pwc-archive/papers-with-abstracts huggingface dataset:
Distributional Effects with Two-Sided Measurement Error: An Application to Intergenerational Income Mobility
Authors: Brantly Callaway, Tong Li, Irina Murtazashvili
Date: 2021-07-20 00:00:00
Tasks: quantile regression

This paper considers identification and estimation of distributional effect parameters that depend on the joint distribution of an outcome and another variable of interest ("treatment") in a setting with "two-sided" measurement error -- that is, where both variables are possibly measured with error. Examples of these parameters in the context of intergenerational income mobility include transition matrices, rank-rank correlations, and the poverty rate of children as a function of their parents' income, among others. Building on recent work on quantile regression (QR) with measurement error in the outcome (particularly, Hausman, Liu, Luo, and Palmer (2021)), we show that, given (i) two linear QR models separately for the outcome and treatment conditional on other observed covariates and (ii) assumptions about the measurement error for each variable, one can recover the joint distribution of the outcome and the treatment. Besides these conditions, our approach does not require an instrument, repeated measurements, or distributional assumptions about the measurement error. Using recent data from the 1997 National Longitudinal Study of Youth, we find that accounting for measurement error notably reduces several estimates of intergenerational mobility parameters.
A Incidência Final dos Tributos Indiretos no Brasil: Estimativa Usando a Matriz de Insumo-Produto 2015
Authors: Rozane Bezerra de Siqueira, José Ricardo Bezerra Nogueira, Carlos Feitosa Luna
Date: 2021-07-20 00:00:00

Taxes on goods and services account for about 45% of total tax revenue in Brazil. This tax collection results in a highly complex system, with several taxes, different tax bases, and a multiplicity of rates. Moreover, about 43% of taxes on goods fall on inputs. In this context, the effective tax rates can substantially differ from the legal rates. In this study we estimate the final incidence of indirect taxes in Brazil using the 2015 Brazilian input-output matrix and a method that incorporates the multisector effects of the taxation of inputs.
A mathematical definition of property rights in a Debreu economy
Authors: Abhimanyu Pallavi Sudhir
Date: 2021-07-14 00:00:00

We present mathematical definitions for rights structures, government and non-attenuation in a generalized N-person game (Debreu abstract economy), thus providing a formal basis for property rights theory.
A Novel Approach to Vehicle Pose Estimation using Automotive Radar
Authors: Martijn Heller, Nikita Petrov, Alexander Yarovoy
Date: 2021-07-20 00:00:00
Tasks: Pose Estimation, Vehicle Pose Estimation

This paper presents a set of novel scan-matching techniques for vehicle pose estimation using automotive radar measurements. The proposed approach modifies the Normal Distributions Transform (NDT) -- a state-of-the-art scan-matching SLAM technique, widely used in lidar-based localization -- to account for particular aspects of radar environment perception. First, the polar NDT (PNDT) is introduced by solving the NDT problem in the polar coordinate system, natural for radar measurements. A better agreement between the measurement uncertainties and their representation in the scan-matching algorithm is achieved. Second, the extension of PNDT to take into account the Doppler measurements -- Doppler polar NDT (DPNDT) -- is proposed. Third, the SNR of detected targets is added to the optimization procedure to minimize the impact of RCS fluctuation. The improvement over the conventional NDT is demonstrated in numerical simulations and real data processing, showing the ability to decrease the localization error by a factor of 3 to 5, depending on the scenario, with a negligible increase in computational complexity. Finally, a DPNDT extension with the capability to compensate angular bias in array beam-forming is presented. Simulation results and real data processing show the possibility to correct it with the accuracy of $0.1^\circ$ almost in real-time.
Neural-Network-based NLOS Identification in Angular Domain at 60-GHz
Authors: Pengfei Lyu, Aziz Benlarbi-Delaï, Zhuoxiang Ren, Julien Sarrazin
Date: 2021-07-20 00:00:00
Tasks: Indoor Localization

