pwc-archive/papers-with-abstracts huggingface dataset:
A 21.3%-Efficiency Clipped-Sinusoid UWB Impulse Radio Transmitter with Simultaneous Inductive Powering and Data Receiving
Authors: Nima Soltani, Hamed M. Jafari, Karim Abdelhalim, Hossein Kassiri, Xilin Liu, Roman Genov
Date: 2022-12-03 00:00:00

An ultra-wide-band impulse-radio (UWB-IR) transmitter (TX) for low-energy biomedical microsystems is presented. High power efficiency is achieved by modulating an LC tank that always resonates in the steady state during transmission. A new clipped-sinusoid scheme is proposed for on-off keying (OOK)-modulation, which is implemented by a voltage clipper circuit with on-chip biasing generation. The TX is designed to provide a high data-rate wireless link within the 3-5 GHz band. The chip was fabricated in 130nm CMOS technology and fully characterized. State-of-the-art power efficiency of 21.3% was achieved at a data-rate of 230Mbps and energy consumption of 21pJ/b. A bit-error-rate (BER) of less than 10^-6 was measured at a distance of 1m without pulse averaging. In addition, simultaneous wireless powering and VCO-based data transmission are supported. A potential extension to a VCO-free all-wireless mode to further reduce the power consumption is also discussed.
Eigenvalue spectral properties of sparse random matrices obeying Dale's law
Authors: Isabelle D Harris, Hamish Meffin, Anthony N Burkitt, Andre D. H Peterson
Date: 2022-12-03 00:00:00

This paper examines the relationship between sparse random network architectures and neural network stability by examining the eigenvalue spectral distribution. Specifically, we generalise classical eigenspectral results to sparse connectivity matrices obeying Dale's law: neurons function as either excitatory (E) or inhibitory (I). By defining sparsity as the probability that a neutron is connected to another neutron, we give explicit formulae that shows how sparsity interacts with the E/I population statistics to scale key features of the eigenspectrum, in both the balanced and unbalanced cases. Our results show that the eigenspectral outlier is linearly scaled by sparsity, but the eigenspectral radius and density now depend on a nonlinear interaction between sparsity, the E/I population means and variances. Contrary to previous results, we demonstrate that a non-uniform eigenspectral density results if any of the E/I population statistics differ, not just the E/I population variances. We also find that 'local' eigenvalue-outliers are present for sparse random matrices obeying Dale's law, and demonstrate that these eigenvalues can be controlled by a modified zero row-sum constraint for the balanced case, however, they persist in the unbalanced case. We examine all levels of connection (sparsity), and distributed E/I population weights, to describe a general class of sparse connectivity structures which unifies all the previous results as special cases of our framework. Sparsity and Dale's law are both fundamental anatomical properties of biological neural networks. We generalise their combined effects on the eigenspectrum of random neural networks, thereby gaining insight into network stability, state transitions and the structure-function relationship.
Educational Inequality of Opportunity and Mobility in Europe
Authors: Joël Terschuur
Date: 2022-12-05 00:00:00

Educational attainment generates labor market returns, societal gains and has intrinsic value for individuals. We study Inequality of Opportunity (IOp) and intergenerational mobility in the distribution of educational attainment. We propose to use debiased IOp estimators based on the Gini coefficient and the Mean Logarithmic Deviation (MLD) which are robust to machine learning biases. We also measure the effect of each circumstance on IOp, we provide tests to compare IOp in two populations and to test joint significance of a group of circumstances. We find that circumstances explain between 38\% and 74\% of total educational inequality in European countries. Mother's education is the most important circumstance in most countries. There is high intergenerational persistence and there is evidence of an educational Great Gatsby curve. We also construct IOp aware educational Great Gatsby curves and find that high income IOp countries are also high educational IOp and less mobile countries.
A Data Fusion Approach for Ride-sourcing Demand Estimation: A Discrete Choice Model with Sampling and Endogeneity Corrections
Authors: Rico Krueger, Michel Bierlaire, Prateek Bansal
Date: 2022-12-05 00:00:00
Tasks: Discrete Choice Models

