pwc-archive/papers-with-abstracts huggingface dataset:
AI and conventional methods for UCT projection data estimation
Authors: Ankur Kumar, Prasunika Khare, Mayank Goswami
Date: 2021-08-17 00:00:00
Tasks: Specificity

A 2D Compact ultrasound computerized tomography (UCT) system is developed. Fully automatic post processing tools involving signal and image processing are developed as well. Square of the amplitude values are used in transmission mode with natural 1.5 MHz frequency and rise time 10.4 ns and fall time 8.4 ns and duty cycle of 4.32%. Highest peak to corresponding trough values are considered as transmitting wave between transducers in direct line talk. Sensitivity analysis of methods to extract peak to corresponding trough per transducer are discussed in this paper. Total five methods are tested. These methods are taken from broad categories: (a) Conventional and (b) Artificial Intelligence (AI) based methods. Conventional methods, namely: (a) simple gradient based peak detection, (b) Fourier based, (c) wavelet transform are compared with AI based methods: (a) support vector machine (SVM), (b) artificial neural network (ANN). Classification step was performed as well to discard the signal which does not has contribution of transmission wave. It is found that conventional methods have better performance. Reconstruction error, accuracy, F-Score, recall, precision, specificity and MCC for 40 x 40 data 1600 data files are measured. Each data file contains 50,002 data point. Ten such data files are used for training the Neural Network. Each data file has 7/8 wave packets and each packet corresponds to one transmission amplitude data. Reconstruction error is found to be minimum for ANN method. Other performance indices show that FFT method is processing the UCT signal with best recovery.
Achieving Full Grating-Lobe-Free Field-of-View with Low-Complexity Co-prime Photonic Beamforming Transceivers
Authors: Aroutin Khachaturian, Reza Fatemi, Ali Hajimiri
Date: 2021-08-17 00:00:00

Integrated photonic active beamforming can significantly reduce the size and cost of coherent imagers for LiDAR and medical imaging applications. In current architectures, the complexity of photonic and electronic circuitry linearly increases with the desired imaging resolution. We propose a novel photonic transceiver architecture based on co-prime sampling techniques that breaks this trade-off and achieves the full (radiating-element-limited) field-of-view (FOV) for a 2D aperture with a single-frequency laser. Using only order-of-N radiating elements, this architecture achieves beamwidth and side-lobe level (SLL) performance equivalent to a transceiver with order-of-N-squared elements with half-wavelength spacing. Furthermore, we incorporate a pulse amplitude modulation (PAM) row-column drive methodology to reduce the number of required electrical drivers for this architecture from order of N to order of square root of N. A silicon photonics implementation of this architecture using two 64-element apertures, one for transmitting and one for receiving, requires only 34 PAM electrical drivers and achieves a transceiver SLL of -11.3dB with 1026 total resolvable spots, and 0.6 degree beamwidth within a 23x16.3 degree FOV.
Feasible Weighted Projected Principal Component Analysis for Factor Models with an Application to Bond Risk Premia
Authors: Sung Hoon Choi
Date: 2021-08-23 00:00:00

I develop a feasible weighted projected principal component (FPPC) analysis for factor models in which observable characteristics partially explain the latent factors. This novel method provides more efficient and accurate estimators than existing methods. To increase estimation efficiency, I take into account both cross-sectional dependence and heteroskedasticity by using a consistent estimator of the inverse error covariance matrix as the weight matrix. To improve accuracy, I employ a projection approach using characteristics because it removes noise components in high-dimensional factor analysis. By using the FPPC method, estimators of the factors and loadings have faster rates of convergence than those of the conventional factor analysis. Moreover, I propose an FPPC-based diffusion index forecasting model. The limiting distribution of the parameter estimates and the rate of convergence for forecast errors are obtained. Using U.S. bond market and macroeconomic data, I demonstrate that the proposed model outperforms models based on conventional principal component estimators. I also show that the proposed model performs well among a large group of machine learning techniques in forecasting excess bond returns.
Inference in high-dimensional regression models without the exact or $L^p$ sparsity
Authors: Jooyoung Cha, Harold D. Chiang, Yuya Sasaki
Date: 2021-08-21 00:00:00
Tasks: regression