This paper introduces an identification method that determines whether a millimeter-wave wireless transmission using directional antennas is being established over a line-of-sight (LOS) or a non-line-of-sight (NLOS) cluster for indoor localization applications. The proposed technique utilizes the channel power angular spectrum that is readily available from a beam training process. In particular, the behavior of five different channel metrics, namely the spatial-domain, time-domain, and frequency-domain channel kurtosis, the mean excess delay, and the RMS delay spread, is analyzed using maximum likelihood ratio and artificial neural network. A noticeable difference between LOS and NLOS clusters is observed and assessed for identification. Hypothesis testing shows errors as low as 0.01-0.02 in simulation and 0.04-0.07 in measurements at 60 GHz.
Performance of a High Power and Capacity Mobile SLIPT Scheme
Authors: Mingliang Xiong, Qingwen Liu, Shengli Zhou, Shun Han, Mingqing Liu
Date: 2021-07-20 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The increasing demands of power supply and data rate for mobile devices promote the research of simultaneous wireless information and power transfer (SWIPT). Optical SWIPT, as known as simultaneous light information and power transfer (SLIPT), has the potential for providing high-capacity communication and high-power wireless charging. However, SLIPT technologies based on light-emitting diodes have low efficiency due to energy dissipation over the air. Laser-based SLIPT technologies need strict positioning accuracy and scanning resolution, which may lead to the increase of costs and complexity. In this paper, we propose a mobile SLIPT scheme based on spatially separated laser resonator (SSLR) and intra-cavity second harmonic generation. The power and data are transferred via separated frequencies, while they share the same self-aligned resonant beam path, without the needs of receiver positioning and beam steering. We establish the analysis model of the resonant beam power and its second harmonic power. Numerical results show that the proposed system can achieve watt-level battery charging power and above 10-bit/s/Hz achievable rate at 6-m distance, which satisfies the requirements of most indoor mobile devices.
Location-aware Channel Estimation for RIS-aided mmWave MIMO Systems via Atomic Norm Minimization
Authors: Hyeonjin Chung, Sunwoo Kim
Date: 2021-07-20 00:00:00

In this paper, we propose a location-aware channel estimation based on the atomic norm minimization (ANM) for the reconfigurable intelligent surface (RIS)-aided millimeter-wave multiple-input-multiple-output (MIMO) systems. The beam training overhead at the base station (BS) is reduced by the direct beam steering towards the RIS with the location of the BS and the RIS. The RIS beamwidth adaptation is proposed to reduce the beam training overhead at the RIS, and also it enables accurate channel estimation by ensuring the user equipment receives all the multipath components from the RIS. After the beam training, the cascaded effective channel of the RIS-aided MIMO systems is estimated by ANM. Depending on whether the beam training overhead at the BS or at the RIS is reduced or not, the channel is represented as a linear combination of either 1D atoms, 2D atoms, or 3D atoms, and the ANM is applied to estimate the channel. Simulation results show that the proposed location-aware channel estimation via 2D ANM and 3D ANM achieves superior estimation accuracy to benchmarks.
Atomic Norm Minimization-based Low-Overhead Channel Estimation for RIS-aided MIMO Systems
Authors: Hyeonjin Chung, Sunwoo Kim
Date: 2021-07-20 00:00:00

Large beam training overhead has been considered as one of main issues in the channel estimation for reconfigurable intelligent surface (RIS)-aided systems. In this paper, we propose an atomic norm minimization (ANM)-based low-overhead channel estimation for RIS-aided multiple-input-multiple-output (MIMO) systems. When the number of beam training is reduced, some multipath signals may not be received during beam training, and this causes channel estimation failure. To solve this issue, the width of beams created by RIS is widened to capture all multipath signals. Pilot signals received during beam training are compiled into one matrix to define the atomic norm of the channel for RIS-aided MIMO systems. Simulation results show that the proposed algorithm outperforms other channel estimation algorithms.
PhD Thesis on Code Modulated Interferometric Imaging System using Phased Arrays
Authors: Vikas Chauhan
Date: 2021-07-19 00:00:00
Tasks: Benchmarking

