pwc-archive/papers-with-abstracts huggingface dataset:
Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects
Authors: Lina Zhang, David T. Frazier, D. S. Poskitt, Xueyan Zhao
Date: 2020-09-06 00:00:00

This paper examines the identification power of instrumental variables (IVs) for average treatment effect (ATE) in partially identified models. We decompose the ATE identification gains into components of contributions driven by IV relevancy, IV strength, direction and degree of treatment endogeneity, and matching via exogenous covariates. Our decomposition is demonstrated with graphical illustrations, simulation studies and an empirical example of childbearing and women's labour supply. Our analysis offers insights for understanding the complex role of IVs in ATE identification and for selecting IVs in practical policy designs. Simulations also suggest potential uses of our analysis for detecting irrelevant instruments.
Optimal Stratification of Survey Experiments
Authors: Max Cytrynbaum
Date: 2021-11-16 00:00:00
Tasks: Survey

This paper studies a two-stage model of experimentation, where the researcher first samples representative units from an eligible pool, then assigns each sampled unit to treatment or control. To implement balanced sampling and assignment, we introduce a new family of finely stratified designs that generalize matched pairs randomization to propensities p(x) not equal to 1/2. We show that two-stage stratification nonparametrically dampens the variance of treatment effect estimation. We formulate and solve the optimal stratification problem with heterogeneous costs and fixed budget, providing simple heuristics for the optimal design. In settings with pilot data, we show that implementing a consistent estimate of this design is also efficient, minimizing asymptotic variance subject to the budget constraint. We also provide new asymptotically exact inference methods, allowing experimenters to fully exploit the efficiency gains from both stratified sampling and assignment. An application to nine papers recently published in top economics journals demonstrates the value of our methods.
Exploration of the Parameter Space in Macroeconomic Agent-Based Models
Authors: Karl Naumann-Woleske, Max Sina Knicker, Michael Benzaquen, Jean-Philippe Bouchaud
Date: 2021-11-16 00:00:00

Agent-Based Models (ABM) are computational scenario-generators, which can be used to predict the possible future outcomes of the complex system they represent. To better understand the robustness of these predictions, it is necessary to understand the full scope of the possible phenomena the model can generate. Most often, due to high-dimensional parameter spaces, this is a computationally expensive task. Inspired by ideas coming from systems biology, we show that for multiple macroeconomic models, including an agent-based model and several Dynamic Stochastic General Equilibrium (DSGE) models, there are only a few stiff parameter combinations that have strong effects, while the other sloppy directions are irrelevant. This suggest an algorithm that efficiently explores the space of parameters by primarily moving along the stiff directions. We apply our algorithm to a medium-sized agent-based model, and show that it recovers all possible dynamics of the unemployment rate. The application of this method to Agent-based Models may lead to a more thorough and robust understanding of their features, and provide enhanced parameter sensitivity analyses. Several promising paths for future research are discussed.
Spillovers of US Interest Rates: Monetary Policy & Information Effects
Authors: Santiago Camara
Date: 2021-11-16 00:00:00

This paper quantifies the international spillovers of US monetary policy by exploiting the high-frequency movement of multiple financial assets around FOMC announcements. I use the identification strategy introduced by Jarocinski & Karadi (2022) to identify two FOMC shocks: a pure US monetary policy and an information disclosure shock. These two FOMC shocks have intuitive and very different international spillovers. On the one hand, a US tightening caused by a pure US monetary policy shock leads to an economic recession, an exchange rate depreciation and tighter financial conditions. On the other hand, a tightening of US monetary policy caused by the FOMC disclosing positive information about the state of the US economy leads to an economic expansion, an exchange rate appreciation and looser financial conditions. Ignoring the disclosure of information by the FOMC biases the impact of a US monetary policy tightening and may explain recent atypical findings.
Optimal index insurance and basis risk decomposition: an application to Kenya
Authors: Matthieu Stigler, David Lobell
Date: 2021-11-16 00:00:00

