pwc-archive/papers-with-abstracts huggingface dataset:
Conformal Metasurfaces: a Novel Solution for Vehicular Communications
Authors: Marouan Mizmizi, Reza Aghazadeh Ayoubi, Dario Tagliaferri, Kai Dong, Gian Guido Gentili, Umberto Spagnolini
Date: 2022-01-21 00:00:00

In future 6G millimeter wave (mmWave)/sub-THz vehicle-to-everything (V2X) communication systems, vehicles are expected to be equipped with massive antenna arrays to realize beam-based links capable of compensating for the severe path loss. However, vehicle-to-vehicle (V2V) direct links are prone to be blocked by surrounding vehicles. Emerging metasurface technologies enable the control of the electromagnetic wave reflection towards the desired direction, enriching the channel scattering to boost communication performance. Reconfigurable intelligent surfaces (RIS), and mostly the pre-configured counterpart intelligent reflecting surfaces (IRS), are a promising low-cost relaying system for 6G. This paper proposes using conformal metasurfaces (either C-RIS or C-IRS) deployed on vehicles' body to mitigate the blockage impact in a highway multi-lane scenario. In particular, conformal metasurfaces create artificial reflections to mitigate blockage by compensating for the non-flat shape of the vehicle's body, such as the lateral doors, with proper phase patterns. We analytically derive the phase pattern to apply to a cylindrical C-RIS/C-IRS approximating the shape of the car body, as a function of both incidence and reflection angles, considering cylindrical RIS/IRS as a generalization of conventional planar ones. We propose a novel design for optimally pre-configured C-IRS to mimic the behavior of an EM flat surface on car doors, proving the benefits of C-RIS and C-IRS in a multi-lane V2V highway scenario. The results show a consistent reduction of blockage probability when exploiting C-RIS/C-IRS, 20% for pre-configured C-IRS, and 70% for C-RIS, as well as a remarkable improvement in terms of average signal-to-noise ratio, respectively 10-20 dB for C-IRS and 30-40 dB for C-RIS.
Moment Propagation of Polynomial Systems Through Carleman Linearization for Probabilistic Safety Analysis
Authors: Sasinee Pruekprasert, Jérémy Dubut, Toru Takisaka, Clovis Eberhart, Ahmet Cetinkaya
Date: 2022-01-21 00:00:00

We develop a method to approximate the moments of a discrete-time stochastic polynomial system. Our method is built upon Carleman linearization with truncation. Specifically, we take a stochastic polynomial system with finitely many states and transform it into an infinite-dimensional system with linear deterministic dynamics, which describe the exact evolution of the moments of the original polynomial system. We then truncate this deterministic system to obtain a finite-dimensional linear system, and use it for moment approximation by iteratively propagating the moments along the finite-dimensional linear dynamics across time. We provide efficient online computation methods for this propagation scheme with several error bounds for the approximation. Our results also show that precise values of certain moments at a given time step can be obtained when the truncated system is sufficiently large. Furthermore, we investigate techniques to reduce the offline computation load using reduced Kronecker power. Based on the obtained approximate moments and their errors, we also provide hyperellipsoidal regions that are safe for some given probability bound. Those bounds allow us to conduct probabilistic safety analysis online through convex optimization. We demonstrate our results on a logistic map with stochastic dynamics and a vehicle dynamics subject to stochastic disturbance.
RHONN Modelling-enabled Nonlinear Predictive Control for Lateral Dynamics Stabilization of An In-wheel Motor Driven Vehicle
Authors: Hao Chen, Junzhi Zhang, Chen Lv
Date: 2022-01-21 00:00:00
Tasks: Model Predictive Control