This paper proposes a new method of inference in high-dimensional regression models and high-dimensional IV regression models. Estimation is based on a combined use of the orthogonal greedy algorithm, high-dimensional Akaike information criterion, and double/debiased machine learning. The method of inference for any low-dimensional subvector of high-dimensional parameters is based on a root-$N$ asymptotic normality, which is shown to hold without requiring the exact sparsity condition or the $L^p$ sparsity condition. Simulation studies demonstrate superior finite-sample performance of this proposed method over those based on the LASSO or the random forest, especially under less sparse models. We illustrate an application to production analysis with a panel of Chilean firms.
Regression Discontinuity Designs
Authors: Matias D. Cattaneo, Rocio Titiunik
Date: 2021-08-20 00:00:00
Tasks: Causal Inference, regression

The Regression Discontinuity (RD) design is one of the most widely used non-experimental methods for causal inference and program evaluation. Over the last two decades, statistical and econometric methods for RD analysis have expanded and matured, and there is now a large number of methodological results for RD identification, estimation, inference, and validation. We offer a curated review of this methodological literature organized around the two most popular frameworks for the analysis and interpretation of RD designs: the continuity framework and the local randomization framework. For each framework, we discuss three main topics: (i) designs and parameters, which focuses on different types of RD settings and treatment effects of interest; (ii) estimation and inference, which presents the most popular methods based on local polynomial regression and analysis of experiments, as well as refinements, extensions, and alternatives; and (iii) validation and falsification, which summarizes an array of mostly empirical approaches to support the validity of RD designs in practice.
Gender Differences in the Cost of Corrections in Group Work
Authors: Yuki Takahashi
Date: 2021-08-23 00:00:00

Corrections among colleagues are an integral part of group work, but people may take corrections as personal criticism, especially corrections by women. I study whether people dislike collaborating with someone who corrects them and more so when that person is a woman. People, including those with high productivity, are less willing to collaborate with a person who has corrected them even if the correction improves group performance. Yet, people respond to corrections by women as negatively as by men. These findings suggest that although women do not face a higher hurdle, correcting colleagues is costly and reduces group efficiency.
Welfare Effects of Labor Income Tax Changes on Married Couples: A Sufficient Statistics Approach
Authors: Egor Malkov
Date: 2021-08-23 00:00:00

This paper develops a framework for assessing the welfare effects of labor income tax changes on married couples. I build a static model of couples' labor supply that features both intensive and extensive margins and derive a tractable expression that delivers a transparent understanding of how labor supply responses, policy parameters, and income distribution affect the reform-induced welfare gains. Using this formula, I conduct a comparative welfare analysis of four tax reforms implemented in the United States over the last four decades, namely the Tax Reform Act of 1986, the Omnibus Budget Reconciliation Act of 1993, the Economic Growth and Tax Relief Reconciliation Act of 2001, and the Tax Cuts and Jobs Act of 2017. I find that these reforms created welfare gains ranging from -0.16 to 0.62 percent of aggregate labor income. A sizable part of the gains is generated by the labor force participation responses of women. Despite three reforms resulted in aggregate welfare gains, I show that each reform created both winners and losers. Furthermore, I uncover two patterns in the relationship between welfare gains and couples' labor income. In particular, the reforms of 1986 and 2017 display a monotonically increasing relationship, while the other two reforms demonstrate a U-shaped pattern. Finally, I characterize the bias in welfare gains resulting from the assumption about a linear tax function. I consider a reform that changes tax progressivity and show that the linearization bias is given by the ratio between the tax progressivity parameter and the inverse elasticity of taxable income. Quantitatively, it means that linearization overestimates the welfare effects of the U.S. tax reforms by 3.6-18.1%.
The changing dynamics of HIV/AIDS during the Covid-19 pandemic in the Rohingya refugee camps in Bangladesh a call for action
Authors: Muhammad Anwar Hossain, Iryna Zablotska-Manos
Date: 2021-08-22 00:00:00
Tasks: Management

