pwc-archive/papers-with-abstracts huggingface dataset:
Hardware-Accelerated SAR Simulation with NVIDIA-RTX Technology
Date: 2020-05-19 00:00:00
Tasks: CPU, GPU

Synthetic Aperture Radar (SAR) is a critical sensing technology that is
notably independent of the sensor-to-target distance and has numerous
cross-cutting applications, e.g., target recognition, mapping, surveillance,
oceanography, geology, forestry (biomass, deforestation), disaster monitoring
(volcano eruptions, oil spills, flooding), and infrastructure tracking (urban
growth, structure mapping). SAR uses a high-power antenna to illuminate target
locations with electromagnetic radiation, e.g., 10GHz radio waves, and
illuminated surface backscatter is sensed by the antenna which is then used to
generate images of structures. Real SAR data is difficult and costly to produce
and, for research, lacks a reliable source ground truth. This article proposes
a open source SAR simulator to compute phase histories for arbitrary 3D scenes
using newly available ray-tracing hardware made available commercially through
the NVIDIA's RTX graphics cards series. The OptiX GPU ray tracing library for
NVIDIA GPUs is used to calculate SAR phase histories at unprecedented
computational speeds. The simulation results are validated against existing SAR
simulation code for spotlight SAR illumination of point targets. The
computational performance of this approach provides orders of magnitude speed
increases over CPU simulation. An additional order of magnitude of GPU
acceleration when simulations are run on RTX GPUs which include hardware
specifically to accelerate OptiX ray tracing. The article describes the OptiX
simulator structure, processing framework and calculations that afford
execution on massively parallel GPU computation device. The shortcoming of the
OptiX library's restriction to single precision float representation is
discussed and modifications of sensitive calculations are proposed to reduce
truncation error thereby increasing the simulation accuracy under this
constraint.
Digraph Signal Processing with Generalized Boundary Conditions
Authors: Bastian Seifert, Markus Püschel
Date: 2020-05-19 00:00:00

Signal processing on directed graphs (digraphs) is problematic, since the graph shift, and thus associated filters, are in general not diagonalizable. Furthermore, the Fourier transform in this case is now obtained from the Jordan decomposition, which may not be computable at all for large graphs. We propose a novel and general solution for this problem based on matrix perturbation theory: We design an algorithm that adds a small number of edges to a given digraph to destroy nontrivial Jordan blocks. The obtained digraph is then diagonalizable and yields, as we show, an approximate eigenbasis and Fourier transform for the original digraph. We explain why and how this construction can be viewed as generalized form of boundary conditions, a common practice in signal processing. Our experiments with random and real world graphs show that we can scale to graphs with a few thousands nodes, and obtain Fourier transforms that are close to orthogonal while still diagonalizing an intuitive notion of convolution. Our method works with adjacency and Laplacian shift and can be used as preprocessing step to enable further processing as we show with a prototypical Wiener filter application.
Pairs Trading with Nonlinear and Non-Gaussian State Space Models
Authors: Guang Zhang
Date: 2020-05-19 00:00:00
Tasks: State Space Models

This paper studies pairs trading using a nonlinear and non-Gaussian state-space model framework. We model the spread between the prices of two assets as an unobservable state variable and assume that it follows a mean-reverting process. This new model has two distinctive features: (1) The innovations to the spread is non-Gaussianity and heteroskedastic. (2) The mean reversion of the spread is nonlinear. We show how to use the filtered spread as the trading indicator to carry out statistical arbitrage. We also propose a new trading strategy and present a Monte Carlo based approach to select the optimal trading rule. As the first empirical application, we apply the new model and the new trading strategy to two examples: PEP vs KO and EWT vs EWH. The results show that the new approach can achieve a 21.86% annualized return for the PEP/KO pair and a 31.84% annualized return for the EWT/EWH pair. As the second empirical application, we consider all the possible pairs among the largest and the smallest five US banks listed on the NYSE. For these pairs, we compare the performance of the proposed approach with that of the existing popular approaches, both in-sample and out-of-sample. Interestingly, we find that our approach can significantly improve the return and the Sharpe ratio in almost all the cases considered.
Distributed Dynamic Economic Dispatch using Alternating Direction Method of Multipliers
Date: 2020-05-20 00:00:00
Tasks: Distributed Computing, energy management, Management