Featuring the fast response and flexibility in control allocation, an electric vehicle with in-wheel motors is a good platform for implementing advanced vehicle dynamics control. Among many active safety functions of an in-wheel motor driven vehicle (IMDV), lateral stability control is a key technology, which can be realized through torque vectoring. To further advance the lateral stabilization performance of the IMDV, in this paper a novel data-driven nonlinear model predictive control (NMPC) is proposed based the recurrent high-order neural network (RHONN) modelling method. First, the new RHONN model is developed to represent vehicle's nonlinear dynamic behaviors. Different from the conventional physics-based modelling method, the RHONN model only needs data and forms high-order polynomials. Based on the RHONN model, the steady-state responses of vehicle's yaw rate and sideslip angle are iteratively optimized and set as the control objectives for low-level controller, aiming to improve the system robustness. Besides, a nonlinear model predictive controller is designed based on the RHONN, which is expected to improve the prediction accuracy during the receding horizon control. Further, a constrained optimization problem is formulated to derive the required yaw moment for vehicle lateral dynamics stabilization. Finally, the performance of the developed RHONN-based nonlinear MPC is validated on an IMDV in the CarSim/Simulink simulation environment. The validation results show that the developed approach outperforms the conventional method, and further improves the stable margin of the system. It is able to enhance the lateral stabilization performance of the IMDV under various driving scenarios, demonstrating the feasibility and effectiveness of the proposed approach.
Minimax-Regret Climate Policy with Deep Uncertainty in Climate Modeling and Intergenerational Discounting
Authors: Stephen J. DeCanio, Charles F. Manski, Alan H. Sanstad
Date: 2022-01-21 00:00:00

Integrated assessment models have become the primary tools for comparing climate policies that seek to reduce greenhouse gas emissions. Policy comparisons have often been performed by considering a planner who seeks to make optimal trade-offs between the costs of carbon abatement and the economic damages from climate change. The planning problem has been formalized as one of optimal control, the objective being to minimize the total costs of abatement and damages over a time horizon. Studying climate policy as a control problem presumes that a planner knows enough to make optimization feasible, but physical and economic uncertainties abound. Earlier, Manski, Sanstad, and DeCanio proposed and studied use of the minimax-regret (MMR) decision criterion to account for deep uncertainty in climate modeling. Here we study choice of climate policy that minimizes maximum regret with deep uncertainty regarding both the correct climate model and the appropriate time discount rate to use in intergenerational assessment of policy consequences. The analysis specifies a range of discount rates to express both empirical and normative uncertainty about the appropriate rate. The findings regarding climate policy are novel and informative. The MMR analysis points to use of a relatively low discount rate of 0.02 for climate policy. The MMR decision rule keeps the maximum future temperature increase below 2C above the 1900-10 level for most of the parameter values used to weight costs and damages.
High-Dimensional Sparse Multivariate Stochastic Volatility Models
Authors: Benjamin Poignard, Manabu Asai
Date: 2022-01-21 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Although multivariate stochastic volatility models usually produce more accurate forecasts compared to the MGARCH models, their estimation techniques such as Bayesian MCMC typically suffer from the curse of dimensionality. We propose a fast and efficient estimation approach for MSV based on a penalized OLS framework. Specifying the MSV model as a multivariate state space model, we carry out a two-step penalized procedure. We provide the asymptotic properties of the two-step estimator and the oracle property of the first-step estimator when the number of parameters diverges. The performances of our method are illustrated through simulations and financial data.
Profit Shifting of Multinational Corporations Worldwide
Authors: Javier Garcia-Bernardo, Petr Janský
Date: 2022-01-20 00:00:00

We exploit the new country-by-country reporting data of multinational corporations, with unparalleled country coverage, to reveal the distributional consequences of profit shifting. We estimate that multinational corporations worldwide shifted over \$850 billion in profits in 2017, primarily to countries with effective tax rates below 10\%. Countries with lower incomes lose a larger share of their total tax revenue due to profit shifting. We further show that a logarithmic function is better suited for capturing the non-linear relationship between profits and tax rates than linear or quadratic functions. Our findings highlight effective tax rates' importance for profit shifting and tax reforms.
Skyline variations allow estimating distance to trees on landscape photos using semantic segmentation
Authors: Laura Martinez-Sanchez, Daniele Borio, Raphaël d'Andrimont, Marijn van der Velde
Date: 2022-01-14 00:00:00
Tasks: Semantic Segmentation