COVID-19 pandemic has affected each and every country's health service and plunged refugees into the most desperate conditions. The plight of Rohingya refugees is among the harshest. It has severely affected their existing HIV/STI prevention and management services and further increased the risk of violence and onward HIV transmission within the camps. In this commentary, we discuss the context and the changing dynamics of HIV/AIDS during COVID-19 among the Rohingya refugee community in Bangladesh. What we currently observe is the worst crisis in the Rohingya refugee camps thus far. Firstly, because of being displaced, Rohingya refugees have increased vulnerability to HIV, as well as to STIs and other poor health outcomes. Secondly, for the same reason, they have inadequate access to HIV testing treatment and care. Not only because of their refugee status but also because of the poor capacity of the host country to provide services. Thirdly, a host of complex economic, socio-cultural and behavioural factors exacerbate their dire situation with access to HIV testing, treatment and care. And finally, the advent of the COVID-19 pandemic has changed priorities in all societies, including the refugee camps. In the context of the unfolding COVID-19 crisis, more emphasis is placed on COVID-19 rather than other health issues, which exacerbates the dire situation with HIV detection, management, and prevention among Rohingya refugees. Despite the common crisis experienced by most countries around the world, the international community has an obligation to work together to improve the life, livelihood, and health of those who are most vulnerable. Rohingya refugees are among them.
Ex-post implementation with interdependent values
Authors: Saurav Goyal, Aroon Narayanan
Date: 2021-08-21 00:00:00

We characterize ex-post implementable allocation rules for single object auctions under quasi-linear preferences with convex interdependent value functions. We show that requiring ex-post implementability is equivalent to requiring that the allocation rule must satisfy a condition that we call eventual monotonicity (EM), which is a weakening of monotonicity, a familiar condition used to characterize dominant strategy implementation.
Noise2Fast: Fast Self-Supervised Single Image Blind Denoising
Authors: Jason Lequyer, Reuben Philip, Amit Sharma, Laurence Pelletier
Date: 2021-08-23 00:00:00
Tasks: Denoising, Image Denoising

In the last several years deep learning based approaches have come to dominate many areas of computer vision, and image denoising is no exception. Neural networks can learn by example to map noisy images to clean images. However, access to noisy/clean or even noisy/noisy image pairs isn't always readily available in the desired domain. Recent approaches have allowed for the denoising of single noisy images without access to any training data aside from that very image. But since they require both training and inference to be carried out on each individual input image, these methods require significant computation time. As such, they are difficult to integrate into automated microscopy pipelines where denoising large datasets is essential but needs to be carried out in a timely manner. Here we present Noise2Fast, a fast single image blind denoiser. Our method is tailored for speed by training on a four-image dataset produced using a unique form of downsampling we refer to as "checkerboard downsampling". Noise2Fast is faster than all tested approaches and is more accurate than all except Self2Self, which takes well over 100 times longer to denoise an image. This allows for a combination of speed and flexibility that was not previously attainable using any other method.
Sparse regularization with a non-convex penalty for SAR imaging and autofocusing
Authors: Zi-Yao Zhang, Odysseas Pappas, Igor G. Rizaev, Alin Achim
Date: 2021-08-22 00:00:00
Tasks: Image Reconstruction