This work presents techniques which can allow low-cost phased-array receivers to be reconfigured as interferometric imagers and thereby reducing cost. Since traditional phased arrays power combine incoming signals prior to digitization, orthogonal code-modulation is applied to each incoming signal using phase shifters within each front-end. These code-modulated signals can then be combined and processed coherently through a shared hardware path. Visibility functions can be recovered through squaring and code-demultiplexing operations. The proposed system modulates incoming signals but demodulates desired correlations. Firstly, we present the operation of the system, a validation of its operation using behavioral models of a traditional phased array and a benchmarking of the code-modulated interferometer against traditional interferometer using simulation results and sensitivity analysis. Secondly, we present a simple CMI system operating in the license-free 60-GHz band using a four-element phased-array receiver. The four-element phased array is thinned to obtain a 13-pixel image and the system is demonstrated through a point-source detected at different locations. Finally, the operation and capabilities of code-modulated interferometry (CMI) are demonstrated at 10-GHz using commercially-available phased arrays. A 33-pixel, eight-element prototype is created using two commercially-available ADAR1000 phased-array receivers from Analog Devices Inc. The chips are connected at board level to a patch antenna array. The 33-pixel camera is demonstrated in hardware for point-source detection. Further to demonstrate the scalability of the concept, a 16-element, 169-pixels CMI imaging system is presented at 10-GHz using the four of the same commercially-available phased arrays from ADI. Two active point sources are imaged simultaneously to present the resolution of the system.
ProfileSR-GAN: A GAN based Super-Resolution Method for Generating High-Resolution Load Profiles
Authors: Lidong Song, Yiyan Li, Ning Lu
Date: 2021-07-18 00:00:00
Tasks: Generative Adversarial Network, Non-Intrusive Load Monitoring, Super-Resolution, Time Series, Time Series Analysis

It is a common practice for utilities to down-sample smart meter measurements from high resolution (e.g. 1-min or 1-sec) to low resolution (e.g. 15-, 30- or 60-min) to lower the data transmission and storage cost. However, down-sampling can remove high-frequency components from time-series load profiles, making them unsuitable for in-depth studies such as quasi-static power flow analysis or non-intrusive load monitoring (NILM). Thus, in this paper, we propose ProfileSR-GAN: a Generative Adversarial Network (GAN) based load profile super-resolution (LPSR) framework for restoring high-frequency components lost through the smoothing effect of the down-sampling process. The LPSR problem is formulated as a Maximum-a-Prior problem. When training the ProfileSR-GAN generator network, to make the generated profiles more realistic, we introduce two new shape-related losses in addition to conventionally used content loss: adversarial loss and feature-matching loss. Moreover, a new set of shape-based evaluation metrics are proposed to evaluate the realisticness of the generated profiles. Simulation results show that ProfileSR-GAN outperforms Mean-Square Loss based methods in all shape-based metrics. The successful application in NILM further demonstrates that ProfileSR-GAN is effective in recovering high-resolution realistic waveforms.
On the Design of Complex EM Devices and Systems through the System-by-Design Paradigm -- A Framework for Dealing with the Computational Complexity
Authors: Andrea Massa, Marco Salucci
Date: 2021-07-16 00:00:00
Tasks: CPU, global-optimization

