pwc-archive/papers-with-abstracts huggingface dataset:
Towards Designing Systems with Large Number of Antennas for Range Extension in Ground-to-Air Communications
Authors: Haneya Naeem Qureshi, Ali Imran
Date: 2020-06-25 00:00:00

Providing broadband connectivity to airborne systems using ground based cellular networks is a promising solution as it offers several advantages over satellite-based solutions. However, limited range of terrestrial base stations is a key challenge in full realization of this approach. This paper addresses this problem by proposing a mathematical framework for range extension leveraging large number of antennas at the base station. In contrast to prior works where range is not considered as a design parameter, we model the signal to noise ratio as a function of both number of antennas as well as the range in line-of-sight ground-to-air systems. This allows us to derive analytical expressions to determine the number of antennas required to increase range in different frequency bands and tracking and non tracking scenarios.
Iterative Algorithms for Joint Scatter and Attenuation Estimation From Broken Ray Transform Data
Authors: Michael R. Walker II, Joseph A. O'Sullivan
Date: 2020-06-25 00:00:00

The single-scatter approximation is fundamental in many tomographic imaging problems including x-ray scatter imaging and optical scatter imaging for certain media. In all cases, noisy measurements are affected by both local scatter events and nonlocal attenuation. Prior works focus on reconstructing one of two images: scatter density or total attenuation. However, both images are media specific and useful for object identification. Nonlocal effects of the attenuation image on the data are summarized by the broken ray transform (BRT). While analytic inversion formulas exist, poor conditioning of the inverse problem is only exacerbated by noisy measurements and sampling errors. This has motivated interest in the related star transforms incorporating BRT measurements from multiple source-detector pairs. However, all analytic methods operate on the log of the data. For media comprising regions with no scatter a new approach is required. We are the first to present a joint estimation algorithm based on Poisson data models for a single-scatter measurement geometry. Monotonic reduction of the log-likelihood function is guaranteed for our iterative algorithm while alternating image updates. We also present a fast algorithm for computing the discrete BRT forward operator. Our generalized approach can incorporate both transmission and scatter measurements from multiple source-detector pairs. Transmission measurements resolve low-frequency ambiguity in the joint image estimation problem, while multiple scatter measurements resolve the attenuation image. The benefits of joint estimation, over single-image estimation, vary with problem scaling. Our results quantify these benefits and should inform design of future acquisition systems.
Identification and Formal Privacy Guarantees
Authors: Tatiana Komarova, Denis Nekipelov
Date: 2020-06-25 00:00:00

Empirical economic research crucially relies on highly sensitive individual datasets. At the same time, increasing availability of public individual-level data makes it possible for adversaries to potentially de-identify anonymized records in sensitive research datasets. Most commonly accepted formal definition of an individual non-disclosure guarantee is referred to as differential privacy. It restricts the interaction of researchers with the data by allowing them to issue queries to the data. The differential privacy mechanism then replaces the actual outcome of the query with a randomised outcome. The impact of differential privacy on the identification of empirical economic models and on the performance of estimators in nonlinear empirical Econometric models has not been sufficiently studied. Since privacy protection mechanisms are inherently finite-sample procedures, we define the notion of identifiability of the parameter of interest under differential privacy as a property of the limit of experiments. It is naturally characterized by the concepts from the random sets theory. We show that particular instances of regression discontinuity design may be problematic for inference with differential privacy as parameters turn out to be neither point nor partially identified. The set of differentially private estimators converges weakly to a random set. Our analysis suggests that many other estimators that rely on nuisance parameters may have similar properties with the requirement of differential privacy. We show that identification becomes possible if the target parameter can be deterministically located within the random set. In that case, a full exploration of the random set of the weak limits of differentially private estimators can allow the data curator to select a sequence of instances of differentially private estimators converging to the target parameter in probability.
Optimal Capacity of a Battery Energy Storage System based on Solar Variability Index to Smooth out Power Fluctuations in PV-Diesel Microgrids
Date: 2020-06-26 00:00:00