With the proliferation of distributed energy resources and the volume of data
stored due to advancement in metering infrastructure, energy management in
power system operation needs distributed computing. In this paper, we propose a
fully distributed Alternating Direction Method of Multipliers (ADMM) algorithm
to solve the distributed economic dispatch (ED) problem, where the optimization
problem is fully decomposed between participating agents. In our proposed
framework, each agent estimates the dual variable and the average of the total
power mismatch of the network using dynamic average consensus, which replaces
the dual updater in the traditional ADMM with a distributed alternative. Unlike
other distributed ADMM, the proposed method does not rely on any specific
assumption and captures the real-time demand change. The algorithm is validated
successfully via case studies for IEEE 30-bus and 300-bus test systems with the
penetration of solar photovoltaic.
Convolutional Neural Network for Behavioral Modeling and Predistortion of Wideband Power Amplifiers
Authors: Xin Hu, Zhijun Liu, Xiaofei Yu, Yulong Zhao, WenHua Chen, Biao Hu, Xuekun Du, Xiang Li, Mohamed Helaoui, Weidong Wang, Fadhel M. Ghannouchi
Date: 2020-05-20 00:00:00

In this paper, we propose a novel behavior model for wideband PAs using a real-valued time-delay convolutional neural network (RVTDCNN). The input data of the model are sorted and arranged as the graph composed of the in-phase and quadrature (I/Q) components and envelope-dependent terms of current and past signals. We design a pre-designed filter using the convolutional layer to extract the basis functions required for the PA forward or reverse modeling. The generated rich basis functions are modeled using a simple fully connected layer. Because of the weight sharing characteristics of the convolutional structure, the strong memory effect does not lead to a obvious increase in the complexity of the model. Meanwhile, the extraction effect of the pre-designed filter also reduces the training complexity of the model. The experimental results show that the performance of the RVTDCNN model is almost the same as the NN models and the multilayer NN models.
Fast Wavelet Decomposition of Linear Operators through Product-Convolution Expansions
Authors: Paul Escande, Pierre Weiss
Date: 2020-05-20 00:00:00

Wavelet decompositions of integral operators have proven their efficiency in reducing computing times for many problems, ranging from the simulation of waves or fluids to the resolution of inverse problems in imaging. Unfortunately, computing the decomposition is itself a hard problem which is oftentimes out of reach for large scale problems. The objective of this work is to design fast decomposition algorithms based on another representation called product-convolution expansion. This decomposition can be evaluated efficiently assuming that a few impulse responses of the operator are available, but it is usually less efficient than the wavelet decomposition when incorporated in iterative methods. The proposed decomposition algorithms, run in quasi-linear time and we provide some numerical experiments to assess its performance for an imaging problem involving space varying blurs.
Consistent ICA: Determined BSS meets spectrogram consistency
Authors: Kohei Yatabe
Date: 2020-05-20 00:00:00
Tasks: blind source separation

Multichannel audio blind source separation (BSS) in the determined situation (the number of microphones is equal to that of the sources), or determined BSS, is performed by multichannel linear filtering in the time-frequency domain to handle the convolutive mixing process. Ordinarily, the filter treats each frequency independently, which causes the well-known permutation problem, i.e., the problem of how to align the frequency-wise filters so that each separated component is correctly assigned to the corresponding sources. In this paper, it is shown that the general property of the time-frequency-domain representation called spectrogram consistency can be an assistant for solving the permutation problem.
Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments
Date: 2020-07-28 00:00:00
Tasks: Action Detection, Activity Detection

