pwc-archive/papers-with-abstracts huggingface dataset:
Calibration of Polarimetric Radar Data using the Sylvester Equation in a Pauli Basis
Authors: Shane R. Cloude
Date: 2021-10-29 00:00:00

In this paper we develop a new approach to the calibration of polarimetric radar data based on two key ideas. The first is the use of in-scene trihedral corner reflectors not only for radiometric and geometric calibration but also to completely remove any receiver distortion components. Secondly, we then show that the remaining transmitter distortion acts as a similarity transformation of the true scattering matrix. This leads us to employ a change of base to the Pauli matrix components. We show that in this basis calibration and the effects of Faraday rotation become much simplified and for example by using reciprocity alone we can then solve for copolar channel imbalance. Finally by using an uncalibrated symmetric point target of opportunity we can estimate cross-talks and hence fully solve the calibration problem without the need for using clutter averaging or symmetry assumptions in the covariance matrix as used in many other algorithms.
Exact computation of growth-rate fluctuations in random environment
Authors: J. Unterberger
Date: 2021-11-07 00:00:00

We consider a general class of Markovian models describing the growth in a randomly fluctuating environment of a clonal biological population having several phenotypes related by stochastic switching. Phenotypes differ e.g. by the level of gene expression for a population of bacteria. The time-averaged growth rate of the population, $\Lambda$, is self-averaging in the limit of infinite times; it may be understood as the fitness of the population in a context of Darwinian evolution. The observation time $T$ being however typically finite, the growth rate fluctuates. For $T$ finite but large, we obtain the variance of the time-averaged growth rate as the maximum of a functional based on the stationary probability distribution for the phenotypes. This formula is general. In the case of two states, the stationary probability was computed by Hufton, Lin and Galla \cite{HufLin2}, allowing for an explicit expression which can be checked numerically.
The central role of metabolism in vascular morphogenesis
Authors: Georgios Gounaris, Mija Jovchevska, Miguel Ruiz Garcia, Eleni Katifori
Date: 2021-11-08 00:00:00

As nutrients travel through microcirculation and are absorbed, their availability continuously decreases. However, a uniform nutrient distribution is critical, as it prevents tissue death in poorly supplied areas. How, then, do vascular networks achieve equi-perfusion? Given the extensive number of vessels in animal vascular systems, the structure of smaller vessels cannot be fully genetically predetermined and thus relies on a self-organizing developmental mechanism. We propose a simple, optimization-based adaptation rule to control vessel radii, aiming to equalize perfusion while minimizing flow resistance and material cost. This adaptation balances three competing factors: perfusion efficiency, energy dissipation, and material expenditure, which together drive complex network morphologies. These morphologies range from hierarchical architectures optimized for minimal resistance and material cost to dense networks that achieve efficient perfusion. Notably, metabolic demand is the primary factor modulating the transition between these contrasting structures. By applying our adaptation rule to networks in the rat mesentery and comparing the resulting optimal perfusion architectures with the experimental data, we can estimate the biologically relevant parameters, such as the oxygen absorption rate, for which the optimization and experimental networks align the most. Surprisingly, the optimal absorption rate we derive matches independent in vivo measurements. This suggests that reduced-order models like ours can provide valuable insights into the development and function of real vascular networks.
Subduing always defecting mutants by multiplayer reactive strategies: Non-reciprocity versus generosity
Authors: Shubhadeep Sadhukhan, Ashutosh Shukla, Sagar Chakraborty
Date: 2021-11-08 00:00:00

A completely non-generous and reciprocal population of players can create a robust cooperating state that cannot be invaded by always defecting free riders if the interactions among players are repeated for long enough. However, strict non-generosity and strict reciprocity are ideal concepts, and may not even be desirable sometimes. Therefore, to what extent generosity or non-reciprocity can be allowed while still not be swamped by the mutants, is a natural question. In this paper, we not only ask this question but furthermore ask how generosity comparatively fares against non-reciprocity in this context. For mathematical concreteness, we work within the framework of multiplayer repeated prisoner's dilemma game with reactive strategies in a finite and an infinite population; and explore the aforementioned questions through the effects of the benefit to cost ratio, the interaction group size, and the population size.
Simultaneous estimation of wall and object parameters in TWR using deep neural network
Authors: Fardin Ghorbani, Hossein Soleimani
Date: 2021-10-30 00:00:00