Battery energy storage systems can be integrated with photovoltaic
(PV)-diesel microgrids, as an enabling technology to increase the penetration
of PV systems and aid microgrid stability by smoothing out the power
fluctuations of the PV systems. The aim of this paper is to derive correlations
between the optimal capacity of the smoothing batteries and variabilities in
daily solar irradiance. Two commonly used smoothing techniques of moving
average and ramp rate control are applied on a real solar irradiance dataset
with a 1-minute resolution for a full calendar year across 11 sites in
Australia. The paper then presents the developed empirical model, based on
linear regressions, to estimate the optimal capacity of the batteries without
requiring the use of detailed simulation studies. The performance of the
developed technique is validated by numerical simulation studies in MATLAB. The
study demonstrates that the empirical model provided reasonably accurate
estimates when using the moving average smoothing technique, but had limited
accuracy under the ramp rate control technique.
Optimal Configuration of Wind-to-Ammonia with the Electric Network and Hydrogen Supply Chain: A Case Study of Inner Mongolia
Date: 2020-06-26 00:00:00

Converting wind energy into ammonia (WtA) has been recognized as a promising
pathway to enhance the usage of wind generation. This paper proposes a generic
optimal configuration model of WtA at the network level to minimize the ammonia
production cost by optimizing capacities and locations of WtA facilities
including wind turbines, electrolyzers, hydrogen tanks and optimizing supply
modes among regions. Specifically, the temporal fluctuation characteristics of
wind resources, the operation flexibility of the ammonia synthesis reactor and
the transport distances are considered. Three typical supply modes, i.e., the
Local WtA, the EN (electric network)-based WtA and the HSC (hydrogen supply
chain)-based WtA, combined with two energy transport modes including EN and HT
(Hydrogen truck trailers) are included with the consideration of the maximal
energy transport capacity of EN and transport distance per day of HT (500km).
Real data of Inner Mongolia (a typical province in China with rich wind
resources and existing ammonia industries) is employed to verify the
effectiveness and significance of proposed model. The effect of above
significant factors on optimal planning capacity of WtA facilities and optimal
energy transport modes is analyzed, which provides guidelines for WtA
configuration. The economic analysis shows that the average LCOA (levelized
cost of ammonia) for WtA is approximately 0.57 euro/kg in Inner Mongolia and
comparable to that for CtA (coal-to-ammonia, 0.41 euro/kg) with a reduction of
30% in capacity cost of the facilities.
Drug Repurposing to find Inhibitors of SARS-CoV-2 Main Protease
Authors: Emilio Angelina, Sebastian Andujar, Oscar Parravicini, Daniel Enriz, Nelida Peruchena
Date: 2020-06-26 00:00:00

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the strain of coronavirus that causes coronavirus disease 2019 (COVID-19), the respiratory illness responsible for the COVID-19 pandemic. Currently there is no known vaccine or specific antiviral treatment for COVID-19 and so, there is an urgent need for expedite discovery of new therapeutics to combat the disease until a vaccine will be available worldwide. Drug repurposing is a strategy for identifying new uses for approved drugs that has the advantage (over conventional approaches that attempt to develop a drug from scratch) that time frame of the overall process can be significantly reduced because of the few number of clinical trial required. In this work, a virtual screening of FDA-approved drugs was performed for repositioning as potential inhibitors of the main protease Mpro of SARS-CoV-2. As a result of this study, 12 drugs are proposed as candidates for inhibitors of the Mpro enzyme. Some of the selected compounds are antiviral drugs that are already being tested in COVID-19 clinical trials (i.e. ribavirin) or are used to alleviate symptoms of the disease (i.e. codeine). Surprisingly, the most promising candidate is the naturally occurring broad spectrum antibiotic oxytetracycline. This compound has largely outperformed the remaining selected candidates along all filtering steps of our virtual screening protocol. If the activity of any of these drugs is experimentally corroborated, they could be used directly in clinical trials without the need for pre-clinical testing or safety evaluation since they are already used as drugs for other diseases.
A pure-jump mean-reverting short rate model
Date: 2020-06-26 00:00:00
Tasks: model