Speech activity detection (SAD), which often rests on the fact that the noise
is "more" stationary than speech, is particularly challenging in non-stationary
environments, because the time variance of the acoustic scene makes it
difficult to discriminate speech from noise. We propose two approaches to SAD,
where one is based on statistical signal processing, while the other utilizes
neural networks. The former employes sophisticated signal processing to track
the noise and speech energies and is meant to support the case for a resource
efficient, unsupervised signal processing approach. The latter introduces a
recurrent network layer that operates on short segments of the input speech to
do temporal smoothing in the presence of non-stationary noise. The systems are
tested on the Fearless Steps challenge, which consists of the transmission data
from the Apollo-11 space mission. The statistical SAD achieves comparable
detection performance to earlier proposed neural network based SADs, while the
neural network based approach leads to a decision cost function of 1.07% on the
evaluation set of the 2020 Fearless Steps Challenge, which sets a new state of
the art.
Optical Wake-up from Power-off State for Autonomous Optical Sensor Nodes
Authors: Uliana Dudko, Ludger Overmeyer
Date: 2020-05-20 00:00:00

Wireless sensor nodes spend most of their time in standby mode and wake up periodically to send the measurement data. Conventionally, the wake-up function is realized using a radio-frequency oscillator with an amplifier to recognize an activating radio signal. Therefore, even during standby operation, the sensor node utilizes a certain amount of energy, which can be critical for an energy-harvesting source. In this study, we propose a novel approach of an optical wake-up for autonomous sensor nodes, which employs a solar cell as a wake-up signal detector. The bright light flash coming from another node or a smartphone exposes a solar cell, which activates the sensor node. Unlike photodiodes or RF-antennas, solar cells do not require any additional energy to detect the light signal. Therefore, the proposed electric circuit allows the sensor node to wake-up from a complete power-off state. The solar cell of the novel wake-up receiver has a sensitive area of 8 mm x 10 mm. The wake-up signal can be recognized from a maximal distance of 25 cm at ambient illumination from 0 to 1600 lx with a transmitter optical power of 20 mW. At power-off state the power consumptions are the lowest among all existing off-the-shelf wake-up receivers: 248 pW at 0 lx and 627 nW at 1600 lx.
Stochastic modeling of assets and liabilities with mortality risk
Authors: Sergio Alvares Maffra, John Armstrong, Teemu Pennanen
Date: 2020-05-20 00:00:00

This paper describes a general approach for stochastic modeling of assets returns and liability cash-flows of a typical pensions insurer. On the asset side, we model the investment returns on equities and various classes of fixed-income instruments including short- and long-maturity fixed-rate bonds as well as index-linked and corporate bonds. On the liability side, the risks are driven by future mortality developments as well as price and wage inflation. All the risk factors are modeled as a multivariate stochastic process that captures the dynamics and the dependencies across different risk factors. The model is easy to interpret and to calibrate to both historical data and to forecasts or expert views concerning the future. The simple structure of the model allows for efficient computations. The construction of a million scenarios takes only a few minutes on a personal computer. The approach is illustrated with an asset-liability analysis of a defined benefit pension fund.
A new formulation of compartmental epidemic modelling for arbitrary distributions of incubation and removal times
Authors: P. Hernández, C. Pena, A. Ramos, J. J. Gómez-Cadenas
Date: 2020-05-20 00:00:00
Tasks: Clustering, Epidemiology

The paradigm for compartment models in epidemiology assumes exponentially distributed incubation and removal times, which is not realistic in actual populations. Commonly used variations with multiple exponentially distributed variables are more flexible, yet do not allow for arbitrary distributions. We present a new formulation, focussing on the SEIR concept that allows to include general distributions of incubation and removal times. We compare the solution to two types of agent-based model simulations, a spatially homogeneous one where infection occurs by proximity, and a model on a scale-free network with varying clustering properties, where the infection between any two agents occurs via their link if it exists. We find good agreement in both cases. Furthermore a family of asymptotic solutions of the equations is found in terms of a logistic curve, which after a non-universal time shift, fits extremely well all the microdynamical simulations. The formulation allows for a simple numerical approach; software in Julia and Python is provided.
Evaluating Features and Metrics for High-Quality Simulation of Early Vocal Learning of Vowels
Authors: Branislav Gerazov, Daniel van Niekerk, Anqi Xu, Paul Konstantin Krug, Peter Birkholz, Yi Xu
Date: 2020-05-20 00:00:00
Tasks: Speech Synthesis