Approximate distance estimation can be used to determine fundamental landscape properties including complexity and openness. We show that variations in the skyline of landscape photos can be used to estimate distances to trees on the horizon. A methodology based on the variations of the skyline has been developed and used to investigate potential relationships with the distance to skyline objects. The skyline signal, defined by the skyline height expressed in pixels, was extracted for several Land Use/Cover Area frame Survey (LUCAS) landscape photos. Photos were semantically segmented with DeepLabV3+ trained with the Common Objects in Context (COCO) dataset. This provided pixel-level classification of the objects forming the skyline. A Conditional Random Fields (CRF) algorithm was also applied to increase the details of the skyline signal. Three metrics, able to capture the skyline signal variations, were then considered for the analysis. These metrics shows a functional relationship with distance for the class of trees, whose contours have a fractal nature. In particular, regression analysis was performed against 475 ortho-photo based distance measurements, and, in the best case, a R2 score equal to 0.47 was achieved. This is an encouraging result which shows the potential of skyline variation metrics for inferring distance related information.
Ordered Transmission-based Detection in Distributed Networks in the Presence of Byzantines
Authors: Chen Quan, Saikiran Bulusu, Baocheng Geng, Pramod K. Varshney
Date: 2022-01-21 00:00:00

The ordered transmission (OT) scheme reduces the number of transmissions needed in the network to make the final decision, while it maintains the same probability of error as the system without using OT scheme. In this paper, we investigate the performance of the system using OT scheme in the presence of Byzantine attacks for binary hypothesis testing problem. We analyze the probability of error for the system under attack and evaluate the number of transmissions saved using Monte Carlo method. We also derive the bounds for the number of transmissions saved in the system under attack. The optimal attacking strategy for the OT-based system is investigated. Simulation results show that the Byzantine attacks have significant impact on the number of transmissions saved even when the signal strength is sufficiently large.
Tensor-based Basis Function Learning for Three-dimensional Sound Speed Fields
Authors: Lei Cheng, Xingyu Ji, Hangfang Zhao, Jianlong Li, Wen Xu
Date: 2022-01-21 00:00:00
Tasks: Tensor Decomposition

Basis function learning is the stepping stone towards effective three-dimensional (3D) sound speed field (SSF) inversion for various acoustic signal processing tasks, including ocean acoustic tomography, underwater target localization/tracking, and underwater communications. Classical basis functions include the empirical orthogonal functions (EOFs), Fourier basis functions, and their combinations. The unsupervised machine learning method, e.g., the K-SVD algorithm, has recently tapped into the basis function design, showing better representation performance than the EOFs. However, existing methods do not consider basis function learning approaches that treat 3D SSF data as a third-order tensor, and thus cannot fully utilize the 3D interactions/correlations therein. To circumvent such a drawback, basis function learning is linked to tensor decomposition in this paper, which is the primary drive for recent multi-dimensional data mining. In particular, a tensor-based basis function learning framework is proposed, which can include the classical basis functions (using EOFs and/or Fourier basis functions) as its special cases. This provides a unified tensor perspective for understanding and representing 3D SSFs. Numerical results using the South China Sea 3D SSF data have demonstrated the excellent performance of the tensor-based basis functions.
Performance Analysis of Hybrid RF-Reconfigurable Intelligent Surfaces Assisted FSO Communication
Authors: Haibo Wang, Zaichen Zhang, Bingcheng Zhu, Yidi Zhang
Date: 2022-01-21 00:00:00