Index insurance is a promising tool to reduce the risk faced by farmers, but high basis risk, which arises from imperfect correlation between the index and individual farm yields, has limited its adoption to date. Basis risk arises from two fundamental sources: the intrinsic heterogeneity within an insurance zone (zonal risk), and the lack of predictive accuracy of the index (design risk). Whereas previous work has focused almost exclusively on design risk, a theoretical and empirical understanding of the role of zonal risk is still lacking. Here we investigate the relative roles of zonal and design risk, using the case of maize yields in Kenya. Our first contribution is to derive a formal decomposition of basis risk, providing a simple upper bound on the insurable basis risk that any index can reach within a given zone. Our second contribution is to provide the first large-scale empirical analysis of the extent of zonal versus design risk. To do so, we use satellite estimates of yields at 10m resolution across Kenya, and investigate the effect of using smaller zones versus using different indices. Our results show a strong local heterogeneity in yields, underscoring the challenge of implementing index insurance in smallholder systems, and the potential benefits of low-cost yield measurement approaches that can enable more local definitions of insurance zones.
Price Stability of Cryptocurrencies as a Medium of Exchange
Authors: Tatsuru Kikuchi, Toranosuke Onishi, Kenichi Ueda
Date: 2021-11-16 00:00:00
Tasks: Dynamic Time Warping

We present positive evidence of price stability of cryptocurrencies as a medium of exchange. For the sample years from 2016 to 2020, the prices of major cryptocurrencies are found to be stable, relative to major financial assets. Specifically, after filtering out the less-than-one-month cycles, we investigate the daily returns in US dollars of the major cryptocurrencies (i.e., Bitcoin, Ethereum, and Ripple) as well as their comparators (i.e., major legal tenders, the Euro and Japanese yen, and the major stock indexes, S&P 500 and MSCI World Index). We examine the stability of the filtered daily returns using three different measures. First, the Pearson correlations increased in later years in our sample. Second, based on the dynamic time-warping method that allows lags and leads in relations, the similarities in the daily returns of cryptocurrencies with their comparators have been present even since 2016. Third, we check whether the cumulative sum of errors to predict cryptocurrency prices, assuming stable relations with comparators' daily returns, does not exceeds the bounds implied by the Black-Scholes model. This test, in other words, does not reject the efficient market hypothesis.
Speech Prediction using an Adaptive Recurrent Neural Network with Application to Packet Loss Concealment
Authors: Reza Lotfidereshgi, Philippe Gournay
Date: 2021-11-15 00:00:00
Tasks: Packet Loss Concealment

This paper proposes a novel approach for speech signal prediction based on a recurrent neural network (RNN). Unlike existing RNN-based predictors, which operate on parametric features and are trained offline on a large collection of such features, the proposed predictor operates directly on speech samples and is trained online on the recent past of the speech signal. Optionally, the network can be pre-trained offline to speed-up convergence at start-up. The proposed predictor is a single end-to-end network that captures all sorts of dependencies between samples, and therefore has the potential to outperform classical linear/non-linear and short-term/long-term speech predictor structures. We apply it to the packet loss concealment (PLC) problem and show that it outperforms the standard ITU G.711 Appendix I PLC technique.
Weighted Histogram Equalization Using Entropy of Probability Density Function
Authors: Thaweesak Trongtirakul, Sos Agaian
Date: 2021-11-16 00:00:00
Tasks: Image Enhancement, Tone Mapping

Low-contrast image enhancement is essential for high-quality image display and other visual applications. However, it is a challenging task as the enhancement is expected to increase the visibility of an image while maintaining its naturalness. In this paper, the weighted histogram equalization using the entropy of the probability density function is proposed. The computation of the local mapping functions utilizes the relationship between non-height bin and height bin distributions. Finally, the complete tone mapping function is produced by concatenating local mapping functions. Computer simulation results on the CSIQ dataset demonstrate that the proposed method produces images with higher visibility and visual quality, which outperforms traditional and recently proposed contrast enhancement algorithms methods in qualitative and quantitative metrics.
Image Denoising in FPGA using Generic Risk Estimation
Authors: Rinson Varghese, Chandrasekhar Seelamantula, Rathna G N, Ashutosh Gupta, Debajyoti Dhar
Date: 2021-11-16 00:00:00
Tasks: Denoising, Image Denoising

