pwc-archive/papers-with-abstracts huggingface dataset:
Performance Analysis of the Gaussian Quasi-Maximum Likelihood Approach for Independent Vector Analysis
Authors: Amir Weiss, Sher Ali Cheema, Martin Haardt, Arie Yeredor
Date: 2020-08-30 00:00:00

Maximum Likelihood (ML) estimation requires precise knowledge of the underlying statistical model. In Quasi ML (QML), a presumed model is used as a substitute to the (unknown) true model. In the context of Independent Vector Analysis (IVA), we consider the Gaussian QML Estimate (QMLE) of the demixing matrices set and present an (approximate) analysis of its asymptotic separation performance. In Gaussian QML the sources are presumed to be Gaussian, with covariance matrices specified by some "educated guess". The resulting quasi-likelihood equations of the demixing matrices take a special form, recently termed an extended "Sequentially Drilled" Joint Congruence (SeDJoCo) transformation, which is reminiscent of (though essentially different from) classical joint diagonalization. We show that asymptotically this QMLE, i.e., the solution of the resulting extended SeDJoCo transformation, attains perfect separation (under some mild conditions) regardless of the sources' true distributions and/or covariance matrices. In addition, based on the "small-errors" assumption, we present a first-order perturbation analysis of the extended SeDJoCo solution. Using the resulting closed-form expressions for the errors in the solution matrices, we provide closed-form expressions for the resulting Interference-to-Source Ratios (ISRs) for IVA. Moreover, we prove that asymptotically the ISRs depend only on the sources' covariances, and not on their specific distributions. As an immediate consequence of this result, we provide an asymptotically attainable lower bound on the resulting ISRs. We also present empirical results, corroborating our analytical derivations, of three simulation experiments concerning two possible model errors - inaccurate covariance matrices and sources' distribution mismodeling.
Measuring and Managing Carbon Risk in Investment Portfolios
Authors: Théo Roncalli, Théo Le Guenedal, Frédéric Lepetit, Thierry Roncalli, Takaya Sekine
Date: 2020-08-30 00:00:00
Tasks: Management

This article studies the impact of carbon risk on stock pricing. To address this, we consider the seminal approach of G\"orgen \textsl{et al.} (2019), who proposed estimating the carbon financial risk of equities by their carbon beta. To achieve this, the primary task is to develop a brown-minus-green (or BMG) risk factor, similar to Fama and French (1992). Secondly, we must estimate the carbon beta using a multi-factor model. While G\"orgen \textsl{et al.} (2019) considered that the carbon beta is constant, we propose a time-varying estimation model to assess the dynamics of the carbon risk. Moreover, we test several specifications of the BMG factor to understand which climate change-related dimensions are priced in by the stock market. In the second part of the article, we focus on the carbon risk management of investment portfolios. First, we analyze how carbon risk impacts the construction of a minimum variance portfolio. As the goal of this portfolio is to reduce unrewarded financial risks of an investment, incorporating the carbon risk into this approach fulfils this objective. Second, we propose a new framework for building enhanced index portfolios with a lower exposure to carbon risk than capitalization-weighted stock indices. Finally, we explore how carbon sensitivities can improve the robustness of factor investing portfolios.
The Extended "Sequentially Drilled" Joint Congruence Transformation and its Application in Gaussian Independent Vector Analysis
Authors: Amir Weiss, Arie Yeredor, Sher Ali Cheema, Martin Haardt
Date: 2020-08-30 00:00:00