The System-by-Design (SbD) is an emerging engineering framework for the optimization-driven design of complex electromagnetic (EM) devices and systems. More specifically, the computational complexity of the design problem at hand is addressed by means of a suitable selection and integration of functional blocks comprising problem-dependent and computationally-efficient modeling and analysis tools as well as reliable prediction and optimization strategies. Thanks to the suitable re-formulation of the problem at hand as an optimization one, the profitable minimum-size coding of the degrees-of-freedom (DoFs), the "smart" replacement of expensive full-wave (FW) simulators with proper surrogate models (SMs), which yield fast yet accurate predictions starting from minimum size/reduced CPU-costs training sets, a favorable "environment" for an optimal exploitation of the features of global optimization tools in sampling wide/complex/nonlinear solution spaces is built. This research summary is then aimed at (i) providing a comprehensive description of the SbD framework and of its pillar concepts and strategies, (ii) giving useful guidelines for its successful customization and application to different EM design problems characterized by different levels of computational complexity, (iii) envisaging future trends and advances in this fascinating and high-interest (because of its relevant and topical industrial and commercial implications) topic. Representative benchmarks concerned with the synthesis of complex EM systems are presented to highlight advantages and potentialities as well as current limitations of the SbD paradigm.
FANCA: In-Silico deleterious mutation analysis for early prediction of leukemia
Authors: Madiha Hameed, Abdul Majiid, Asifullah Khan
Date: 2021-07-19 00:00:00
Tasks: Drug Discovery

As a novel biomarker from the Fanconi anemia complementation group (FANC) family, FANCA is antigens to Leukemia cancer. The overexpression of FANCA has predicted the second most common cancer in the world that is responsible for cancer-related deaths. Non-synonymous SNPs are an essential group of SNPs that lead to alterations in encoded polypeptides. Changes in the amino acid sequences of gene products lead to Leukemia. First, we study individual SNPs in the coding region of FANCA and computational tools like PROVEAN, PolyPhen2, MuPro, and PANTHER to compute deleterious mutation scores. The three-dimensional structural and functional prediction conducted using I-TASSER. Further, the predicted structure refined using the GlaxyWeb tool. In the study, the proteomic data has been retrieved from the UniProtKB. The coding region of the dataset contains 100 non-synonymous single nucleotide polymorphisms (nsSNPs), and 24 missense SNPs have been determined as deleterious by all analyses. In this work, six well-known computational tools were employed to study Leukemia-associated nsSNPs. It is inferred that these nsSNPs could play their role in the up-regulation of FANCA, which further leads to provoke leukemia advancement. The current research would benefit researchers and practitioners in handling cancer-associated diseases related to FANCA. The proposed study would also help to develop precision medicine in the field of drug discovery.
Moore's law, Wright's law and the Countdown to Exponential Space
Authors: Daniel Berleant, Venkat Kodali, Richard Segall, Hyacinthe Aboudja, Michael Howell
Date: 2021-07-08 00:00:00

Technologies have often been observed to improve exponentially over time. In practice this often means identifying a constant known as the doubling time, describing the time period over which the technology roughly doubles in some measure of performance or of performance per dollar. Moore's law is, classically, the empirical observation that the number of electronic components that can be put on a chip doubles every 18 months to 2 years. Today it is frequently stated as the number of computations available per unit of cost. Generalized to the appropriate doubling time, it describes the rate of advancement in many technologies. A frequently noted competitor to Moore's law is known as Wright's law, which has aeronautical roots. Wright's law (also called power law, experience curve and Henderson's law) relates some quality of a manufactured unit (for Wright, airplanes) to the volume of units manufactured. The Wright's law equation expresses the idea that performance - price or a quality metric - improves according to a power of the number produced, or alternatively stated, improves by a constant percentage for every doubling of the total number produced. Does exploration of outer space conform to Moore's law or Wright's law-like behavior? Our results below are broadly consistent with these laws. This is true for many technologies. Although the two laws can make somewhat different predictions, Sahal found that they converge to the same predictions when manufacturing volume increases exponentially over time. When space exploration transitions into an independent commercial sector, as many people hope and expect, spacecraft technology will then likely enter an era of unambiguously exponential advancement.
Co-optimization of Energy and Reserve with Incentives to Wind Generation: Case Study
Authors: Yves Smeers, Sebastian Martin, Jose A. Aguado
Date: 2021-07-03 00:00:00
Tasks: Sensitivity