Ride-sourcing services offered by companies like Uber and Didi have grown rapidly in the last decade. Understanding the demand for these services is essential for planning and managing modern transportation systems. Existing studies develop statistical models for ride-sourcing demand estimation at an aggregate level due to limited data availability. These models lack foundations in microeconomic theory, ignore competition of ride-sourcing with other travel modes, and cannot be seamlessly integrated into existing individual-level (disaggregate) activity-based models to evaluate system-level impacts of ride-sourcing services. In this paper, we present and apply an approach for estimating ride-sourcing demand at a disaggregate level using discrete choice models and multiple data sources. We first construct a sample of trip-based mode choices in Chicago, USA by enriching household travel survey with publicly available ride-sourcing and taxi trip records. We then formulate a multivariate extreme value-based discrete choice with sampling and endogeneity corrections to account for the construction of the estimation sample from multiple data sources and endogeneity biases arising from supply-side constraints and surge pricing mechanisms in ride-sourcing systems. Our analysis of the constructed dataset reveals insights into the influence of various socio-economic, land use and built environment features on ride-sourcing demand. We also derive elasticities of ride-sourcing demand relative to travel cost and time. Finally, we illustrate how the developed model can be employed to quantify the welfare implications of ride-sourcing policies and regulations such as terminating certain types of services and introducing ride-sourcing taxes.
A comprehensive study of cotton price fluctuations using multiple Econometric and LSTM neural network models
Authors: Morteza Tahami Pour Zarandi, Mehdi Ghasemi Meymandi, Mohammad Hemami
Date: 2022-12-03 00:00:00
Tasks: Econometrics

This paper proposes a new coherent model for a comprehensive study of the cotton price using econometrics and Long-Short term memory neural network (LSTM) methodologies. We call a simple cotton price trend and then assumed conjectures in structural method (ARMA), Markov switching dynamic regression, simultaneous equation system, GARCH families procedures, and Artificial Neural Networks that determine the characteristics of cotton price trend duration 1990-2020. It is established that in the structural method, the best procedure is AR (2) by Markov switching estimation. Based on the MS-AR procedure, it concludes that tending to regime change from decreasing trend to an increasing one is more significant than a reverse mode. The simultaneous equation system investigates three procedures based on the acreage cotton, value-added, and real cotton price. Finally, prediction with the GARCH families TARCH procedure is the best-fitting model, and in the LSTM neural network, the results show an accurate prediction by the training-testing method.
Compacter networks as a defensive mechanism: How firms clustered during 2015 Financial Crisis in China
Authors: Yujue Wang
Date: 2022-12-03 00:00:00
Tasks: Clustering, Data Visualization

The stock market's reaction to the external risk shock is closely related to the cross-shareholding network structure. This paper takes the public information of listed companies in the A-share securities market as the primary sample to study the relationship between the stock return rate, market performance, and network topology before and after China's stock market crash in 2015. Data visualization and empirical analysis demonstrate that the return rate of stocks is related to the company's traditional business ability and the social capital brought by cross-holding. Several heteroscedasticity tests and endogeneity tests with IV are conducted to support the robustness. The structure of the cross-shareholding network experienced upheaval after the shock, even distorting the effects of market value, and assets holding on the return rate. The enterprises in the entire shareholding network are connected more firmly to overcome systematic external risks. The number of enterprise clusters is significantly reduced during the process. Besides, the number of newly established cross-shareholding relationships shows an outbreak, which may explain the rapid maintenance of stability in the financial system. When stable clustering is formed before and after a stock crash (rather than when it occurs), the clustering coefficient of clear clustering still has an apparent positive influence on the return rate of stocks. To sum up, the compacted network may prevent the firms from pursuing aggressive earning before the financial crisis, but would protect firms from suffering relatively high losses during and after the shock.
Short-term shock, long-lasting payment: Evidence from the Lushan Earthquake
Authors: Yujue Wang
Date: 2022-12-03 00:00:00
Tasks: Causal Inference

Abrupt catastrophic events bring business risks into firms. The paper introduces the Great Lushan Earthquake in 2013 in China as an unexpected shock to explore the causal effects on public firms in both the long and short term. DID-PSM methods are conducted to examine the robustness of causal inference. The identifications and estimations indicate that catastrophic shock significantly negatively impacts cash flow liquidity and profitability in the short term. Besides, the practical influences on firms' manufacturing and operation emerge in the treated group. Firms increase non-business expenditures and retained earnings as a financial measure to resist series risk during the shock period. As the long-term payment, the decline in production factors, particularly in employment level and the loss in fixed assets, are permanent. The earthquake's comprehensive interactions are also reflected. The recovery from the disaster would benefit the companies by raising the growth rate of R\&D and enhancing competitiveness through increasing market share, though these effects are temporary. PSM-DID and event study methods are implemented to investigate the general effects of specific strong earthquakes on local public firms nationwide. Consistent with the Lushan Earthquake, the ratio of cash flow to sales dropped drastically and recovered in 3 subsequent semesters. The shock on sales was transitory, only in the current semester.
Digital leisure and the gig economy: a two-sector model of growth
Authors: Francesco Angelini, Luca V. Ballestra, Massimiliano Castellani
Date: 2022-12-05 00:00:00