The way infants use auditory cues to learn to speak despite the acoustic mismatch of their vocal apparatus is a hot topic of scientific debate. The simulation of early vocal learning using articulatory speech synthesis offers a way towards gaining a deeper understanding of this process. One of the crucial parameters in these simulations is the choice of features and a metric to evaluate the acoustic error between the synthesised sound and the reference target. We contribute with evaluating the performance of a set of 40 feature-metric combinations for the task of optimising the production of static vowels with a high-quality articulatory synthesiser. Towards this end we assess the usability of formant error and the projection of the feature-metric error surface in the normalised F1-F2 formant space. We show that this approach can be used to evaluate the impact of features and metrics and also to offer insight to perceptual results.
Blocking of the CD80/86 axis as a therapeutic approach to prevent progression to more severe forms of COVID-19
Authors: Antonio Julià, Irene Bonafonte, Antonio Gómez, María López-Lasanta, Mireia López-Corbeto, Sergio H. Martínez-Mateu, Jordi Lladós, Iván Rodríguez-Nunez, Richard M. Myers, Sara Marsal
Date: 2020-05-20 00:00:00
Tasks: Blocking

In its more severe forms, COVID-19 progresses towards an excessive immune response, leading to the systemic overexpression of proinflammatory cytokines like IL6, mostly from the infected lungs. This cytokine storm can cause multiple organ damage and death. Consequently, there is a pressing need to identify therapies to treat and prevent severe symptoms during COVID-19. Based on previous clinical evidence, we hypothesized that inhibiting T cell co-stimulation by blocking CD80/86 could be an effective therapeutic strategy against progression to severe proinflammatory states. To support this hypothesis, we performed an analysis integrating blood transcriptional data we generated from rheumatoid arthritis patients treated with abatacept -- a CD80/86 co-stimulation inhibitor -- with the pathological features associated with COVID-19, particularly in its more severe forms. We have found that many of the biological processes that have been consistently associated with COVID-19 pathology are reversed by CD80/86 co-stimulation inhibition, including the downregulation of IL6 production. Also, analysis of previous transcriptional data from blood of SARS-CoV-infected patients showed that the response to abatacept has a very high level of antagonism to that elicited by COVID-19. Finally, analyzing a recent single cell RNA-seq dataset from bronchoalveolar lavage fluid cells from COVID-19 patients, we found a significant correlation along the main elements of the C80/86 axis: CD86+/80+ antigen presenting cells, activated CD4+ T cells and IL6 production. Our in-silico study provides additional support to the hypothesis that blocking of the CD80/CD86 signaling axis may be protective of the excessive proinflammatory state associated with COVID-19 in the lungs.
Recipes for hedging exotics with illiquid vanillas
Authors: Joaquin Fernandez-Tapia, Olivier Guéant
Date: 2020-05-20 00:00:00

In this paper, we address the question of the optimal Delta and Vega hedging of a book of exotic options when there are execution costs associated with the trading of vanilla options. In a framework where exotic options are priced using a market model (e.g. a local volatility model recalibrated continuously to vanilla option prices) and vanilla options prices are driven by a stochastic volatility model, we show that, using simple approximations, the optimal dynamic Delta and Vega hedging strategies can be computed easily using variational techniques.
Reduction of valuation risk by Kalman filtering in business valuation models
Authors: Rene Scheurwater
Date: 2020-05-20 00:00:00

A recursive free cash flow model (FCFF) is proposed to determine the corporate value of a company in an efficient market in which new market and company-specific information is modelled by additive white noise. The stochastic equations of the FCFF model are solved explicitly to obtain the average corporate value and valuation risk. It is pointed out that valuation risk can be reduced significantly by implementing a conventional two-step Kalman filter in the recursive FCFF model, thus improving its predictive power. Systematic errors of the Kalman filter, caused by intermediate changes in risk and hence in the weighted average cost of capital (WACC), are detected by measuring the residuals. By including an additional adjustment step in the conventional Kalman filtering algorithm, it is shown that systematic errors can be eliminated by recursively adjusting the WACC. The performance of the three-step adaptive Kalman filter is tested by Monte Carlo simulation which demonstrates the reliability and robustness against systematic errors. It is also proved that the conventional and adaptive Kalman filtering algorithms can be implemented into other valuation models such as the economic value added model (EVA) and free cash flow to equity model (FCFE).
NOMA for Energy-Efficient LiFi-Enabled Bidirectional IoT Communication
Authors: Chen Chen, Shu Fu, Xin Jian, Min Liu, Xiong Deng, Zhiguo Ding
Date: 2020-05-20 00:00:00