Independent Vector Analysis (IVA) has emerged in recent years as an extension of Independent Component Analysis (ICA) into multiple sets of mixtures, where the source signals in each set are independent, but may depend on source signals in the other sets. In a semi-blind IVA (or ICA) framework, information regarding the probability distributions of the sources may be available, giving rise to Maximum Likelihood (ML) separation. In recent work we have shown that under the multivariate Gaussian model, with arbitrary temporal covariance matrices (stationary or non-stationary) of the source signals, ML separation requires the solution of a "Sequentially Drilled" Joint Congruence (SeDJoCo) transformation of a set of matrices, which is reminiscent of (but different from) classical joint diagonalization. In this paper we extend our results to the IVA problem, showing how the ML solution for the Gaussian model (with arbitrary covariance and cross-covariance matrices) takes the form of an extended SeDJoCo problem. We formulate the extended problem, derive a condition for the existence of a solution, and propose two iterative solution algorithms. In addition, we derive the induced Cram\'er-Rao Lower Bound (iCRLB) on the resulting Interference-to-Source Ratios (ISR) matrices, and demonstrate by simulation how the ML separation obtained by solving the extended SeDJoCo problem indeed attains the iCRLB (asymptotically), as opposed to other separation approaches, which cannot exploit prior knowledge regarding the sources' distributions.
A continuous-time asset market game with short-lived assets
Authors: Mikhail Zhitlukhin
Date: 2020-08-30 00:00:00

We consider a continuous-time game-theoretic model of an investment market with short-lived assets and endogenous asset prices. The first goal of the paper is to formulate a stochastic equation which determines wealth processes of investors and to provide conditions for the existence of its solution. The second goal is to show that there exists a strategy such that the logarithm of the relative wealth of an investor who uses it is a submartingale regardless of the strategies of the other investors, and the relative wealth of any other essentially different strategy vanishes asymptotically. This strategy can be considered as an optimal growth portfolio in the model.
Joint Transmission in QoE-Driven Backhaul-Aware MC-NOMA Cognitive Radio Network
Authors: Hosein Zarini, Ata Khalili, Hina Tabassum, Mehdi Rasti
Date: 2020-08-30 00:00:00
Tasks: Scheduling

In this paper, we develop a resource allocation framework to optimize the downlink transmission of a backhaul-aware multi-cell cognitive radio network (CRN) which is enabled with multi-carrier non-orthogonal multiple access (MC-NOMA). The considered CRN is composed of a single macro base station (MBS) and multiple small BSs (SBSs) that are referred to as the primary and secondary tiers, respectively. For the primary tier, we consider orthogonal frequency division multiple access (OFDMA) scheme and also Quality of Service (QoS) to evaluate the user satisfaction. On the other hand in secondary tier, MC-NOMA is employed and the user satisfaction for web, video and audio as popular multimedia services is evaluated by Quality-of-Experience (QoE). Furthermore, each user in secondary tier can be served simultaneously by multiple SBSs over a subcarrier via Joint Transmission (JT). In particular, we formulate a joint optimization problem of power control and scheduling (i.e., user association and subcarrier allocation) in secondary tier to maximize total achievable QoE for the secondary users. An efficient resource allocation mechanism has been developed to handle the non-linear form interference and to overcome the non-convexity of QoE serving functions. The scheduling and power control policy leverage on Augmented Lagrangian Method (ALM). Simulation results reveal that proposed solution approach can control the interference and JT-NOMA improves total perceived QoE compared to the existing schemes.
Preference Robust Optimization with Quasi-Concave Choice Functions in Multi-Attribute Decision-Making: Characterization and Computation
Authors: Jian Wu, William B. Haskell, Wenjie Huang, Huifu Xu
Date: 2020-08-31 00:00:00
Tasks: Attribute, Decision Making, Portfolio Optimization

In behavioural economics, a decision maker's (DM's) preferences are often expressed by a preference functional such as expected utility or a distortion risk measure, which assigns a numerical value to a risky prospect. Preference robust optimization (PRO) is about decision making where the DM's preference functional is ambiguous and the optimal decision is based on the worst-case preference functional from a set of plausible ones constructed from available partial information about the DM's true preferences. In this paper, we propose a choice function (a particular class of preference functionals) based PRO model where the DM's preferences over a prospect space satisfy Von Neumann-Morgenstern's (VNM's) axioms of completeness, monotonicity, and continuity. We concentrate on the class of choice functions which are monotonic, quasi-concave, and multi-attribute. The resulting PRO model is broader than the existing expected utility-based PRO models in that: (a) it captures a broader class of DM's preferences; and (b) it can be effectively applied to multi-attribute decision making problems where the DM's preferences over different attributes are related in a nonlinear manner. We propose a cutting plane-type method for evaluating the worst-case choice function and solve the resulting PRO problem by solving a sequence of convex optimization problems. We examine the behavior and scalability of the proposed model and computational schemes numerically on a multi-portfolio optimization problem and a capital allocation problem.
Dynamics of Laser-Charged UAVs: A Battery Perspective
Authors: Wael Jaafar, Halim Yanikomeroglu
Date: 2020-08-31 00:00:00