The generic risk estimator addresses the problem of denoising images corrupted by additive white noise without placing any restriction on the statistical distribution of the noise. In this paper, we discuss an efficient FPGA implementation of this algorithm. We use the undecimated Haar wavelet transform with shrinkage parameters for each sub-band as the denoising function. The computational complexity and memory requirement of the algorithm is first analyzed. To optimize the performance, a combination of convolution and recursion is employed to realize Haar filter bank and gradient descent algorithm is used to find the shrinkage parameters. A fully pipelined and parallel architecture is developed to achieve high throughput. The proposed design achieves an execution time of 3.5ms for an image of size 512x512. We also show that the recursive implementation of Haar wavelet is more expensive than the direct implementation in terms of hardware utilization.
RapidRead: Global Deployment of State-of-the-art Radiology AI for a Large Veterinary Teleradiology Practice
Authors: Michael Fitzke, Conrad Stack, Andre Dourson, Rodrigo M. B. Santana, Diane Wilson, Lisa Ziemer, Arjun Soin, Matthew P. Lungren, Paul Fisher, Mark Parkinson
Date: 2021-11-09 00:00:00
Tasks: Drift Detection

This work describes the development and real-world deployment of a deep learning-based AI system for evaluating canine and feline radiographs across a broad range of findings and abnormalities. We describe a new semi-supervised learning approach that combines NLP-derived labels with self-supervised training leveraging more than 2.5 million x-ray images. Finally we describe the clinical deployment of the model including system architecture, real-time performance evaluation and data drift detection.
A Dynamic Codebook Design for Analog Beamforming in MIMO LEO Satellite Communications
Authors: Joan Palacios, Nuria González-Prelcic, Carlos Mosquera, Takayuki Shimizu
Date: 2021-11-16 00:00:00

Beamforming gain is a key ingredient in the performance of LEO satellite communication systems to be integrated into cellular networks. However, beam codebooks previously designed in the context of MIMO communication for terrestrial networks, do not provide the appropriate performance in terms of inter-beam interference and gain stability as the satellite moves. In this paper, we propose a dynamic codebook that provides a stable gain during the period of time that the satellite covers a given cell, while avoiding link retraining and extra calculation as the satellite moves. In addition, the proposed codebook provides a higher signal-to-interference-plus-noise (SINR) ratio than those DFT codebooks commonly used in cellular systems.
Array Placement in Distributed Massive MIMO for Power Saving considering Environment Information
Authors: Yi-Hang Zhu, Gilles Callebaut, Liesbet Van der Perre, François Rottenberg
Date: 2021-11-16 00:00:00

Distributed massive MIMO (D-mMIMO) has been considered for future networks as it holds the potential to offer superior capacity while enabling energy savings in the network. A D-mMIMO system has multiple arrays. Optimizing the locations of the arrays is essential for the energy efficiency of the system. In existing works, array placement has been optimized mostly based on common channel models, which rely on a given statistical distribution and Euclidean distance between user locations and arrays. These models are justified if applied to sufficiently large cells, where the statistical description of the channel is expected to fit its empirical condition. However, with the advent of small cells, this is no longer the case. The channel propagation condition becomes highly environment-specific. This paper investigates array placement optimization with different ways of modeling the propagation conditions taking the environment information (e.g., buildings) into account. We capture the environment information via a graph. Two shortest path-based propagation models are introduced based on the graph. We validate the performance of the models with a ray-tracing simulator considering different signal coverage levels. The simulation results demonstrate that higher energy efficiency is achieved by using the array placements found via the proposed models compared to a Euclidean distance-based propagation model in small cells. The average power saving at 96% signal coverage, for example, reaches more than 5 dB.
Radar Aided mmWave Vehicle-to-InfrastructureLink Configuration Using Deep Learning
Authors: Yun Chen, Andrew Graff, Nuria González-Prelcic, Takayuki Shimizu
Date: 2021-11-16 00:00:00
Tasks: Deep Learning, Prediction