Optical reconfigurable intelligent surface (ORIS) is an emerging technology that can achieve reconfigurable optical propagation environments by precisely adjusting signal's reflection and shape through a large number of passive reflecting elements. In this paper, we investigate the performance of an ORIS-assisted dual-hop hybrid radio frequency (RF) and free space optics (FSO) communication system. By jointly considering the physical models of ORIS, RF channel, atmospheric turbulence, and pointing error, the closed-form solutions of the system's precise outage probability, asymptotic outage probability and BER have been derived. It is shown through numerical results that the derivation results are accurate and the RF-FSO links with ORISs show a slightly worse performance than the traditional RF-FSO links. Based on theoretical analysis and simulation results, the system design and effect of each parameter have been discussed.
Underwater Node Localization using Optoacoustic Signals
Authors: Muntasir Mahmud, Mohamed Younis, Gary Carter, Fow-Sen Choa
Date: 2022-01-21 00:00:00

Localization of underwater networks is important in many military and civil applications. Because GPS receivers do not work below the water surface, traditional localization methods form a relative topology of underwater nodes (UWNs) and utilize either anchor nodes or floating gateways with dual transceivers in order to determine global coordinates. However, these methods introduce logistical complications and security risks in deploying the anchor and/or surface gateways. This paper tackles such an issue by proposing new localization techniques which can remotely localize UWNs using optoacoustic signals. In our approach, GPS coordinates are transmitted from air to the UWN via creating an underwater temporary isotropic acoustic transmitter with the optoacoustic process. We analyze the process of controlling the shape and size of the plasma to create the isotropic acoustic transmitter and experimentally validate the generation of isotropic acoustic signals. Then two methods of localization are proposed for static and dynamic UWNs. Finally, the simulation results with experimental values show the effectiveness of our approach. Comparing to the traditional techniques, our approach achieves the same accuracy without using any surface or underwater anchor nodes.
Noise-specific denoising method with applications to high-frequency ultrasonic images
Authors: Gonzalo D. Maso Talou, Pablo J. Blanco
Date: 2022-01-21 00:00:00
Tasks: Denoising, Vocal Bursts Intensity Prediction

Denoising is of utmost importance for the visualization and processing of images featuring low signal-to-noise ratio. Total variation methods are among the most popular techniques to perform this task improving the signal-to-noise ratio while preserving coherent intensity discontinuities. In this work, a novel method, termed maximum likelihood data, is proposed, endowing the total variation formulation with the capability to deal with noise-specific models and pre-processing stages for a certain image of interest. To do this, the data fidelity term is modified by means of a maximum likelihood estimator between the original and the denoised image. To assess the improvements of the proposed method with respect to the total variation formulation, we study the denoising of high-frequency ultrasonic images on in-silico and in-vivo setups. The proposed method delivered a better contrast, preservation and localization of the structures while diminishing the intensity bias of the total variation formulation for the multiplicative noise. The enhancement of medical images through denoising helps to improve the outcome of subsequently applied image processing such as registration and segmentation procedures.
On minimum phase transformation and filter design
Authors: JC Olivier, E. Barnard
Date: 2022-01-21 00:00:00

Minimum-phase finite impulse response filters are widely used in practice, and much research has been devoted to the design of such filters. However, for the important case of Chebyshev filters there is a curious mismatch between current best practice and well-established theoretical principles. The paper shows that this difference can be understood through analysis of the time-domain factorization of a suitable extended matrix. This analysis explains why the definition of a factorable linear phase filter must be revised. The time domain analysis of factorization suggests initial values leading to fast and accurate convergence of iterative algorithms for the design of minimum-phase finite impulse response filters. Numerical results are provided to demonstrate that a significant improvement in filter tap accuracy is obtained when the well-established theoretical principles are correctly applied to the design of minimum phase finite impulse response filters.
Development and validation of a targeted LC-MS/MS quantitation method to monitor cell culture expression of tetanus neurotoxin during vaccine production
Authors: Antoine Francotte, Raphael Esson, Eric Abachin, Melissa Vanhamme, Alexandre Dobly, Bruce Carpick, Sylvie Uhlrich, Jean-François Dierick, Celine Vanhee
Date: 2022-01-20 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

