity, volatility, unsystematic and systematic mortality. We price the equity-linked contracts by assuming that the insurer hedges the risks to reduce the local variance of the net asset value process and requires a compensation for the non-hedgeable part of the liability in the form of an instantaneous standard deviation risk margin. The price can then be expressed as the solution of a system of non-linear partial differential equations. We reformulate the problem as a backward stochastic differential equation with jumps and solve it numerically by the use of efficient neural networks. Sensitivity analysis is performed with respect to initial parameters and an analysis of the accuracy of the approximation of the true price with our neural networks is provided.
Adjusted Expected Shortfall
Authors: Matteo Burzoni, Cosimo Munari, Ruodu Wang
Date: 2020-07-17 00:00:00
Tasks: Position

We introduce and study the main properties of a class of convex risk measures that refine Expected Shortfall by simultaneously controlling the expected losses associated with different portions of the tail distribution. The corresponding adjusted Expected Shortfalls quantify risk as the minimum amount of capital that has to be raised and injected into a financial position $X$ to ensure that Expected Shortfall $ES_p(X)$ does not exceed a pre-specified threshold $g(p)$ for every probability level $p\in[0,1]$. Through the choice of the benchmark risk profile $g$ one can tailor the risk assessment to the specific application of interest. We devote special attention to the study of risk profiles defined by the Expected Shortfall of a benchmark random loss, in which case our risk measures are intimately linked to second-order stochastic dominance.
On the Complexity of Quadratization for Polynomial Differential Equations
Authors: Mathieu Hemery, François Fages, Sylvain Soliman
Date: 2020-07-17 00:00:00

Chemical reaction networks (CRNs) are a standard formalism used in chemistry and biology to reason about the dynamics of molecular interaction networks. In their interpretation by ordinary differential equations, CRNs provide a Turing-complete model of analog computattion, in the sense that any computable function over the reals can be computed by a finite number of molecular species with a continuous CRN which approximates the result of that function in one of its components in arbitrary precision. The proof of that result is based on a previous result of Bournez et al. on the Turing-completeness of polyno-mial ordinary differential equations with polynomial initial conditions (PIVP). It uses an encoding of real variables by two non-negative variables for concentrations, and a transformation to an equivalent quadratic PIVP (i.e. with degrees at most 2) for restricting ourselves to at most bimolecular reactions. In this paper, we study the theoretical and practical complexities of the quadratic transformation. We show that both problems of minimizing either the number of variables (i.e., molecular species) or the number of monomials (i.e. elementary reactions) in a quadratic transformation of a PIVP are NP-hard. We present an encoding of those problems in MAX-SAT and show the practical complexity of this algorithm on a benchmark of quadratization problems inspired from CRN design problems.
Initialization of a Disease Transmission Model
Date: 2020-07-17 00:00:00
Tasks: model, State Estimation

Approaches to the calculation of the full state vector of a larger
epidemiological model for the spread of COVID-19 in Sweden at the initial time
instant from available data and with a simplified dynamical model are proposed
and evaluated. The larger epidemiological model is based on a continuous Markov
chain and captures the demographic composition of and the transport flows
between the counties of Sweden. Its intended use is to predict the outbreak
development in temporal and spatial coordinates as well as across the
demographic groups. It can also support evaluating and comparing of prospective
intervention strategies in terms of e.g. lockdown in certain areas or isolation
of specific age groups. The simplified model is a discrete time-invariant
linear system that has cumulative infectious incidence, infected population,
asymptomatic population, exposed population, and infectious pressure as the
state variables. Since the system matrix of the model depends on a number
transition rates, structural properties of the model are investigated for
suitable parameter ranges. It is concluded that the model becomes unobservable
for some parameter values. Two contrasting approaches to the initial state
estimation are considered. One is a version of Rauch-Tung-Striebel smoother and
another is based on solving a batch nonlinear optimization problem. The
benefits and shortcomings of the considered estimation techniques are analyzed
and compared on synthetic data for several Swedish counties.
Low Complexity Reconfigurable Modified FRM Architecture with Full Spectral Utilization for Efficient Channelizers
Authors: Parvathi A. K., V. Sakthivel
Date: 2020-07-17 00:00:00