In this paper, we aim to sustain unmanned aerial vehicle (UAV) based missions for longer periods of times through different techniques. First, we consider on-the-mission UAV recharging by a low-power laser source (below 1 kilowatt). In order to achieve the maximal energy gain from the low-power laser source, we propose an operational compromise, which consists of the UAV resting over buildings with cleared line-of-sight to the laser source. Second, to provide a precise energy consumption/harvesting estimation at the UAV, we investigate the latter's dynamics in a mission environment. Indeed, we study the UAV's battery dynamics by leveraging the electrical models for motors and battery. Subsequently, using these models, the path planning problem in a particular Internet-of-Things based use-case is revisited from the battery perspective. The objective is to extend the UAV's operation time using both laser-charging and accurate battery level estimation. Through a graph theory approach, the problem is solved optimally, and compared to benchmark trajectory approaches. Numerical results demonstrate the efficiency of this novel battery perspective for all path planning approaches. In contrast, we found that the energy perspective is very conservative and does not exploit optimally the available energy resources. Nevertheless, we propose a simple adjustment method to correct the energy perspective, by carefully evaluating the energy as a function of the UAV motion regimes. Finally, the impact of several parameters, such as turbulence and distance to charging source, is studied.
Iterative Symbol Recovery For Power Efficient DC Biased Optical OFDM Systems
Authors: Amir Weiss, Arie Yeredor, Mark Shtaif
Date: 2020-08-31 00:00:00

Orthogonal frequency division multiplexing (OFDM) has proven itself as an effective multi-carrier digital communication technique. In recent years the interest in optical OFDM has grown significantly, due to its spectral efficiency and inherent resilience to frequency-selective channels and to narrowband interference. For these reasons it is currently considered to be one of the leading candidates for deployment in short fiber links such as the ones intended for inter-data-center communications. In this paper we present a new power-efficient symbol recovery scheme for dc-biased optical OFDM (DCO-OFDM) in an intensity-modulation direct-detection (IM/DD) system. We introduce an alternative method for clipping in order to maintain a non-negative real-valued signal and still preserve information, which is lost when using clipping, and propose an iterative detection algorithm for this method. A reduction of $50\%$ in the transmitted optical power along with an increase of signal-independent noise immunity (gaining $3$[dB] in SNR), compared to traditional DCO-OFDM with a DC bias of $2$ standard deviations of the OFDM signal, is attained by our new scheme for a symbol error rate (SER) of $10^{-3}$ in a QPSK constellation additive white gaussian noise (AWGN) flat channel model.
Securing the Inter-Spacecraft Links: Doppler Frequency Shift based Physical Layer Key Generation
Authors: Ozan Alp Topal, Gunes Karabulut Kurt, Halim Yanikomeroglu
Date: 2020-08-31 00:00:00

We propose a novel physical layer secret key generation method for the inter-spacecraft communication links. By exploiting the Doppler frequency shifts of the reciprocal spacecraft links as a unique secrecy source, spacecrafts aim to obtain identical secret keys from their individual observations. We obtain theoretical expressions for the key disagreement rate (KDR). Using generalized Gauss-Laguerre quadrature, we derive closed form expressions for the KDR. Through numerical studies, the tightness of the provided approximations are shown. Both the theoretical and numerical results demonstrate the validity and the practicality of the presented physical layer key generation procedure considering the security of the communication links of spacecrafts.
Privacy-Preserving Distributed Zeroth-Order Optimization
Authors: C. Gratton, N. K. D. Venkategowda, R. Arablouei, S. Werner
Date: 2020-08-31 00:00:00
Tasks: Privacy Preserving