The tetanus neurotoxin (TeNT) is one of the most toxic proteins known to man, which prior to the use of the vaccine against the TeNT producing bacteria Clostridium tetani, resulted in a 20 % mortality rate upon infection. The clinical detrimental effects of tetanus have decreased immensely since the introduction of global vaccination programs, which depend on sustainable vaccine production. One of the major critical points in the manufacturing of these vaccines is the stable and reproducible production of high levels of toxin by the bacterial seed strains. In order to minimize time loss, the amount of TeNT is often monitored during and at the end of the bacterial culturing. The different methods that are currently available to assess the amount of TeNT in the bacterial medium suffer from variability, lack of sensitivity, and/or require specific antibodies. In accordance with the consistency approach and the three Rs (3Rs), both aiming to reduce the use of animals for testing, in-process monitoring of TeNT production could benefit from animal and antibody-free analytical tools. In this paper, we describe the development and validation of a new and reliable antibody free targeted LC-MS/MS method that is able to identify and quantify the amount of TeNT present in the bacterial medium during the different production time points up to the harvesting of the TeNT just prior to further upstream purification and detoxification. The quantitation method, validated according to ICH guidelines and by the application of the total error approach, was utilized to assess the amount of TeNT present in the cell culture medium of two TeNT production batches during different steps in the vaccine production process prior to the generation of the toxoid. The amount of TeNT generated under different physical stress conditions applied during bacterial culture was also monitored.
Fractional Optimal Control Model of SARS-CoV-2 (COVID-19) Disease in Ghana
Authors: Samuel Okyere, Joseph Ackora-Prah, Kwaku Forkuoh Darkwah, Francis Tabi Oduro, Ebenezer Bonyah
Date: 2022-01-18 00:00:00

Research focus on optimal control problems brought on by fractional differential equations has been extensively applied in practice. However, because they are still open-ended and challenging, a number of problems with fractional mathematical modeling and problems with optimal control require additional study. Using fractional-order derivatives defined in the Atangana Baleanu Caputo sense, we alter the integer-order model that has been proposed in the literature. We prove the solution's existence, uniqueness, equilibrium points, fundamental reproduction number, and local stability of the equilibrium points. The operator's numerical approach was put into practice to obtain a numerical simulation to back up the analytical conclusions. Fractional optimum controls were incorporated into the model to identify the most efficient intervention strategies for controlling the disease. Utilizing actual data from Ghana for the months of March 2020 to March 2021, the model is validated. The simulation's results show that the fractional operator significantly affected each compartment and that the incidence rate of the population rose when v>0.6. The examination of the most effective control technique discovered that social exclusion and vaccination were both very effective methods for halting the development of the illness.
Overt and covert paths for sound in the auditory system of mammals
Authors: Bernard M. Auriol, Jérôme Béard, Jean-Marc Broto, Didier F. Descouens, Lise J. S. Durand, Frederick Garcia, Christian F. Gillieaux, Elizabeth G. Joiner, Bernard Libes, Robert Ruiz, Claire Thalamas
Date: 2013-10-27 00:00:00

The consensus, according to which the transmission of sound from the tympanum to the Outer Hair Cells is solely mechanical, is problematic, especially with respect to high pitched sounds. We demonstrate that the collagenous fibers of the tympanum produce electric potentials synchronous to acoustic vibrations and that, contrary to expectations, their amplitude increases as the frequency of the vibration increases. These electrical potentials cannot be reduced to the cochlear microphonic. Moreover, the alteration of collagen as well as that of the gap junctions (electric synapses) necessary for the transmission of the electric potentials to the complex formed by the Deiters Cells and Outer Hair Cells, results in hypoacousis or deafness. The discovery of an electronic pathway, complementary to air and bone conduction has the potential for elucidating certain important as yet unexplained aspects of hearing with respect to cochlear amplification, otoacoustic emissions, and hypoacusis related to the deterioration of collagen or of gap-junctions. Thus, our findings have important implications for both theory and practice.