This paper presents a deep learning model for simultaneously estimating target and wall parameters in Through-the-Wall Radar. In this work, we consider two modes: single-target and two-targets. In both cases, we consider the permittivity and thickness for the wall, as well as the two-dimensional coordinates of the target's center and permittivity. This means that in the case of a single target, we estimate five values, whereas, in the case of two targets, we estimate eight values simultaneously, each of which represents the mentioned parameters. We discovered that when using deep neural networks to solve the target locating problem, giving the model more parameters of the problem increases the location accuracy. As a result, we included two wall parameters in the problem and discovered that the accuracy of target locating improves while the wall parameters are estimated. We were able to estimate the parameters of wall permittivity and thickness, as well as two-dimensional coordinates and permittivity of targets in single-target and two-target modes with 99\% accuracy by using a deep neural network model.
Proteins Evolution Upon Point Mutations
Authors: J. A. Vila
Date: 2021-11-01 00:00:00

The primary aim of this work is to explore how proteins point mutations impact their marginal stability and, hence, their evolvability. With this purpose, we show that the use of four classic notions, namely, those from Leibniz & Kant (1768), Maynard Smith (1970), Einstein & Infeld (1961), and Anfinsen (1973), is sufficient for a better understanding of the protein-evolution and, consequently, to determine the factors that could control it. The preliminary results -- without considering epistasis effects explicitly -- indicate that the protein marginal-stability change upon point mutations provides the necessary and sufficient information to describe, through a Boltzmann factor, the evolution of the amide hydrogen-exchange protection factors. This finding is of paramount importance because it illustrates the impact of point mutations on both the protein marginal-stability and the ensemble of folded conformations coexisting with the native state and, in the presence of metamorphism, on the propensity for the appearance of new folds and functions.
Exponential GARCH-Ito Volatility Models
Authors: Donggyu Kim
Date: 2021-11-08 00:00:00
Tasks: parameter estimation

This paper introduces a novel Ito diffusion process to model high-frequency financial data, which can accommodate low-frequency volatility dynamics by embedding the discrete-time non-linear exponential GARCH structure with log-integrated volatility in a continuous instantaneous volatility process. The key feature of the proposed model is that, unlike existing GARCH-Ito models, the instantaneous volatility process has a non-linear structure, which ensures that the log-integrated volatilities have the realized GARCH structure. We call this the exponential realized GARCH-Ito (ERGI) model. Given the auto-regressive structure of the log-integrated volatility, we propose a quasi-likelihood estimation procedure for parameter estimation and establish its asymptotic properties. We conduct a simulation study to check the finite sample performance of the proposed model and an empirical study with 50 assets among the S\&P 500 compositions. The numerical studies show the advantages of the new proposed model.
Procurements with Bidder Asymmetry in Cost and Risk-Aversion
Authors: Gaurab Aryal, Hanna Charankevich, Seungwon Jeong, Dong-Hyuk Kim
Date: 2021-11-08 00:00:00
Tasks: counterfactual, Data Augmentation

We propose an empirical method to analyze data from first-price procurements where bidders are asymmetric in their risk-aversion (CRRA) coefficients and distributions of private costs. Our Bayesian approach evaluates the likelihood by solving type-symmetric equilibria using the boundary-value method and integrates out unobserved heterogeneity through data augmentation. We study a new dataset from Russian government procurements focusing on the category of printing papers. We find that there is no unobserved heterogeneity (presumably because the job is routine), but bidders are highly asymmetric in their cost and risk-aversion. Our counterfactual study shows that choosing a type-specific cost-minimizing reserve price marginally reduces the procurement cost; however, inviting one more bidder substantially reduces the cost, by at least 5.5%. Furthermore, incorrectly imposing risk-neutrality would severely mislead inference and policy recommendations, but the bias from imposing homogeneity in risk-aversion is small.
The Wrong Kind of Information
Authors: Aditya Kuvalekar, João Ramos, Johannes Schneider
Date: 2021-11-07 00:00:00