In this paper, SAR image reconstruction with joint phase error estimation (autofocusing) is formulated as an inverse problem. An optimization model utilising a sparsity-enforcing Cauchy regularizer is proposed, and an alternating minimization framework is used to solve it, in which the desired image and the phase errors are optimized alternatively. For the image reconstruction sub-problem (f-sub-problem), two methods are presented capable of handling the problem's complex nature, and we thus present two variants of our SAR image autofocusing algorithm. Firstly, we design a complex version of the forward-backward splitting algorithm (CFBA) to solve the f-sub-problem iteratively. For the second variant, the Wirtinger alternating minimization autofocusing (WAMA) method is presented, in which techniques of Wirtinger calculus are utilized to minimize the complex-valued cost function in the f-sub-problem in a direct fashion. For both methods, the phase error estimation sub-problem is solved by simply expanding and observing its cost function. Moreover, the convergence of both algorithms is discussed in detail. By conducting experiments on both simulated scenes and real SAR images, the proposed method is demonstrated to give impressive autofocusing results compared to other state of the art methods.
MITI Minimum Information guidelines for highly multiplexed tissue images
Authors: Denis Schapiro, Clarence Yapp, Artem Sokolov, Sheila M. Reynolds, Yu-An Chen, Damir Sudar, Yubin Xie, Jeremy L. Muhlich, Raquel Arias-Camison, Sarah Arena, Adam J. Taylor, Milen Nikolov, Madison Tyler, Jia-Ren Lin, Erik A. Burlingame, Human Tumor Atlas Network, Young H. Chang, Samouil L Farhi, Vésteinn Thorsson, Nithya Venkatamohan, Julia L. Drewes, Dana Pe'er, David A. Gutman, Markus D. Herrmann, Nils Gehlenborg, Peter Bankhead, Joseph T. Roland, John M. Herndon, Michael P. Snyder, Michael Angelo, Garry Nolan, Jason R. Swedlow, Nikolaus Schultz, Daniel T. Merrick, Sarah A. Mazzilli, Ethan Cerami, Scott J. Rodig, Sandro Santagata, Peter K. Sorger
Date: 2021-08-21 00:00:00

The imminent release of tissue atlases combining multi-channel microscopy with single cell sequencing and other omics data from normal and diseased specimens creates an urgent need for data and metadata standards that guide data deposition, curation and release. We describe a Minimum Information about highly multiplexed Tissue Imaging (MITI) standard that applies best practices developed for genomics and other microscopy data to highly multiplexed tissue images and traditional histology.
Suitability of FPS and DPS in NOMA for Real-Time and Non-Real Time Applications
Authors: Moontasir Rafique, Abdullah Alavi, Aadnan Farhad, Mohammad T. Kawser
Date: 2021-08-23 00:00:00

Non-Orthogonal Multiple Access (NOMA) is a popular solution for supporting a high number of users and along with significant bandwidth in 5G cellular communication. By using a technique called cooperative relaying, the same data is sent to all the users, and one user can relay data to the other. In order to provide enough power for the users, energy harvesting techniques have been introduced with Simultaneous Wireless Information and Power Transfer (SWIPT) coming to prominence in recent times. In this paper, analysis has been made comparing two different power allocation schemes in NOMA, Fixed Power allocation Scheme (FPS) and Dynamic Power allocation Scheme (DPS). The comparisons were made in terms of their performance and characteristics while undergoing SWIPT. It has been found that by using DPS, an almost 25% increase in peak spectral efficiency can be obtained compared to FPS. However, DPS suffers from a higher outage probability as the increase of power causes the signal bandwidth to drop below the target rate a significant number of times. Based on the detailed results, conclusions were drawn as to which power allocation coefficient scheme would be used in real-time and non-real time communication standards, respectively. The results suggest that for real-time communication, FPS is more suitable while for non-real-time communication, DPS appears to work better than FPS.
Elevation Angle-Dependent 3D Trajectory Design for Aerial RIS-aided Communication
Authors: Yifan Liu, Bin Duo, Qingqing Wu, Xiaojun Yuan, Jun Li, Yonghui Li
Date: 2021-08-23 00:00:00
Tasks: Scheduling

This paper investigates an aerial reconfigurable intelligent surface (RIS)-aided communication system under the probabilistic line-of-sight (LoS) channel, where an unmanned aerial vehicle (UAV) equipped with an RIS is deployed to assist two ground nodes in their information exchange. An optimization problem with the objective of maximizing the minimum average achievable rate is formulated to jointly design the communication scheduling, the RIS's phase shift, and the three-dimensional (3D) UAV trajectory. To solve such a non-convex problem, we propose an efficient iterative algorithm to obtain its suboptimal solution. Simulation results show that our proposed design significantly outperforms the existing schemes and provides new insights into the elevation angle and distance trade-off for the UAV-borne RIS communication system.
Automatic Modulation Classification Using Involution Enabled Residual Networks
Authors: Hao Zhang, Lu Yuan, Guangyu Wu, Fuhui Zhou, Qihui Wu
Date: 2021-08-23 00:00:00
Tasks: Classification