The process of market digitization at the world level and the increasing and extended usage of digital devices reshaped the way consumers employ their leisure time, with the emergence of what can be called digital leisure. This new type of leisure produces data that firms can use, with no explicit cost paid by consumers. At the same time, the global digitalization process has allowed workers to allocate part of (or their whole) working time to the Gig Economy sector, which strongly relies on data as a production factor. In this paper, we develop a two-sector growth model to study how the above mechanism can shape the dynamics of growth, also assessing how shocks in either the traditional or the Gig Economy sector can modify the equilibrium of the overall economy. We find that shocks in the TFP can crowd out working time from a sector to the other, while shocks on the elasticity of production to data determines a change in the time allocated to digital leisure.
Interferometric Passive Radar Imaging with Deep Denoising Priors
Authors: Samia Kazemi, Bariscan Yonel, Birsen Yazıcı
Date: 2022-12-04 00:00:00
Tasks: Denoising

Passive radar has key advantages over its active counterpart in terms of cost and stealth. In this paper, we address passive radar imaging problem by interferometric inversion using a spectral estimation method with a priori information within a deep learning (DL) framework. Cross-correlating the received signals from different look directions mitigates the influence of shared transmitter related phase components despite lack of a cooperative transmitter, and permits tractable inference via interferometric inversion. We thereon leverage deep architectures for modeling a priori information and for improving sample efficiency of state-of-the-art methods. Our approach comprises of an iterative algorithm based on generalizing the power method, and applies denoisers using plug-and-play (PnP) and regularization by denoising (RED) techniques. We evaluate our approach using simulated data for passive synthetic aperture radar (SAR) by using convolutional neural networks (CNN) as denoisers. The numerical experiments show that our method can achieve faster reconstruction and superior image quality in sample starved regimes than the state-of-the-art passive interferometric imaging algorithms.
CloudBrain-ReconAI: An Online Platform for MRI Reconstruction and Image Quality Evaluation
Authors: Yirong Zhou, Chen Qian, Jiayu Li, Zi Wang, Yu Hu, Biao Qu, Liuhong Zhu, Jianjun Zhou, Taishan Kang, Jianzhong Lin, Qing Hong, Jiyang Dong, Di Guo, Xiaobo Qu
Date: 2022-12-04 00:00:00
Tasks: Cloud Computing, compressed sensing, Image Reconstruction, MRI Reconstruction

Efficient collaboration between engineers and radiologists is important for image reconstruction algorithm development and image quality evaluation in magnetic resonance imaging (MRI). Here, we develop CloudBrain-ReconAI, an online cloud computing platform, for algorithm deployment, fast and blind reader study. This platform supports online image reconstruction using state-of-the-art artificial intelligence and compressed sensing algorithms with applications to fast imaging and high-resolution diffusion imaging. Through visiting the website, radiologists can easily score and mark the images. Then, automatic statistical analysis will be provided. CloudBrain-ReconAI is now open accessed at https://csrc.xmu.edu.cn/CloudBrain.html and will be continually improved to serve the MRI research community.
Signal enhancement for two-dimensional cryo-EM data processing
Authors: Guy Sharon, Yoel Shkolnisky, Tamir Bendory
Date: 2022-12-02 00:00:00
Tasks: Dimensionality Reduction, Symmetry Detection, Vocal Bursts Valence Prediction

Different tasks in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM) require enhancing the quality of the highly noisy raw images. To this end, we develop an efficient algorithm for signal enhancement of cryo-EM images. The enhanced images can be used for a variety of downstream tasks, such as 2-D classification, removing uninformative images, constructing {ab initio} models, generating templates for particle picking, providing a quick assessment of the data set, dimensionality reduction, and symmetry detection. The algorithm includes built-in quality measures to assess its performance and alleviate the risk of model bias. We demonstrate the effectiveness of the proposed algorithm on several experimental data sets. In particular, we show that the quality of the resulting images is high enough to produce ab initio models of $\sim 10$ \AA resolution. The algorithm is accompanied by a publicly available, documented and easy-to-use code.
Geometric Constellation Shaping with Low-complexity Demappers for Wiener Phase-noise Channels
Authors: Andrej Rode, Laurent Schmalen
Date: 2022-12-05 00:00:00

We show that separating the in-phase and quadrature component in optimized, machine-learning based demappers of optical communications systems with geometric constellation shaping reduces the required computational complexity whilst retaining their good performance.
Estimating Multi-Modal Dense Multipath Components using Auto-Encoders
Authors: Steffen Schieler, Michael Döbereiner, Sebastian Semper, Markus Landmann
Date: 2022-12-05 00:00:00