Agents, some with a bias, decide between undertaking a risky project and a safe alternative based on information about the project's efficiency. Only a part of that information is verifiable. Unbiased agents want to undertake only efficient projects, while biased agents want to undertake any project. If the project causes harm, a court examines the verifiable information, forms a belief about the agent's type, and decides the punishment. Tension arises between deterring inefficient projects and a chilling effect on using the unverifiable information. Improving the unverifiable information always increases overall efficiency, but improving the verifiable information may reduce efficiency.
On the benefits of index insurance in US agriculture: a large-scale analysis using satellite data
Authors: Matthieu Stigler, David Lobell
Date: 2020-11-25 00:00:00

Index insurance has been promoted as a promising solution for reducing agricultural risk compared to traditional farm-based insurance. By linking payouts to a regional factor instead of individual loss, index insurance reduces monitoring costs, and alleviates the problems of moral hazard and adverse selection. Despite its theoretical appeal, demand for index insurance has remained low in many developing countries, triggering a debate on the causes of the low uptake. Surprisingly, there has been little discussion in this debate about the experience in the United States. The US is an unique case as both farm-based and index-based products have been available for more than two decades. Furthermore, the number of insurance zones is very large, allowing interesting comparisons over space. As in developing countries, the adoption of index insurance is rather low -- less than than 5\% of insured acreage. Does this mean that we should give up on index insurance? In this paper, we investigate the low take-up of index insurance in the US leveraging a field-level dataset for corn and soybean obtained from satellite predictions. While previous studies were based either on county aggregates or on relatively small farm-level dataset, our satellite-derived data gives us a very large number of fields (close to 1.8 million) comprised within a large number of index zones (600) observed over 20 years. To evaluate the suitability of index insurance, we run a large-scale simulation comparing the benefits of both insurance schemes using a new measure of farm-equivalent risk coverage of index insurance. We make two main contributions. First, we show that in our simulations, demand for index insurance is unexpectedly high, at about 30\% to 40\% of total demand. This result is robust to relaxing several assumptions of the model and to using prospect theory instead of expected utility.
Concavity and Convexity of Order Statistics in Sample Size
Authors: Mitchell Watt
Date: 2021-11-08 00:00:00

We show that the expectation of the $k^{\mathrm{th}}$-order statistic of an i.i.d. sample of size $n$ from a monotone reverse hazard rate (MRHR) distribution is convex in $n$ and that the expectation of the $(n-k+1)^{\mathrm{th}}$-order statistic from a monotone hazard rate (MHR) distribution is concave in $n$ for $n\ge k$. We apply this result to the analysis of independent private value auctions in which the auctioneer faces a convex cost of attracting bidders. In this setting, MHR valuation distributions lead to concavity of the auctioneer's objective. We extend this analysis to auctions with reserve values, in which concavity is assured for sufficiently small reserves or for a sufficiently large number of bidders.
Marriage through friends
Authors: Ugo Bolletta, Luca Paolo Merlino
Date: 2021-11-06 00:00:00

In this paper, we propose a model of the marriage market in which individuals meet potential partners either directly or through their friends. When socialization is exogenous, a higher arrival rate of direct meetings also implies more meetings through friends. When individuals decide how much to invest in socialization, meetings through friends are first increasing and then decreasing in the arrival rate of direct offers. Hence, our model can rationalize the negative correlation between the advent of online dating and the decrease of marriages through friends observed in the US over the past decades.
Maximizing Unambiguous Velocity Range in Phase-contrast MRI with Multipoint Encoding
Authors: Shen Zhao, Rizwan Ahmad, Lee C. Potter
Date: 2021-11-07 00:00:00