Automatic modulation classification (AMC) is of crucial importance for realizing wireless intelligence communications. Many deep learning based models especially convolution neural networks (CNNs) have been proposed for AMC. However, the computation cost is very high, which makes them inappropriate for beyond the fifth generation wireless communication networks that have stringent requirements on the classification accuracy and computing time. In order to tackle those challenges, a novel involution enabled AMC scheme is proposed by using the bottleneck structure of the residual networks. Involution is utilized instead of convolution to enhance the discrimination capability and expressiveness of the model by incorporating a self-attention mechanism. Simulation results demonstrate that our proposed scheme achieves superior classification performance and faster convergence speed comparing with other benchmark schemes.
Pairwise Node Localization From Differences in Their UWB Channels to Observer Nodes
Authors: Gregor Dumphart, Robin Kramer, Robert Heyn, Marc Kuhn, Armin Wittneben
Date: 2021-08-22 00:00:00

We consider the problem of localization and distance estimation between a pair of wireless nodes in a multipath propagation environment, but not the usual way of processing a channel measurement between them. We propose a novel paradigm which compares the two nodes' ultra-wideband (UWB) channels to other nodes, called observers. The main idea is that the dissimilarity between the channel impulse responses (CIRs) increases with $d$ and allows for an estimate $\hat{d}$. Our approach relies on extracting common multipath components (MPCs) from the CIRs. This is realistic in indoor or urban scenarios and if $d$ is considerably smaller than the observer distances. We present distance estimators which utilize the rich location information contained in MPC delay differences. Likewise, we present estimators for the relative position vector which process both MPC delays and MPC directions. We do so for various important cases: with and without time synchronization, delay measurement errors, and knowledge of the MPC association between the CIRs. The estimators exhibit great technological advantages: they do not require line-of-sight conditions, observer location knowledge, or environment knowledge. We study the estimation accuracy with a numerical evaluation based on random sampling and, additionally, with an experimental evaluation based on measurements in an indoor environment. The proposal shows the potential for great accuracy in theory and practice. We describe how the paradigm could incorporate novel measurements into cooperative localization frameworks for spatio-temporal tracking. This could enable affordable wireless network localization in dynamic multipath settings.
Energy Efficient Power Allocation in Massive MIMO Systems with Mismatch Channel Estimation Error
Authors: Mahdi Nangir, Abdolrasoul Sakhaei Gharagezlou, Nima Imani
Date: 2021-08-22 00:00:00

In this paper, we investigate the energy efficient power allocation for the downlink in the massive multiple-input multiple-output (MIMO) systems under zero-forcing (ZF) receiver. The radio frequencies (RF) that have a significant effect on system performance has not been considered in most of previous researches. Increasing of random changes in RF creates a mismatch channel. We must consider the effects of the RF circuit for the performance evaluation of the massive MIMO systems. We also consider the rate of system in the presence of estimation error, similar to real world, with the quality of service (QoS) constraint and the transfer capacity of users. In the scenario of this paper, users are divided into two groups. The first group are users who have stronger channel conditions or in other words are located in the center of the cell, and the second group belongs to users who have weak channel conditions and are located at the edge of the cell. By using Karush-Kuhn-Tucker (KKT) conditions, we obtain the optimal power of users. The results of implementation and simulations are given to confirm the efficiency of the proposed algorithm.
Development of an R-Mode Simulator Using MF DGNSS Signals
Authors: Suhui Jeong, Pyo-Woong Son
Date: 2021-08-21 00:00:00

With the development of positioning, navigation, and timing (PNT) information-based industries, PNT information is becoming increasingly important. Therefore, various navigation studies have been actively conducted to back up global positioning system (GPS) in scenarios in which it is disabled. Ranging using signals of opportunity (SoOP) has the advantage of infrastructure already being in place. Among them, the ranging mode (R-Mode) is a technology that uses available SoOPs such as a medium frequency (MF) differential global navigation satellite System (DGNSS) signal that has recently been recognized for its potential for navigation and is currently under research. In this study, we developed a signal simulator that considers the characteristics of MF DGNSS signals and skywaves used in R-Mode.