pwc-archive/papers-with-abstracts huggingface dataset:
The elastic origins of tail asymmetry
Authors: Satoshi Nakano, Kazuhiko Nishimura
Date: 2021-10-16 00:00:00
Tasks: Time Series, Time Series Analysis

Based on a multisector general equilibrium framework, we show that the sectoral elasticity of substitution plays the key role in the evolution of asymmetric tails of macroeconomic fluctuations and the establishment of robustness against productivity shocks. Non-unitary elasticity of substitution renders a nonlinear Domar aggregation, where normal sectoral productivity shocks translate into non-normal aggregated shocks with variable expected output growth. We empirically estimate 100 sectoral elasticities of substitution, using the time-series linked input-output tables for Japan, and find that the production economy is elastic overall, relative to Cobb-Douglas with unitary elasticity. Along with the previous assessment of an inelastic production economy for the US, the contrasting tail asymmetry of the distribution of aggregated shocks between the US and Japan is explained. Moreover, robustness of an economy is assessed by the expected output growth, the level of which is led by the sectoral elasticities of substitution, under zero mean productivity shocks.
Rise of the Kniesians: The professor-student network of Nobel laureates in economics
Authors: Richard S. J. Tol
Date: 2020-11-26 00:00:00

The paper presents the professor-student network of Nobel laureates in economics. 74 of the 79 Nobelists belong to one family tree. The remaining 5 belong to 3 separate trees. There are 350 men in the graph, and 4 women. Karl Knies is the central-most professor, followed by Wassily Leontief. No classical and few neo-classical economists have left notable descendants. Harvard is the central-most university, followed by Chicago and Berlin. Most candidates for the Nobel prize belong to the main family tree, but new trees may arise for the students of Terence Gorman and Denis Sargan.
Identifying the Effects of Sanctions on the Iranian Economy using Newspaper Coverage
Authors: Dario Laudati, M. Hashem Pesaran
Date: 2021-10-04 00:00:00

This paper considers how sanctions affected the Iranian economy using a novel measure of sanctions intensity based on daily newspaper coverage. It finds sanctions to have significant effects on exchange rates, inflation, and output growth, with the Iranian rial over-reacting to sanctions, followed up with a rise in inflation and a fall in output. In absence of sanctions, Iran's average annual growth could have been around 4-5 per cent, as compared to the 3 per cent realized. Sanctions are also found to have adverse effects on employment, labor force participation, secondary and high-school education, with such effects amplified for females.
Auction design with ambiguity: Optimality of the first-price and all-pay auctions
Authors: Sosung Baik, Sung-Ha Hwang
Date: 2021-10-16 00:00:00
Tasks: All

We study the optimal auction design problem when bidders' preferences follow the maxmin expected utility model. We suppose that each bidder's set of priors consists of beliefs close to the seller's belief, where "closeness" is defined by a divergence. For a given allocation rule, we identify a class of optimal transfer candidates, named the win-lose dependent transfers, with the following property: each type of bidder's transfer conditional on winning or losing is independent of the competitor's type report. Our result reduces the infinite-dimensional optimal transfer problem to a two-dimensional optimization problem. By solving the reduced problem, we find that: (i) among efficient mechanisms with no premiums for losers, the first-price auction is optimal; and, (ii) among efficient winner-favored mechanisms where each bidder pays smaller amounts when she wins than loses: the all-pay auction is optimal. Under a simplifying assumption, these two auctions remain optimal under the endogenous allocation rule.
MRI Recovery with A Self-calibrated Denoiser
Authors: Sizhuo Liu, Philip Schniter, Rizwan Ahmad
Date: 2021-10-18 00:00:00
Tasks: compressed sensing, Denoising, MRI Reconstruction