In this paper, we consider a light fidelity (LiFi)-enabled bidirectional Internet of Things (IoT) communication system, where visible light and infrared light are used in the downlink and uplink, respectively. In order to improve the energy efficiency (EE) of the bidirectional LiFi-IoT system, non-orthogonal multiple access (NOMA) with a quality-of-service (QoS)-guaranteed optimal power allocation (OPA) strategy is applied to maximize the EE of the system. We derive a closed-form OPA set based on the identification of the optimal decoding orders in both downlink and uplink channels, which can enable low-complexity power allocation. Moreover, we propose an adaptive channel and QoS-based user pairing approach by jointly considering users' channel gains and QoS requirements. We further analyze the EE of the bidirectional LiFi-IoT system and the user outage probabilities (UOPs) of both downlink and uplink channels of the system. Extensive analytical and simulation results demonstrate the superiority of NOMA with OPA in comparison to orthogonal multiple access (OMA) and NOMA with typical channel-based power allocation strategies. It is also shown that the proposed adaptive channel and QoS-based user pairing approach greatly outperforms individual channel/QoS-based approaches, especially when users have diverse QoS requirements.
L2-Stability Analysis of The Set-Membership Affine Projection Algorithm
Authors: Rajab Shabaani
Date: 2020-05-11 00:00:00

In this letter, we study the local and the global robustness of the set-membership affine projection (SM-AP) algorithm. We demonstrate that the SM-AP algorithm has l2-stability. In fact, the SM-AP algorithm never diverges; no matter how the parameters of the SM-AP algorithm has been adopted. Finally, the simulation results substantiate the validity of the proposed analysis.
Finite Mixture Approximation of CARMA(p,q) Models
Authors: Lorenzo Mercuri, Andrea Perchiazzo, Edit Rroji
Date: 2020-05-20 00:00:00

In this paper we show how to approximate the transition density of a CARMA(p, q) model driven by means of a time changed Brownian Motion based on the Gauss-Laguerre quadrature. We then provide an analytical formula for option prices when the log price follows a CARMA(p, q) model. We also propose an estimation procedure based on the approximated likelihood density.
Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform
Authors: Yuliang Sun, Tai Fei, Xibo Li, Alexander Warnecke, Ernst Warsitz, Nils Pohl
Date: 2020-05-20 00:00:00
Tasks: Action Detection, Activity Detection, Edge-computing, Gesture Recognition

In this paper, a real-time signal processing frame-work based on a 60 GHz frequency-modulated continuous wave (FMCW) radar system to recognize gestures is proposed. In order to improve the robustness of the radar-based gesture recognition system, the proposed framework extracts a comprehensive hand profile, including range, Doppler, azimuth and elevation, over multiple measurement-cycles and encodes them into a feature cube. Rather than feeding the range-Doppler spectrum sequence into a deep convolutional neural network (CNN) connected with recurrent neural networks, the proposed framework takes the aforementioned feature cube as input of a shallow CNN for gesture recognition to reduce the computational complexity. In addition, we develop a hand activity detection (HAD) algorithm to automatize the detection of gestures in real-time case. The proposed HAD can capture the time-stamp at which a gesture finishes and feeds the hand profile of all the relevant measurement-cycles before this time-stamp into the CNN with low latency. Since the proposed framework is able to detect and classify gestures at limited computational cost, it could be deployed in an edge-computing platform for real-time applications, whose performance is notedly inferior to a state-of-the-art personal computer. The experimental results show that the proposed framework has the capability of classifying 12 gestures in real-time with a high F1-score.