A new multi-factor short rate model is presented which is bounded from below
by a real-valued function of time. The mean-reverting short rate process is
modeled by a sum of pure-jump Ornstein--Uhlenbeck processes such that the
related bond prices possess affine representations. Also the dynamics of the
associated instantaneous forward rate is provided and a condition is derived
under which the model can be market-consistently calibrated. The analytical
tractability of this model is illustrated by the derivation of an explicit
plain vanilla option price formula. With view on practical applications,
suitable probability distributions are proposed for the driving jump processes.
The paper is concluded by presenting a post-crisis extension of the proposed
short and forward rate model.
Variance and interest rate risk in unit-linked insurance policies
Authors: David R. Baños, Marc Lagunas-Merino, Salvador Ortiz-Latorre
Date: 2020-06-26 00:00:00

One of the risks derived from selling long term policies that any insurance company has, arises from interest rates. In this paper we consider a general class of stochastic volatility models written in forward variance form. We also deal with stochastic interest rates to obtain the risk-free price for unit-linked life insurance contracts, as well as providing a perfect hedging strategy by completing the market. We conclude with a simulation experiment, where we price unit-linked policies using Norwegian mortality rates. In addition we compare prices for the classical Black-Scholes model against the Heston stochastic volatility model with a Vasicek interest rate model.
Distributed Localization in Wireless Sensor Networks Under Denial-of-Service Attacks
Date: 2020-06-26 00:00:00

In this paper, we study the problem of localizing the sensors' positions in
presence of denial-of-service (DoS) attacks. We consider a general attack
model, in which the attacker action is only constrained through the frequency
and duration of DoS attacks. We propose a distributed iterative localization
algorithm with an abandonment strategy based on the barycentric coordinate of a
sensor with respect to its neighbors, which is computed through relative
distance measurements. In particular, if a sensor's communication links for
receiving its neighbors' information lose packets due to DoS attacks, then the
sensor abandons the location estimation. When the attacker launches DoS
attacks, the AS-DILOC algorithm is proved theoretically to be able to
accurately locate the sensors regardless of the attack strategy at each time.
The effectiveness of the proposed algorithm is demonstrated through simulation
examples.
Secure Beamforming for Multiple Intelligent Reflecting Surfaces Aided mmWave Systems
Authors: Yue Xiu, Jun Zhao, Chau Yuen, Zhongpei Zhang, Guan Gui
Date: 2020-06-26 00:00:00

In this letter, secure beamforming in a multiple intelligent reflecting surfaces (IRSs)-aided millimeter-wave (mmWave) system is investigated. In this system, the secrecy rate is maximized by controlling the on-off status of each IRS as well as optimizing the phase shift matrix of the IRSs. This problem is posed as a joint optimization problem of transmit beamforming and IRS control, whose goal is to maximize the secrecy rate under the total transmission power and unit-modulus constraints. The problem is difficult to solve optimally due to the nonconvexity of constraint conditions and coupled variables. To deal with this problem, we propose an alternating optimization (AO)-based algorithm based on successive convex approximation (SCA) and manifold optimization (MO) technologies. Numerical simulations show that the proposed AO-based algorithm can effectively improve the secrecy rate and outperforms the traditional single IRS-aided scheme.
Social Welfare in Search Games with Asymmetric Information
Authors: Gilad Bavly, Yuval Heller, Amnon Schreiber
Date: 2020-06-26 00:00:00

We consider games in which players search for a hidden prize, and they have asymmetric information about the prize location. We study the social payoff in equilibria of these games. We present sufficient conditions for the existence of an equilibrium that yields the first-best payoff (i.e., the highest social payoff under any strategy profile), and we characterize the first-best payoff. The results have interesting implications for innovation contests and R&D races.
Status Quo Bias and the Decoy Effect: A Comparative Analysis in Choice under Risk
Authors: Miguel Costa-Gomes, Georgios Gerasimou
Date: 2020-06-26 00:00:00
Tasks: Descriptive