The high overhead of the beam training process is the main challenge when establishing mmWave communication links, especially for vehicle-to-everything (V2X) scenarios where the channels are highly dynamic. In this paper, we obtain prior information to speed up the beam training process by implementing two deep neural networks (DNNs) that realize radar-to-communication (R2C) channel information translation in a vehicle-to-infrastructure (V2I) system. Specifically, the first DNN is built to extract the information from the radar azimuth power spectrum (APS) to reconstruct the communication APS, while the second DNN exploits the information extracted from the spatial covariance of the radar channel to realize R2C covariance prediction. The achieved data rate and the similarity between the estimated and the true communication APS are used to evaluate the prediction performance. The covariance estimation method generally provides higher similarity, as the APS predictions cannot always capture the mismatch between the radar and communication APS. Compared to the beam training method which exploits directly the radar APS without an attempt to translate it to the communication channel, our proposed deep learning (DL) aided methods remarkably reduce the beam training overhead, resulting in a 13.3% and 21.9% rate increase when using the communication APS prediction and covariance prediction, respectively.
Blind Channel Estimation for MIMO Systems via Variational Inference
Authors: Jiancheng Tang, Qianqian Yang, Zhaoyang Zhang
Date: 2021-11-16 00:00:00
Tasks: Variational Inference

In this paper, we investigate the blind channel estimation problem for MIMO systems under Rayleigh fading channel. Conventional MIMO communication techniques require transmitting a considerable amount of training symbols as pilots in each data block to obtain the channel state information (CSI) such that the transmitted signals can be successfully recovered. However, the pilot overhead and contamination become a bottleneck for the practical application of MIMO systems with the increase of the number of antennas. To overcome this obstacle, we propose a blind channel estimation framework, where we introduce an auxiliary posterior distribution of CSI and the transmitted signals given the received signals to derive a lower bound to the intractable likelihood function of the received signal. Meanwhile, we generate this auxiliary distribution by a neural network based variational inference framework, which is trained by maximizing the lower bound. The optimal auxiliary distribution which approaches real prior distribution is then leveraged to obtain the maximum a posterior (MAP) estimation of channel matrix and transmitted data. The simulation results demonstrate that the performance of the proposed blind channel estimation method closely approaches that of the conventional pilot-aided methods in terms of the channel estimation error and symbol error rate (SER) of the detected signals even without the help of pilots.
Minimax Robust Landmine Detection Using Forward-Looking Ground-Penetrating Radar
Authors: Afief Dias Pambudi, Michael Fauß, Fauzia Ahmad, Abdelhak M. Zoubir
Date: 2021-11-16 00:00:00
Tasks: Landmine

We propose a robust likelihood-ratio test (LRT) to detect landmines and unexploded ordnance using a forward-looking ground-penetrating radar. Instead of modeling the distributions of the target and clutter returns with parametric families, we construct a band of feasible probability densities under each hypothesis. The LRT is then devised based on the least favorable densities within the bands. This detector is designed to maximize the worst-case performance over all feasible density pairs and, hence, does not require strong assumptions about the clutter and noise distributions. The proposed technique is evaluated using electromagnetic field simulation data of shallow-buried targets. We show that, compared to detectors based on parametric models, robust detectors can lead to significantly reduced false alarm rates, particularly in cases where there is a mismatch between the assumed model and the true distributions.
End-to-end Learning of a Constellation Shape Robust to Channel Condition Uncertainties
Authors: Ognjen Jovanovic, Metodi P. Yankov, Francesco Da Ros, Darko Zibar
Date: 2021-11-16 00:00:00