This paper proposes a design of low complexity, reconfigurable and narrow transition band (NTB) filter bank (FB). In our proposed Modified Frequency Response Masking (ModFRM) architecture, the modal filter and complementary filter in conventional FRM approach are replaced by a power complementary and linear phase FB. Additionally, a new masking strategy is proposed by which an M-channel FB can be designed by alternately masking even channels and odd channels. By using this masking strategy, it is only necessary to alternately mask even channels and odd channels by the two masking filters while modulating them over the multiple spectra replicas appearing in [0,2$\pi$] to generate uniform ModFRM FB. Also, reconfigurability of filter bank can be achieved by adjusting the interpolation values appropriately, to obtain more masking responses. To reduce the overall implementation complexity, masking filters are optimized using the interpolated FIR (IFIR) technique. The results indicate that the proposed method requires substantially less multipliers in comparison to the reconfigurable FB existing in literature. Finally, non-uniform FB are generated from the uniform FB by combining nearby channels. The proposed non-uniform ModFRM FB is used for the extraction of different communication standards in the software defined radio (SDR) channelizer. When more channels are to be extracted, the proposed scheme was able to achieve a reduced hardware complexity in comparison to other filter bank based SDR channelizers.
Estimation and uncertainty quantification for piecewise smooth signal recovery
Authors: Victor Churchill, Anne Gelb
Date: 2020-07-17 00:00:00
Tasks: Uncertainty Quantification

This paper presents a sparse Bayesian learning (SBL) algorithm for linear inverse problems with a high order total variation (HOTV) sparsity prior. For the problem of sparse signal recovery, SBL often produces more accurate estimates than maximum a posteriori estimates, including those that rely on l1 regularization. Moreover, rather than a single signal estimate, SBL yields a full posterior density estimate which can be used for uncertainty quantification. However, SBL is only immediately applicable to problems having a direct sparsity prior, or to those that can be formed via synthesis. This paper demonstrates how a problem with an HOTV sparsity prior can be formulated via synthesis, and then develops a corresponding Bayesian learning method. This expands the class of problems available to Bayesian learning to include, e.g., inverse problems dealing with the recovery of piecewise smooth functions or signals from data. Numerical examples are provided to demonstrate how this new technique is effectively employed.
Estimation of time-varying kernel densities and chronology of the impact of COVID-19 on financial markets
Authors: Matthieu Garcin, Jules Klein, Sana Laaribi
Date: 2020-07-17 00:00:00
Tasks: Density Estimation

The time-varying kernel density estimation relies on two free parameters: the bandwidth and the discount factor. We propose to select these parameters so as to minimize a criterion consistent with the traditional requirements of the validation of a probability density forecast. These requirements are both the uniformity and the independence of the so-called probability integral transforms, which are the forecast time-varying cumulated distributions applied to the observations. We thus build a new numerical criterion incorporating both the uniformity and independence properties by the mean of an adapted Kolmogorov-Smirnov statistic. We apply this method to financial markets during the COVID-19 crisis. We determine the time-varying density of daily price returns of several stock indices and, using various divergence statistics, we are able to describe the chronology of the crisis as well as regional disparities. For instance, we observe a more limited impact of COVID-19 on financial markets in China, a strong impact in the US, and a slow recovery in Europe.
Similarity quantification for linear stochastic systems: A coupling compensator approach
Authors: B. C. van Huijgevoort, S. Haesaert
Date: 2020-07-17 00:00:00

For the formal verification and design of control systems, abstractions with quantified accuracy are crucial. This is especially the case when considering accurate deviation bounds between a stochastic continuous-state model and its finite (reduced-order) abstraction. In this work, we introduce a coupling compensator to parameterize the set of relevant couplings and we give a comprehensive computational approach and analysis for linear stochastic systems. More precisely, we develop a computational method that characterizes the set of possible simulation relations and gives a trade-off between the error contributions on the systems output and deviations in the transition probability. We show the effect of this error trade-off on the guaranteed satisfaction probability for case studies where a formal specification is given as a temporal logic formula.
Modelling cellular signalling variability based on single-cell data: the TGFb/SMAD signaling pathway
Authors: Uddipan Sarma, Lorenz Hexemer, Uchenna Alex Anyaegbunam, Stefan Legewie
Date: 2020-07-17 00:00:00