We present a maximum-likelihood estimation algorithm for radio channel measurements exhibiting a mixture of independent Dense Multipath Components. The novelty of our approach is in the algorithms initialization using a deep learning architecture. Currently, available approaches can only deal with scenarios where a single mode is present. However, in measurements, two or more modes are often observed. This much more challenging multi-modal setting bears two important questions: How many modes are there, and how can we estimate those? To this end, we propose a Neural Net-architecture that can reliably estimate the number of modes present in the data and also provide an initial assessment of their shape. These predictions are used to initialize for gradient- and model-based optimization algorithm to further refine the estimates. We demonstrate numerically how the presented architecture performs on measurement data and analytically study its influence on the estimation of specular paths in a setting where the single-modal approach fails.
Complexity-based Financial Stress Evaluation
Authors: Hongjian Xiao, Yao Lei Xu, Danilo P. Mandic
Date: 2022-12-05 00:00:00
Tasks: Time Series, Time Series Analysis

Financial markets typically exhibit dynamically complex properties as they undergo continuous interactions with economic and environmental factors. The Efficient Market Hypothesis indicates a rich difference in the structural complexity of security prices between normal (stable markets) and abnormal (financial crises) situations. Considering the analogy between market undulation of price time series and physical stress of bio-signals, we investigate whether stress indices in bio-systems can be adopted and modified so as to measure 'standard stress' in financial markets. This is achieved by employing structural complexity analysis, based on variants of univariate and multivariate sample entropy, to estimate the stress level of both financial markets on the whole and the performance of the individual financial indices. Further, we propose a novel graphical framework to establish the sensitivity of individual assets and stock markets to financial crises. This is achieved through Catastrophe Theory and entropy-based stress evaluations indicating the unique performance of each index/individual stock in response to different crises. Four major indices and four individual equities with gold prices are considered over the past 32 years from 1991-2021. Our findings based on nonlinear analyses and the proposed framework support the Efficient Market Hypothesis and reveal the relations among economic indices and within each price time series.
Three High-rate Beamforming Methods for Active IRS-aided Wireless Network
Authors: Feng Shu, Jing Liu, Yeqing Lin, Yang Liu, Zhilin Chen, Xuehui Wang, Rongen Dong, Jiangzhou Wang
Date: 2022-12-05 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Due to its ability of breaking the double-fading effect experienced by passive intelligent reflecting surface (IRS), active IRS is evolving a potential technique for future 6G wireless networks. To fully exploit the amplifying gain achieved by active IRS, two high-rate methods, maximum ratio reflecting (MRR) and selective ratio reflecting (SRR) are presented, which are motivated by maximum ratio combining and selective ratio combining. Moreover, both MRR and SRR are in closed-form expressions. To further improve the rate, a maximum approximate-signal-to-noise ratio (Max-ASNR) is first proposed with an alternately iterative infrastructure between adjusting the norm of beamforming vector and its normalized vector. This may make a substantial rate enhancement over existing equal-gain reflecting (EGR). Simulation results show that the proposed three methods perform much better than existing method EGR in terms of rate. They are in decreasing order of rate performance: Max-ASNR, MRR, SRR, and EGR.
3D Localization with a Single Partially-Connected Receiving RIS: Positioning Error Analysis and Algorithmic Design
Authors: Jiguang He, Aymen Fakhreddine, Charles Vanwynsberghe, Henk Wymeersch, George C. Alexandropoulos
Date: 2022-12-05 00:00:00
Tasks: parameter estimation

In this paper, we introduce the concept of partially-connected Receiving Reconfigurable Intelligent Surfaces (R-RISs), which refers to metasurfaces designed to efficiently sense electromagnetic waveforms impinging on them, and perform localization of the users emitting them. The presented R-RIS hardware architecture comprises subarrays of meta-atoms, with each of them incorporating a waveguide assigned to direct the waveforms reaching its meta-atoms to a reception Radio-Frequency (RF) chain, enabling signal/channel parameter estimation. We particularly focus on the scenarios where the user is located in the far-field of all the R-RIS subarrays, and present a three-Dimensional (3D) localization method which is based on narrowband signaling and Angle of Arrival (AoA) estimates of the impinging signals at each single-receive-RF R-RIS subarray. For the AoA estimation, which relies on spatially sampled versions of the received signals via each subarray's phase configuration of meta-atoms, we devise an off-grid atomic norm minimization approach, which is followed by subspace-based root MUltiple SIgnal Classification (MUSIC). The AoA estimates are finally combined via a least-squared line intersection method to obtain the position coordinates of a user emitting synchronized localization pilots. Our derived theoretical Cram\'er Rao Lower Bounds (CRLBs) on the estimation parameters, which are compared with extensive computer simulation results of our localization approach, verify the effectiveness of the proposed R-RIS-empowered 3D localization system, which is showcased to offer cm-level positioning accuracy. Our comprehensive performance evaluations also demonstrate the impact of various system parameters on the localization performance, namely the training overhead and the distance between the R-RIS and the user, as well as the spacing among the R-RIS's subarrays and its partitioning patterns.