Vendor interoperability is one of the desired future characteristics of optical networks. This means that the transmission system needs to support a variety of hardware with different components, leading to system uncertainties throughout the network. For example, uncertainties in signal-to-noise ratio and laser linewidth can negatively affect the quality of transmission within an optical network due to e.g. mis-parametrization of the transceiver signal processing algorithms. In this paper, we propose to geometrically optimize a constellation shape that is robust to uncertainties in the channel conditions by utilizing end-to-end learning. In the optimization step, the channel model includes additive noise and residual phase noise. In the testing step, the channel model consists of laser phase noise, additive noise and blind phase search as the carrier phase recovery algorithm. Two noise models are considered for the additive noise: white Gaussian noise and nonlinear interference noise model for fiber nonlinearities. The latter models the behavior of an optical fiber channel more accurately because it considers the nonlinear effects of the optical fiber. For this model, the uncertainty in the signal-to-noise ratio can be divided between amplifier noise figures and launch power variations. For both noise models, our results indicate that the learned constellations are more robust to uncertainties in channel conditions compared to a standard constellation scheme such as quadrature amplitude modulation and standard geometric constellation shaping techniques.
Toward UL-DL Rate Balancing: Joint Resource Allocation and Hybrid-Mode Multiple Access for UAV-BS Assisted Communication Systems
Authors: Haiyong Zeng, Xu Zhu, Yufei Jiang, Zhongxiang Wei, Sumei Sun
Date: 2021-11-16 00:00:00

In this paper, we investigate unmanned aerial vehicle (UAV) assisted communication systems that require quasi-balanced data rates in uplink (UL) and downlink (DL), as well as users' heterogeneous traffic. To the best of our knowledge, this is the first work to explicitly investigate joint UL-DL optimization for UAV assisted systems under heterogeneous requirements. A hybrid-mode multiple access (HMMA) scheme is proposed toward heterogeneous traffic, where non-orthogonal multiple access (NOMA) targets high average data rate, while orthogonal multiple access (OMA) aims to meet users' instantaneous rate demands by compensating for their rates. HMMA enables a higher degree of freedom in multiple access and achieves a superior minimum average rate among users than the UAV assisted NOMA or OMA schemes. Under HMMA, a joint UL-DL resource allocation algorithm is proposed with a closed-form optimal solution for UL/DL power allocation to achieve quasi-balanced average rates for UL and DL. Furthermore, considering the error propagation in successive interference cancellation (SIC) of NOMA, an enhanced-HMMA scheme is proposed, which demonstrates high robustness against SIC error and a higher minimum average rate than the HMMA scheme.
An Unsupervised Deep Unfolding Framework for robust Symbol Level Precoding
Authors: Abdullahi Mohammad, Christos Masouros, Yiannis Andreopoulos
Date: 2021-11-15 00:00:00

Symbol Level Precoding (SLP) has attracted significant research interest due to its ability to exploit interference for energy-efficient transmission. This paper proposes an unsupervised deep-neural network (DNN) based SLP framework. Instead of naively training a DNN architecture for SLP without considering the specifics of the optimization objective of the SLP domain, our proposal unfolds a power minimization SLP formulation based on the interior point method (IPM) proximal `log' barrier function. Furthermore, we extend our proposal to a robust precoding design under channel state information (CSI) uncertainty. The results show that our proposed learning framework provides near-optimal performance while reducing the computational cost from O(n7.5) to O(n3) for the symmetrical system case where n = number of transmit antennas = number of users. This significant complexity reduction is also reflected in a proportional decrease in the proposed approach's execution time compared to the SLP optimization-based solution.
Learning-Based Symbol Level Precoding: A Memory-Efficient Unsupervised Learning Approach
Authors: Abdullahi Mohammad, Christos Masouros, Yiannis Andreopoulos
Date: 2021-11-15 00:00:00
Tasks: Model Compression

Symbol level precoding (SLP) has been proven to be an effective means of managing the interference in a multiuser downlink transmission and also enhancing the received signal power. This paper proposes an unsupervised learning based SLP that applies to quantized deep neural networks (DNNs). Rather than simply training a DNN in a supervised mode, our proposal unfolds a power minimization SLP formulation in an imperfect channel scenario using the interior point method (IPM) proximal `log' barrier function. We use binary and ternary quantizations to compress the DNN's weight values. The results show significant memory savings for our proposals compared to the existing full-precision SLP-DNet with significant model compression of ~21x and ~13x for both binary DNN-based SLP (RSLP-BDNet) and ternary DNN-based SLP (RSLP-TDNets), respectively.