In phase-contrast magnetic resonance imaging (PC-MRI), the velocity of spins at a voxel is encoded in the image phase. The strength of the velocity encoding gradient offers a trade-off between the velocity-to-noise ratio (VNR) and the extent of phase aliasing. Phase differences provide invariance to an unknown background phase. Existing literature proposes processing a reduced set of phase difference equations, simplifying the phase unwrapping problem at the expense of VNR or unaliased range of velocities, or both. Here, we demonstrate that the fullest unambiguous range of velocities is a parallelepiped, which can be accessed by jointly processing all phase differences. The joint processing also minimizes the velocity-to-noise ratio. The simple understanding of the unambiguous parallelepiped provides the potential for analyzing new multi-point acquisitions for an enhanced range of unaliased velocities; two examples are given.
Learning-based Remote Radio Head Selection and Localization in Distributed Antenna System
Authors: Artan Salihu, Stefan Schwarz, Markus Rupp
Date: 2021-11-07 00:00:00

In this work, we consider estimating user positions in a spatially distributed antenna system (DAS) from the uplink channel state information (CSI). However, with the increased number of remote radio heads (RRHs), collecting CSI at a central unit (CU) can significantly increase the fronthaul overhead and computational complexity of the CU. This problem can be mitigated by selecting a subset of RRHs. Thus, we present a deep learning-based approach to select a subset of RRHs for wireless localization. We employ an RRH selection layer that is jointly trained with the rest of the network and learn the model parameters as well as the set of selected RRHs. We show that the selection strategy comes at a relatively small cost of localization performance. Nonetheless, by comparison to a trivial approach based on the maximization of the channel gain, we show that the proposed method leads to significant performance gains in a propagation environment dominated by non-line-of-sight.
AI-based Radio Resource Management and Trajectory Design for PD-NOMA Communication in IRS-UAV Assisted Networks
Authors: Hussein M. Hariz, Saeed Sheikhzadeh, Nader Mokari, Mohammad R. Javan, B. Abbasi-Arand, Eduard A. Jorswieck
Date: 2021-11-06 00:00:00
Tasks: Management, Reinforcement Learning (RL)

In this paper, we consider that the unmanned aerial vehicles (UAVs) with attached intelligent reflecting surfaces (IRSs) play the role of flying reflectors that reflect the signal of users to the destination, and utilize the power-domain non-orthogonal multiple access (PD-NOMA) scheme in the uplink. We investigate the benefits of the UAV-IRS on the internet of things (IoT) networks that improve the freshness of collected data of the IoT devices via optimizing power, sub-carrier, and trajectory variables, as well as, the phase shift matrix elements. We consider minimizing the average age-of-information (AAoI) of users subject to the maximum transmit power limitations, PD-NOMA-related restriction, and the constraints related to UAV's movement. The optimization problem consists of discrete and continuous variables. Hence, we divide the resource allocation problem into two sub-problems and use two different reinforcement learning (RL) based algorithms to solve them, namely the double deep Qnetwork (DDQN) and a proximal policy optimization (PPO). Our numerical results illustrate the performance gains that can be achieved for IRS enabled UAV communication systems. Moreover, we compare our deep RL (DRL) based algorithm with matching algorithm and random trajectory, showing the combination of DDQN and PPO algorithm proposed in this paper performs 10% and 15% better than matching algorithm and random-trajectory algorithm, respectively.
Trajectory PHD and CPHD Filters with Unknown Detection Profile
Authors: Shaoxiu Wei, Boxiang Zhang, Wei Yi
Date: 2021-11-06 00:00:00
Tasks: Computational Efficiency

Compared to the probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters, the trajectory PHD (TPHD) and trajectory CPHD (TCPHD) filters are for sets of trajectories, and thus are able to produce trajectory estimates with better estimation performance. In this paper, we develop the TPHD and TCPHD filters which can adaptively learn the history of the unknown target detection probability, and therefore they can perform more robustly in scenarios where targets are with unknown and time-varying detection probabilities. These filters are referred to as the unknown TPHD (U-TPHD) and unknown TCPHD (U-TCPHD) filters.By minimizing the Kullback-Leibler divergence (KLD), the U-TPHD and U-TCPHD filters can obtain, respectively, the best Poisson and independent identically distributed (IID) density approximations over the augmented sets of trajectories. For computational efficiency, we also propose the U-TPHD and U-TCPHD filters that only consider the unknown detection profile at the current time. Specifically, the Beta-Gaussian mixture method is adopted for the implementation of proposed filters, which are referred to as the BG-U-TPHD and BG-U-TCPHD filters. The L-scan approximations of these filters with much lower computational burden are also presented. Finally, various simulation results demonstrate that the BG-U-TPHD and BG-U-TCPHD filters can achieve robust tracking performance to adapt to unknown detection profile. Besides, it also shows that usually a small value of the L-scan approximation can achieve almost full efficiency of both filters but with a much lower computational costs.
OTFS-Based Joint Communication and Sensing for Future Industrial IoT
Authors: Kai Wu, J. Andrew Zhang, Xiaojing Huang, Y. Jay Guo
Date: 2021-11-06 00:00:00