Plug-and-play (PnP) methods that employ application-specific denoisers have been proposed to solve inverse problems, including MRI reconstruction. However, training application-specific denoisers is not feasible for many applications due to the lack of training data. In this work, we propose a PnP-inspired recovery method that does not require data beyond the single, incomplete set of measurements. The proposed method, called recovery with a self-calibrated denoiser (ReSiDe), trains the denoiser from the patches of the image being recovered. The denoiser training and a call to the denoising subroutine are performed in each iteration of a PnP algorithm, leading to a progressive refinement of the reconstructed image. For validation, we compare ReSiDe with a compressed sensing-based method and a PnP method with BM3D denoising using single-coil MRI brain data.
Synthetic Aperture Radar Image Change Detection via Siamese Adaptive Fusion Network
Authors: Yunhao Gao, Feng Gao, Junyu Dong, Qian Du, Heng-Chao Li
Date: 2021-10-18 00:00:00
Tasks: Change Detection

Synthetic aperture radar (SAR) image change detection is a critical yet challenging task in the field of remote sensing image analysis. The task is non-trivial due to the following challenges: Firstly, intrinsic speckle noise of SAR images inevitably degrades the neural network because of error gradient accumulation. Furthermore, the correlation among various levels or scales of feature maps is difficult to be achieved through summation or concatenation. Toward this end, we proposed a siamese adaptive fusion network for SAR image change detection. To be more specific, two-branch CNN is utilized to extract high-level semantic features of multitemporal SAR images. Besides, an adaptive fusion module is designed to adaptively combine multiscale responses in convolutional layers. Therefore, the complementary information is exploited, and feature learning in change detection is further improved. Moreover, a correlation layer is designed to further explore the correlation between multitemporal images. Thereafter, robust feature representation is utilized for classification through a fully-connected layer with softmax. Experimental results on four real SAR datasets demonstrate that the proposed method exhibits superior performance against several state-of-the-art methods. Our codes are available at https://github.com/summitgao/SAR_CD_SAFNet.
The General sampling theorem, Compressed sensing and a method of image sampling and reconstruction with sampling rates close to the theoretical limit
Authors: L. Yaroslavsky
Date: 2021-10-17 00:00:00
Tasks: compressed sensing

The article addresses the problem of image sampling with minimal possible sampling rates and reviews the recent advances in sampling theory and methods: modern formulations of the sampling theorems, potentials and limitations of Compressed sensing methods and a practical method of image sampling and reconstruction with sampling rates close to the theoretical minimum.
Adaptive Time-Channel Beamforming for Time-of-Flight Correction
Authors: Avner Shultzman, Oded Drori, Yonina C. Eldar
Date: 2021-10-17 00:00:00

Adaptive beamforming can lead to substantial improvement in resolution and contrast of ultrasound images over standard delay and sum beamforming. Here we introduce the adaptive time-channel (ATC) beamformer, a data-driven approach that combines spatial and temporal information simultaneously, thus generalizing minimum variance beamformers. Moreover, we broaden the concept of apodization to the temporal dimension. Our approach reduces noises by allowing for the weights to adapt in both the temporal and spatial dimensions, thereby reducing artifacts caused by the media's inhomogeneities. We apply our method to in-silico data and show 12% resolution enhancement along with 2-fold contrast improvement, and significant noise reduction with respect to delay and sum and minimum variance beamformers.
Self-Learned Kernel Low Rank Approach TO Accelerated High Resolution 3D Diffusion MRI
Authors: Abhijit Baul, Nian Wang, Choyi Zhang, Leslie Ying, Yuchou Chang, Ukash Nakarmi
Date: 2021-10-16 00:00:00
Tasks: compressed sensing, Diffusion  MRI

Diffusion Magnetic Resonance Imaging (dMRI) is a promising method to analyze the subtle changes in the tissue structure. However, the lengthy acquisition time is a major limitation in the clinical application of dMRI. Different image acquisition techniques such as parallel imaging, compressed sensing, has shortened the prolonged acquisition time but creating high-resolution 3D dMRI slices still requires a significant amount of time. In this study, we have shown that high-resolution 3D dMRI can be reconstructed from the highly undersampled k-space and q-space data using a Kernel LowRank method. Our proposed method has outperformed the conventional CS methods in terms of both image quality and diffusion maps constructed from the diffusion-weighted images
Vit-GAN: Image-to-image Translation with Vision Transformes and Conditional GANS
Authors: Yiğit Gündüç
Date: 2021-10-11 00:00:00
Tasks: Image Segmentation, Image-to-Image Translation, Semantic Segmentation, Translation