Inertia and context-dependent choice effects are well-studied classes of behavioural phenomena. While much is known about these effects in isolation, little is known about whether one of them "dominates" the other when both can potentially be present. Knowledge of any such dominance is relevant for effective choice architecture and descriptive modelling. We initiate this empirical investigation with a between-subjects lab experiment in which each subject made a single decision over two or three money lotteries. Our experiment was designed to test for dominance between *status quo bias* and the *decoy effect*. We find strong evidence for status quo bias and no evidence for the decoy effect. We also find that status quo bias can be powerful enough so that, at the aggregate level, a fraction of subjects switch from being risk-averse to being risk-seeking. Survey evidence suggests that this is due to subjects focusing on the maximum possible amount when the risky lottery is the default and on the highest probability of winning the biggest possible reward when there is no default. The observed reversal in risk attitudes is explainable by a large class of Koszegi-Rabin (2006) reference-dependent preferences.
Revealing Choice Bracketing
Authors: Andrew Ellis, David J. Freeman
Date: 2020-06-26 00:00:00

Experiments suggest that people fail to take into account interdependencies between their choices -- they do not broadly bracket. Researchers often instead assume that people narrowly bracket, but existing designs do not test it. We design a novel experiment and revealed preference tests for how someone brackets their choices. In portfolio allocation under risk, social allocation, and induced-value shopping experiments, 40-43% of subjects are consistent with narrow bracketing and 0-16% with broad bracketing. Adjusting for each model's predictive precision, 74% of subjects are best described by narrow bracketing, 13% by broad bracketing, and 6% by intermediate cases.
Analysis of the SNR loss distribution with covariance mismatched training samples
Authors: Olivier Besson
Date: 2020-06-26 00:00:00
Tasks: Form

We analyze the distribution of the signal to noise ratio (SNR) loss at the output of an adaptive filter which is trained with samples that do not share the same covariance matrix as the samples for which the filter is foreseen. Our objective is to find an accurate approximation of the distribution of the SNR loss which has a similar form as in the case of no mismatch. We successively consider the case where the two covariance matrices satisfy the so-called generalized eigenrelation and the case where they are arbitrary. In the former case, this amounts to approximate a central quadratic form in normal variables while the latter case entails approximating a non-central quadratic form in Student distributed variables. In order to obtain the approximate distribution, a Pearson type approach is advocated. A numerical study show that this approximation is rather accurate and enables one to assess, in a straightforward manner, the impact of covariance mismatch.
A metabolomic measure of energy metabolism moderates how an inflammatory miRNA relates to rs-fMRI network and motor control in football athletes
Date: 2020-06-23 00:00:00

Collision sports athletes experience many head acceleration events (HAEs) per
season. The effects of these subconcussive events are largely understudied
since HAEs may produce no overt symptoms, and are likely to diffusely manifest
across multiple scales of study (e.g., molecular, cellular network, and
behavior). This study integrated resting-state fMRI with metabolome,
transcriptome and computational virtual reality (VR) behavior measures to
assess the effects of exposure to HAEs on players in a collegiate American
football team. Permutation-based mediation and moderation analysis was used to
investigate relationships between network fingerprint, changes in omic measures
and VR metrics over the season. Change in an energy cycle fatty acid,
tridecenedioate, moderated the relationship between 1) miR-505 and DMN
fingerprint and 2) the relationship between DMN fingerprint and worsening VR
Balance measures (all p less than or equal to 0.05). In addition, the
similarity in DMN over the season was negatively related to cumulative number
of HAEs above 80G, and DMN fingerprint was less similar across the season in
athletes relative to age-matched non-athletes. miR-505 was also positively
related to average number of HAEs above 25G per session. It is important to
note that tridecenedioate has a double bond making it a candidate for ROS
scavenging. These findings between a candidate ROS-related metabolite,
inflammatory miRNA, altered brain imaging and diminished behavioral performance
suggests that impact athletes may experience chronic neuroinflammation. The
rigorous permutation-based mediation/moderation may provide a methodology for
investigating complex multi-scale biological data within humans alone and thus
assist study of other functional brain problems.
Elimination of Redundant Polynomial Constraints and Its Use in Constrained Control
Date: 2020-06-26 00:00:00
Tasks: Model Predictive Control