Non-genetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFb/SMAD signaling pathway.
SkipConvNet: Skip Convolutional Neural Network for Speech Dereverberation using Optimally Smoothed Spectral Mapping
Authors: Vinay Kothapally, Wei Xia, Shahram Ghorbani, John H. L. Hansen, Wei Xue, Jing Huang
Date: 2020-07-17 00:00:00
Tasks: Decoder, Speaker Verification, Speech Dereverberation, speech-recognition, Speech Recognition

The reliability of using fully convolutional networks (FCNs) has been successfully demonstrated by recent studies in many speech applications. One of the most popular variants of these FCNs is the `U-Net', which is an encoder-decoder network with skip connections. In this study, we propose `SkipConvNet' where we replace each skip connection with multiple convolutional modules to provide decoder with intuitive feature maps rather than encoder's output to improve the learning capacity of the network. We also propose the use of optimal smoothing of power spectral density (PSD) as a pre-processing step, which helps to further enhance the efficiency of the network. To evaluate our proposed system, we use the REVERB challenge corpus to assess the performance of various enhancement approaches under the same conditions. We focus solely on monitoring improvements in speech quality and their contribution to improving the efficiency of back-end speech systems, such as speech recognition and speaker verification, trained on only clean speech. Experimental findings show that the proposed system consistently outperforms other approaches.
A Novel Spectrally-Efficient Uplink Hybrid-Domain NOMA System
Authors: Chen Quan, Animesh Yadav, Baocheng Geng, Pramod K. Varshney, H. Vincent Poor
Date: 2020-07-17 00:00:00
Tasks: Clustering

This paper proposes a novel hybrid-domain (HD) non-orthogonal multiple access (NOMA) approach to support a larger number of uplink users than the recently proposed code-domain NOMA approach, i.e., sparse code multiple access (SCMA). HD-NOMA combines the code-domain and power-domain NOMA schemes by clustering the users in small path loss (strong) and large path loss (weak) groups. The two groups are decoded using successive interference cancellation while within the group users are decoded using the message passing algorithm. To further improve the performance of the system, a spectral-efficiency maximization problem is formulated under a user quality-of-service constraint, which dynamically assigns power and subcarrier to the users. The problem is non-convex and has sparsity constraints. The alternating optimization procedure is used to solve it iteratively. We apply successive convex approximation and reweighted $\ell_1$ minimization approaches to deal with the non-convexity and sparsity constraints, respectively. The performance of the proposed HD-NOMA is evaluated and compared with the conventional SCMA scheme through numerical simulation. The results show the potential of HD-NOMA in increasing the number of uplink users.
Bond indifference prices and indifference yield curves
Authors: Matthew Lorig
Date: 2020-07-17 00:00:00

In a market with stochastic interest rates, we consider an investor who can either (i) invest all if his money in a savings account or (ii) purchase zero-coupon bonds and invest the remainder of his wealth in a savings account. The indifference price of the bond is the price for which the investor could achieve the same expected utility under both scenarios. In an affine term structure setting, under the assumption that an investor has a utility function in either exponential or power form, we show that the indifference price of a zero-coupon bond is the root of an integral expression. As an example, we compute bond indifference prices and the corresponding indifference yield curves in the Vasicek setting and interpret the results.
How Flexible is that Functional Form? Quantifying the Restrictiveness of Theories
Authors: Drew Fudenberg, Wayne Gao, Annie Liang
Date: 2020-07-17 00:00:00
Tasks: Form

We propose a restrictiveness measure for economic models based on how well they fit synthetic data from a pre-defined class. This measure, together with a measure for how well the model fits real data, outlines a Pareto frontier, where models that rule out more regularities, yet capture the regularities that are present in real data, are preferred. To illustrate our approach, we evaluate the restrictiveness of popular models in two laboratory settings -- certainty equivalents and initial play -- and in one field setting -- takeup of microfinance in Indian villages. The restrictiveness measure reveals new insights about each of the models, including that some economic models with only a few parameters are very flexible.