In this paper, we have developed a general-purpose architecture, Vit-Gan, capable of performing most of the image-to-image translation tasks from semantic image segmentation to single image depth perception. This paper is a follow-up paper, an extension of generator-based model [1] in which the obtained results were very promising. This opened the possibility of further improvements with adversarial architecture. We used a unique vision transformers-based generator architecture and Conditional GANs(cGANs) with a Markovian Discriminator (PatchGAN) (https://github.com/YigitGunduc/vit-gan). In the present work, we use images as conditioning arguments. It is observed that the obtained results are more realistic than the commonly used architectures.
Cyclic Prefix (CP) Jamming Against Eavesdropping Relays in OFDM Systems
Authors: Muhammad Sohaib J. Solaija, Haji M. Furqan, Zekeriyya Esat Ankaralı, Hüseyin Arslan
Date: 2021-10-18 00:00:00
Tasks: Diversity

Cooperative communication has been widely used to provide spatial diversity benefits for low-end user equipments, especially in ad hoc and wireless sensor networks. However, the lack of strong authentication mechanisms in these networks leaves them prone to eavesdropping relays. In this paper, we propose a secure orthogonal frequency division multiplexing (OFDM) transmission scheme, where the destination node transmits a jamming signal over the cyclic prefix (CP) duration of the received signal. Simulation results verify that as long as at least a part of the jamming signal falls to the actual data portion of the eavesdropping relay, it spreads through all the data symbols due to the fast Fourier transformation (FFT) operation, resulting in degraded interception at the eavesdropper.
AoA Estimation for OAM Communication Systems With Mode-Frequency Multi-Time ESPRIT Method
Authors: Wen-Xuan Long, Rui Chen, Marco Moretti, Jiandong Li
Date: 2021-10-18 00:00:00

Radio orbital angular momentum (OAM) communications require accurate alignment between the transmit and receive beam directions. Accordingly, a key feature of OAM receivers is the ability to reliably estimate the angle of arrival (AoA) of multi-mode OAM beams. Considering the limitations of existing AoA estimation techniques, in this paper, we propose an easier-to-implement AoA estimation method based on applying multiple times the estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm to the received training signals in OAM mode and frequency domains, which is denoted as the mode-frequency (M-F) multi-time (MT)-ESPRIT algorithm. With this method, the misalignment error of real OAM channels can be greatly reduced and the performance approaches that of ideally aligned OAM channels.
eVTOL Communications and Networking in UAM: Requirements, Key Enablers, and Challenges
Authors: Abdullah Abu Zaid, Baha Eddine Youcef Belmekki, Mohamed-Slim Alouini
Date: 2021-10-17 00:00:00
Tasks: Autonomous Driving

Electric vertical takeoff and landing (eVTOL) aircraft have attracted great attention during the last years as the long-awaited enabler of urban air mobility (UAM), allowing a more sustainable method of transportation and accelerating the development of smart cities. The operation of eVTOLs over urban areas introduces safety hazards for passengers, pedestrians, and buildings, which prioritises safety considerations. Ensuring the safe operation of eVTOLs requires studying their communication, networking, computing requirements. In this paper, we showcase eVTOL requirements in UAM in terms of coverage, data rate, latency, spectrum efficiency, networking, and computing. Then, we identify potential key technological enablers to address these requirements and their challenges. Finally, we carry out a comparative case study between the terrestrial and aerial communication infrastructures to serve eVTOLs in UAM.
Robust Kalman filters with unknown covariance of multiplicative noise
Authors: Xingkai Yu, Ziyang Meng
Date: 2021-10-17 00:00:00
Tasks: Robust Design