We develop a privacy-preserving distributed algorithm to minimize a regularized empirical risk function when the first-order information is not available and data is distributed over a multi-agent network. We employ a zeroth-order method to minimize the associated augmented Lagrangian function in the primal domain using the alternating direction method of multipliers (ADMM). We show that the proposed algorithm, named distributed zeroth-order ADMM (D-ZOA), has intrinsic privacy-preserving properties. Unlike the existing privacy-preserving methods based on the ADMM where the primal or the dual variables are perturbed with noise, the inherent randomness due to the use of a zeroth-order method endows D-ZOA with intrinsic differential privacy. By analyzing the perturbation of the primal variable, we show that the privacy leakage of the proposed D-ZOA algorithm is bounded. In addition, we employ the moments accountant method to show that the total privacy leakage grows sublinearly with the number of ADMM iterations. D-ZOA outperforms the existing differentially private approaches in terms of accuracy while yielding the same privacy guarantee. We prove that D-ZOA converges to the optimal solution at a rate of $\mathcal{O}(1/M)$ where $M$ is the number of ADMM iterations. The convergence analysis also reveals a practically important trade-off between privacy and accuracy. Simulation results verify the desirable privacy-preserving properties of D-ZOA and its superiority over a state-of-the-art algorithm as well as its network-wide convergence to the optimal solution.
The behavior of stock market prices throughout the episodes of capital inflows
Authors: Boubekeur Baba, Guven Sevil
Date: 2020-08-31 00:00:00
Tasks: Clustering

This study aims to investigate the behavior of stock prices throughout the episodes of foreign capital flows using data of daily stock prices and quarterly foreign capital flows from 14 EMEs. To this end, the episodes of capital flows are identified using the threshold and the k-means clustering approaches. Next, the stock index changepoints are detected using the Pruned Exact Linear Time (PELT) method. Finally, we combine the results by distributing the detected changepoints over the identified capital flows. The results reveal that the stock indices have been rarely pushed further during the entire surge episodes identified by both approaches, and thus surges of capital flows do not necessarily lead to further appreciation of stock prices. In the meantime, a significant appreciation of stock prices is observed during the normal state of capital flows. On the other hand, it is noticed that the stock prices have not often depreciated during the episodes of foreign capital outflows in all the selected EMEs, which means that stock prices have been less vulnerable to reversals of foreign capital flows
Modeling the Impact of 5G Leakage on Weather Prediction
Authors: Mohammad Yousefvand, Chung-Tse Michael Wu, Ruo-Qian Wang, Joseph Brodie, Narayan Mandayam
Date: 2020-08-31 00:00:00
Tasks: Prediction, Weather Forecasting

The 5G band allocated in the 26 GHz spectrum referred to as 3GPP band n258, has generated a lot of anxiety and concern in the meteorological data forecasting community including the National Oceanic and Atmospheric Administration (NOAA). Unlike traditional spectrum coexistence problems, the issue here stems from the leakage of n258 band transmissions impacting the observations of passive sensors (e.g. AMSU-A) operating at 23.8 GHz on weather satellites used to detect the amount of water vapor in the atmosphere, which in turn affects weather forecasting and predictions. In this paper, we study the impact of 5G leakage on the accuracy of data assimilation based weather prediction algorithms by using a first order propagation model to characterize the effect of the leakage signal on the brightness temperature (atmospheric radiance) and the induced noise temperature at the receiving antenna of the passive sensor (radiometer) on the weather observation satellite. We then characterize the resulting inaccuracies when using the Weather Research and Forecasting Data Assimilation model (WRFDA) to predict temperature and rainfall. For example, the impact of 5G leakage of -20dBW to -15dBW on the well-known Super Tuesday Tornado Outbreak data set, affects the meteorological forecasting up to 0.9 mm in precipitation and 1.3 {\deg}C in 2m-temperature. We outline future directions for both improved modeling of 5G leakage effects as well as mitigation using cross-layer antenna techniques coupled with resource allocation.
Random Walk for modelling Multi Core Fiber cross-talk and step distribution characterisation
Authors: Alessandro Ottino, Hui Yuan, Yunnuo Xu, Eric Sillekens, Georgios Zervas
Date: 2020-08-31 00:00:00