This case study presents an analysis and quantification of the impact of the lack of co-optimization of energy and reserve in the presence of high penetration of wind energy. The methodology is developed in a companion paper, Part I. Two models, with and without co-optimization are confronted. The modeling of reserve and the incentive to renewable as well as the calibration of the model are inspired by the Spanish market. A sensitivity analysis is performed on configurations that differ by generation capacity, ramping capability, and market parameters (available wind, Feed in Premium to wind, generators risk aversion, and reserve requirement). The models and the case study are purely illustrative but the methodology is general.
Volatility of S&P500: Estimation and Evaluation
Authors: Wen Su
Date: 2021-07-20 00:00:00
Tasks: Management

In an era when derivatives is getting popular, risk management has gradually become the core content of modern finance. In order to study how to accurately estimate the volatility of the S&P 500 index, after introducing the theoretical background of several methods, this paper uses the historical volatility method, GARCH model method and implied volatility method to estimate the real volatility respectively. At the same time, two ways of adjusting the estimation window, rolling and increasing, are also considered. The unbiased test and goodness of fit test are used to evaluate these methods. The empirical result shows that the implied volatility is the best estimator of the real volatility. The rolling estimation window is recommended when using the historical volatility. On the contrary, the estimation window is supposed to be increased when using the GARCH model.
Order Book Queue Hawkes-Markovian Modeling
Authors: Philip Protter, Qianfan Wu, Shihao Yang
Date: 2021-07-20 00:00:00
Tasks: Model Selection, Variable Selection

This article presents a Hawkes process model with Markovian baseline intensities for high-frequency order book data modeling. We classify intraday order book trading events into a range of categories based on their order types and the price changes after their arrivals. To capture the stimulating effects between multiple types of order book events, we use the multivariate Hawkes process to model the self- and mutually-exciting event arrivals. We also integrate a Markovian baseline intensity into the event arrival dynamic, by including the impacts of order book liquidity state and time factor to the baseline intensity. A regression-based non-parametric estimation procedure is adopted to estimate the model parameters in our Hawkes+Markovian model. To eliminate redundant model parameters, LASSO regularization is incorporated in the estimation procedure. Besides, model selection method based on Akaike Information Criteria is applied to evaluate the effect of each part of the proposed model. An implementation example based on real LOB data is provided. Through the example, we study the empirical shapes of Hawkes excitement functions, the effects of liquidity state as well as time factors, the LASSO variable selection, and the explanatory power of Hawkes and Markovian elements to the dynamics of the order book.
GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation
Authors: Zhijing Jin, Qipeng Guo, Xipeng Qiu, Zheng Zhang
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Knowledge Graphs, Text Generation, Unsupervised KG-to-Text Generation

Data collection for the knowledge graph-to-text generation is expensive. As a result, research on unsupervised models has emerged as an active field recently. However, most unsupervised models have to use non-parallel versions of existing small supervised datasets, which largely constrain their potential. In this paper, we propose a large-scale, general-domain dataset, GenWiki. Our unsupervised dataset has 1.3M text and graph examples, respectively. With a human-annotated test set, we provide this new benchmark dataset for future research on unsupervised text generation from knowledge graphs.
Regularized Classification-Aware Quantization
Authors: Daniel Severo, Elad Domanovitz, Ashish Khisti
Date: 2021-07-12 00:00:00
Tasks: Binary Classification, Classification, Quantization

Traditionally, quantization is designed to minimize the reconstruction error of a data source. When considering downstream classification tasks, other measures of distortion can be of interest; such as the 0-1 classification loss. Furthermore, it is desirable that the performance of these quantizers not deteriorate once they are deployed into production, as relearning the scheme online is not always possible. In this work, we present a class of algorithms that learn distributed quantization schemes for binary classification tasks. Our method performs well on unseen data, and is faster than previous methods proportional to a quadratic term of the dataset size. It works by regularizing the 0-1 loss with the reconstruction error. We present experiments on synthetic mixture and bivariate Gaussian data and compare training, testing, and generalization errors with a family of benchmark quantization schemes from the literature. Our method is called Regularized Classification-Aware Quantization.