In this paper, state and noise covariance estimation problems for linear system with unknown multiplicative noise are considered. The measurement likelihood is modelled as a mixture of two Gaussian distributions and a Student's t distribution, respectively. The unknown covariance of multiplicative noise is modelled as an inverse Gamma/Wishart distribution and the initial condition is formulated as the nominal covariance. By using robust design and choosing hierarchical priors, two variational Bayesian based robust Kalman filters are proposed. Stability and covergence of the proposed filters, the covariance parameters, the VB inference, and the estimation error dynamics are analyzed. The lower and upper bounds are also provided to guarantee the performance of the proposed filters. A target tracking simulation is provided to validate the effectiveness of the proposed filters.
Numerical Overcurrent Relay: A Digitizing Element Testing Automation and Simulation Based on Wavelet Transform
Authors: Emad Awada
Date: 2021-10-16 00:00:00
Tasks: Denoising

In todays modern technology and fast growth in electrical power supply, the need for precise protection relays has become a major issue in the power distribution system. That is, to ensure a safe operation at all levels of generation, distribution, and consumer load, the power relay should perform at the highest level of accuracy. Therefore, this work will investigate the enhancement of the Overcurrent Numerical Relay in terms of the quality of digitized incoming analog current waveform prior to numerical relay decision. However, as it is an expensive process of testing the digitizing element of numerical overcurrent relay, Analog to Digital Converter, this newly proposed method of combing test automation by LabView and advanced waveform analysis algorithms by Wavelet transform will be applied to enhance the testing process, reduce data compiling complexity, and decrease cost. In addition, the testing simulation will be presented using Matlab software to verify the testing result. As a result, with a bank of filters, denoising, and decomposition structures, Wavelet transform can provide promising results in testing and verifying the accuracy of numerical relay digitizing process along with automation process to increase the possibility of improving numerical relay digitizer testing reliability with lower cost.
How can a Cognitive Radar Mask its Cognition?
Authors: Kunal Pattanayak, Vikram Krishnamurthy, Christopher Berry
Date: 2021-10-16 00:00:00

We study how a cognitive radar can mask (hide) its cognitive ability from an adversarial jamming device. Specifically, if the radar optimally adapts its waveform based on adversarial target maneuvers (probes), how should the radar choose its waveform parameters (response) so that its utility function cannot be recovered by the adversary. This paper abstracts the radar's cognition masking problem in terms of the spectra (eigenvalues) of the state and observation noise covariance matrices, and embeds the algebraic Riccati equation into an economics-based utility maximization setup. Given an observed sequence of radar responses, the adversary tests for utility maximization behavior of the radar and estimates its utility function that rationalizes the radar's responses. In turn, the radar deliberately chooses sub-optimal responses so that its utility function almost fails the utility maximization test, and hence, its cognitive ability is masked from the adversary. We illustrate the performance of our cognition masking scheme via simple numerical examples. Our approach in this paper is based on revealed preference theory in microeconomics for identifying rationality.
PDMM: A novel Primal-Dual Majorization-Minimization algorithm for Poisson Phase-Retrieval problem
Authors: Ghania Fatima, Zongyu Li, Aakash Arora, Prabhu Babu
Date: 2021-10-16 00:00:00
Tasks: Retrieval

In this paper, we introduce a novel iterative algorithm for the problem of phase-retrieval where the measurements consist of only the magnitude of linear function of the unknown signal, and the noise in the measurements follow Poisson distribution. The proposed algorithm is based on the principle of majorization-minimization (MM); however, the application of MM here is very novel and distinct from the way MM has been usually used to solve optimization problems in the literature. More precisely, we reformulate the original minimization problem into a saddle point problem by invoking Fenchel dual representation of the log (.) term in the Poisson likelihood function. We then propose tighter surrogate functions over both primal and dual variables resulting in a double-loop MM algorithm, which we have named as Primal-Dual Majorization-Minimization (PDMM) algorithm. The iterative steps of the resulting algorithm are simple to implement and involve only computing matrix vector products. We also extend our algorithm to handle various L1 regularized Poisson phase-retrieval problems (which exploit sparsity). The proposed algorithm is compared with previously proposed algorithms such as wirtinger flow (WF), MM (conventional), and alternating direction methods of multipliers (ADMM) for the Poisson data model. The simulation results under different experimental settings show that PDMM is faster than the competing methods, and its performance in recovering the original signal is at par with the state-of-the-art algorithms.
A Tutorial on Terahertz-Band Localization for 6G Communication Systems
Authors: Hui Chen, Hadi Sarieddeen, Tarig Ballal, Henk Wymeersch, Mohamed-Slim Alouini, Tareq Y. Al-Naffouri
Date: 2021-10-16 00:00:00