A novel random walk based model for inter-core cross-talk (IC-XT) characterization of multi-core fibres capable of accurately representing both time-domain distribution and frequency-domain representation of experimental IC-XT has been proposed. It was demonstrated that this model is a generalization of the most widely used model in literature to which it will converge when the number of samples and measurement time-window tend to infinity. In addition, this model is consistent with statistical analysis such as short term average crosstalk (STAXT), keeping the same convergence properties and it showed to be almost independent to time-window. To validate this model, a new type of characterization of the IC-XT in the dB domain (based on a pseudo random walk) has been proposed and the statistical properties of its step distribution have been evaluated. The performed analysis showed that this characterization is capable of fitting every type of signal source with an accuracy above 99.3%. It also proved to be very robust to time-window length, temperature and other signal properties such as symbol rate and pseudo-random bit stream (PRBS) length. The obtained results suggest that the model was able to communicate most of the relevant information using a short observation time, making it suitable for IC-XT characterization and core-pair source signal classification. Using machine-learning (ML) techniques for source-signal classification, we empirically demonstrated that this technique carries more information regarding IC-XT than traditional statistical methods.
Mode-dependent Loss and Gain Estimation in SDM Transmission Based on MMSE Equalizers
Authors: Ruby S. B. Ospina, Menno van den Hout, Juan Carlos Alvarado-Zacarias, Jose Enrique Antonio-López, Marianne Bigot-Astruc, Adrian Amezcua Correa, Pierre Sillard, Rodrigo Amezcua-Correa, Chigo Okonkwo, Darli A. A. Mello
Date: 2020-08-31 00:00:00

The capacity in space division multiplexing (SDM) systems with coupled channels is fundamentally limited by mode-dependent loss (MDL) and mode-dependent gain (MDG) generated in components and amplifiers. In these systems, MDL/MDG must be accurately estimated for performance analysis and troubleshooting. Most recent demonstrations of SDM with coupled channels perform MDL/MDG estimation by digital signal processing (DSP) techniques based on the coefficients of multiple-input multiple-output (MIMO) adaptive equalizers. Although these methods provide a valid indication of the order of magnitude of the accumulated MDL/MDG over the link, MIMO equalizers are usually updated according to the minimum mean square error (MMSE) criterion, which is known to depend on the channel signal-to-noise ratio (SNR). Therefore, MDL/MDG estimation techniques based on the adaptive filter coefficients are also impaired by noise. In this paper, we model analytically the influence of the SNR on DSP-based MDL/MDG estimation, and show that the technique is prone to errors. Based on the transfer function of MIMO MMSE equalizers, and assuming a known SNR, we calculate a correction factor that improves the estimation process in moderate levels of MDL/MDG and SNR. The correction factor is validated by simulation of a 6-mode long-haul transmission link, and experimentally using a 3-mode transmission link. The results confirm the limitations of the standard estimation method in scenarios of high additive noise and MDL/MDG, and indicate the correction factor as a possible solution in practical SDM scenarios.
Mind The Gap: Real-time Decentralized Distance Estimation using Ultrasound and Bluetooth across Multiple Smartphones
Authors: Devansh R. Agrawal, Peter Lyon, Martin Frobisher, Andy Doherty, Ben Allen, Freddie Rawlins
Date: 2020-08-28 00:00:00

Robust, low-cost solutions are needed to maintain social distancing guidelines during the COVID-19 pandemic. We establish a method to measure the distance between multiple phones across a large number of closely spaced smartphones with a median absolute error of 8.5~cm. The application works in real-time, using Time of Flight of near-ultrasound signals, providing alerts with sufficient responsiveness to be useful for distancing while devices are in users pockets and they are moving at walking speed. The approach is decentralized, requires no additional hardware, and can operate in the background without an internet connection. We have no device-specific requirements nor need any manual calibration or device synchronization. It has been tested with over 20 different phones models, from both the Android and iOS systems in the past 5 years. To the best of our knowledge, this is the first successful such implementation, and has 25000 users at time of publishing.