Effective wireless communications are increasingly important in maintaining the successful closed-loop operation of mission-critical industrial Internet-of-Things (IIoT) applications. To meet the ever-increasing demands on better wireless communications for IIoT, we propose an orthogonal time-frequency space (OTFS) waveform-based joint communication and radio sensing (JCAS) scheme -- an energy-efficient solution for not only reliable communications but also high-accuracy sensing. OTFS has been demonstrated to have higher reliability and energy efficiency than the currently popular IIoT communication waveforms. JCAS has also been highly recommended for IIoT, since it saves cost, power and spectrum compared to having two separate radio frequency systems. Performing JCAS based on OTFS, however, can be hindered by a lack of effective OTFS sensing. This paper is dedicated to filling this technology gap. We first design a series of echo pre-processing methods that successfully remove the impact of communication data symbols in the time-frequency domain, where major challenges, like inter-carrier and inter-symbol interference and noise amplification, are addressed. Then, we provide a comprehensive analysis of the signal-to-interference-plus-noise ratio (SINR) for sensing and optimize a key parameter of the proposed method to maximize the SINR. Extensive simulations show that the proposed sensing method approaches the maximum likelihood estimator with respect to the estimation accuracy of target parameters and manifests applicability to wide ranges of key system parameters. Notably, the complexity of the proposed method is only dominated by a two-dimensional Fourier transform.
RAIL: Robust Acoustic Indoor Localization for Drones
Authors: Alireza Famili, Angelos Stavrou, Haining Wang, Jung-Min, Park
Date: 2021-11-06 00:00:00
Tasks: Autonomous Navigation, Indoor Localization

Navigating in environments where the GPS signal is unavailable, weak, purposefully blocked, or spoofed has become crucial for a wide range of applications. A prime example is autonomous navigation for drones in indoor environments: to fly fully or partially autonomously, drones demand accurate and frequent updates of their locations. This paper proposes a Robust Acoustic Indoor Localization (RAIL) scheme for drones designed explicitly for GPS-denied environments. Instead of depending on GPS, RAIL leverages ultrasonic acoustic signals to achieve precise localization using a novel hybrid Frequency Hopping Code Division Multiple Access (FH-CDMA) technique. Contrary to previous approaches, RAIL is able to both overcome the multi-path fading effect and provide precise signal separation in the receiver. Comprehensive simulations and experiments using a prototype implementation demonstrate that RAIL provides high-accuracy three-dimensional localization with an average error of less than $1.5$~cm.
Securing your Airspace: Detection of Drones Trespassing Protected Areas
Authors: Alireza Famili, Angelos Stavrou, Haining Wang, Jung-Min, Park, Ryan Gerdes
Date: 2021-11-05 00:00:00

There has been a rapid growth in the deployment of Unmanned Aerial Vehicles (UAVs) in various applications ranging from vital safety-of-life such as surveillance and reconnaissance at nuclear power plants to entertainment and hobby applications. While popular, drones can pose serious security threats that can be unintentional or intentional. Thus, there is an urgent need for real-time accurate detection and classification of drones. In this article, we perform a survey of drone detection approaches presenting their advantages and limitations. We analyze detection techniques that employ radars, acoustic and optical sensors, and emitted radio frequency (RF) signals. We compare their performance, accuracy, and cost, concluding that combining multiple sensing modalities might be the path forward.