Terahertz (THz) communications are celebrated as key enablers for converged localization and sensing in future sixth-generation (6G) wireless communication systems and beyond. Instead of being a byproduct of the communication system, localization in 6G is indispensable for location-aware communications. Towards this end, we aim to identify the prospects, challenges, and requirements of THz localization techniques. We first review the history and trends of localization methods and discuss their objectives, constraints, and applications in contemporary communication systems. We then detail the latest advances in THz communications and introduce the THz-specific channel and system models. Afterward, we formulate THz-band localization as a 3D position/orientation estimation problem, detailing geometry-based localization techniques and describing potential THz localization and sensing extensions. We further formulate the offline design and online optimization of THz localization systems, provide numerical simulation results, and conclude by providing lessons learned and future research directions. Preliminary results illustrate that under the same transmission power and array footprint, THz-based localization outperforms millimeter wave-based localization. In other words, the same level of localization performance can be achieved at THz-band with less transmission power or a smaller footprint.
Rethinking Modern Communication from Semantic Coding to Semantic Communication
Authors: Kun Lu, Qingyang Zhou, Rongpeng Li, Zhifeng Zhao, Xianfu Chen, Jianjun Wu, Honggang Zhang
Date: 2021-10-16 00:00:00
Tasks: Reinforcement Learning (RL), Semantic Communication

Modern communications are usually designed to pursue a higher bit-level precision and fewer bits while transmitting a message. This article rethinks these two major features and introduces the concept and advantage of semantics that characterizes a new kind of semantics-aware communication framework, incorporating both the semantic encoding and the semantic communication problem. After analyzing the underlying defects of existing semantics-aware techniques, we establish a confidence-based distillation mechanism for the joint semantics-noise coding (JSNC) problem and a reinforcement learning (RL)-powered semantic communication paradigm that endows a system the ability to convey the semantics instead of pursuing the bit level accuracy. On top of these technical contributions, this work provides a new insight to understand how the semantics are processed and represented in a semantics-aware coding and communication system, and verifies the significant benefits of doing so. Targeted on the next generation's semantics-aware communication, some critical concerns and open challenges such as the information overhead, semantic security and implementation cost are also discussed and envisioned.
Robust Adaptive Beamforming Maximizing the Worst-Case SINR over Distributional Uncertainty Sets for Random INC Matrix and Signal Steering Vector
Authors: Yongwei Huang, Wenzheng Yang, Sergiy A. Vorobyov
Date: 2021-10-16 00:00:00

The robust adaptive beamforming (RAB) problem is considered via the worst-case signal-to-interference-plus-noise ratio (SINR) maximization over distributional uncertainty sets for the random interference-plus-noise covariance (INC) matrix and desired signal steering vector. The distributional uncertainty set of the INC matrix accounts for the support and the positive semidefinite (PSD) mean of the distribution, and a similarity constraint on the mean. The distributional uncertainty set for the steering vector consists of the constraints on the known first- and second-order moments. The RAB problem is formulated as a minimization of the worst-case expected value of the SINR denominator achieved by any distribution, subject to the expected value of the numerator being greater than or equal to one for each distribution. Resorting to the strong duality of linear conic programming, such a RAB problem is rewritten as a quadratic matrix inequality problem. It is then tackled by iteratively solving a sequence of linear matrix inequality relaxation problems with the penalty term on the rank-one PSD matrix constraint. To validate the results, simulation examples are presented, and they demonstrate the improved performance of the proposed robust beamformer in terms of the array output SINR.