The reduction of constraints to obtain minimal representations of sets is a
very common problem in many engineering applications. While well-established
methodologies exist for the case of linear constraints, the problem of how to
detect redundant non-linear constraints is an open problem. In this paper we
present a novel methodology based on Sum of Squares for the elimination of
redundant polynomial constraints. The paper also presents some relevant
applications of the presented method to constrained control problems. In
particular, we show how the proposed method can be used in the Model Predictive
Control and in the Reference Governor frameworks to reduce the computational
burden of the online algorithms. Furthermore, this method can also be used to
eliminate the terminal constraints in MPC in a simple way that is independent
from the cost function.
Solution of matching equations of IDA-PBC by Pfaffian differential equations
Authors: M. Reza J. Harandi, Hamid. D. Taghirad
Date: 2020-06-26 00:00:00

Finding the general solution of partial differential equations (PDEs) is essential for controller design in newly developed methods. Interconnection and damping assignment passivity based control (IDA-PBC) is one of such methods in which the solution to corresponding PDEs which are called matching equations, is needed to apply it in practice. In this paper, these matching equations are transformed to corresponding Pfaffian differential equations. Furthermore, it is shown that upon satisfaction of the integrability condition, the solution to the corresponding third-order Pfaffian differential equation may be obtained quite easily. The method is applied to the PDEs of IDA-PBC in some benchmark systems such as Magnetic levitation system, Pendubot, and underactuated cable driven robot to verify its applicability.
Endogenous Treatment Effect Estimation with some Invalid and Irrelevant Instruments
Date: 2020-06-26 00:00:00
Tasks: valid

Instrumental variables (IV) regression is a popular method for the estimation
of the endogenous treatment effects. Conventional IV methods require all the
instruments are relevant and valid. However, this is impractical especially in
high-dimensional models when we consider a large set of candidate IVs. In this
paper, we propose an IV estimator robust to the existence of both the invalid
and irrelevant instruments (called R2IVE) for the estimation of endogenous
treatment effects. This paper extends the scope of Kang et al. (2016) by
considering a true high-dimensional IV model and a nonparametric reduced form
equation. It is shown that our procedure can select the relevant and valid
instruments consistently and the proposed R2IVE is root-n consistent and
asymptotically normal. Monte Carlo simulations demonstrate that the R2IVE
performs favorably compared to the existing high-dimensional IV estimators
(such as, NAIVE (Fan and Zhong, 2018) and sisVIVE (Kang et al., 2016)) when
invalid instruments exist. In the empirical study, we revisit the classic
question of trade and growth (Frankel and Romer, 1999).
The Nonuniversality of Wealth Distribution Tails Near Wealth Condensation Criticality
Authors: Sam L. Polk, Bruce M. Boghosian
Date: 2020-06-26 00:00:00

In this work, we modify the affine wealth model of wealth distributions to examine the effects of nonconstant redistribution on the very wealthy. Previous studies of this model, restricted to flat redistribution schemes, have demonstrated the presence of a phase transition to a partially wealth-condensed state, or "partial oligarchy," at the critical value of an order parameter. These studies have also indicated the presence of an exponential tail in wealth distribution precisely at criticality. Away from criticality, the tail was observed to be Gaussian. In this work, we generalize the flat redistribution within the affine wealth model to allow for an essentially arbitrary redistribution policy. We show that the exponential tail observed near criticality in prior work is, in fact, a special case of a much broader class of critical, slower-than-Gaussian decays that depend sensitively on the corresponding asymptotic behavior of the progressive redistribution model used. We thereby demonstrate that the functional form of the tail of the wealth distribution in a near-critical society is not universal in nature but rather entirely determined by the specifics of public policy decisions. This is significant because most major economies today are observed to be near-critical.