pwc-archive/papers-with-abstracts huggingface dataset:
Sequential Choices, Option Values, and the Returns to Education
Authors: Manudeep Bhuller, Philipp Eisenhauer, Moritz Mendel
Date: 2022-05-11 00:00:00

Using detailed Norwegian data on earnings, education and work histories, we estimate a dynamic structural model of education and sector choices that captures rich life-cycle patterns by ability. We validate the model against variation in education choices induced by a compulsory schooling reform. Our approach allows us to estimate the ex-ante returns to different education tracks across the life-cycle by individual ability and quantify the contribution of option values. We find substantial heterogeneity in returns and establish crucial roles for option values and re-enrollment in determining education choices and the impact of schooling policies.
An integrated debris environment assessment model
Authors: Akhil Rao, Francesca Letizia
Date: 2022-05-10 00:00:00
Tasks: counterfactual, model

Launch behaviors are a key determinant of the orbital environment. Physical and economic forces such as fragmentations and changing launch costs, or policies like post-mission disposal (PMD) compliance requirements, will alter the relative attractiveness of different orbits and lead operators to adjust their launch behaviors. However, integrating models of adaptive launch behavior with models of the debris environment remains an open challenge. We present a statistical framework for integrating theoretically-grounded models of launch behavior with evolutionary models of the low-Earth orbit (LEO) environment. We implement this framework using data on satellite launches, the orbital environment, launch vehicle prices, sectoral revenues, and government budgets over 2007-2020. The data are combined with a multi-shell and multi-species Particle-in-a-Box (PIB) model of the debris environment and a two-stage budgeting model of commercial, civil government, and defense decisions to allocate new launches across orbital shells. We demonstrate the framework's capabilities in three counterfactual scenarios: unexpected fragmentation events in highly-used regions, a sharp decrease in the cost of accessing lower parts of LEO, and increasing compliance with 25-year PMD guidelines. Substitution across orbits based on their evolving characteristics and the behavior of other operators induces notable changes in the debris environment relative to models without behavioral channels.
Evaluating Pest Management Strategies: A Robust Method and its Application to Strawberry Disease Management
Authors: Ariel Soto-Caro, Feng Wu, Zhengfei Guan, Natalia Peres
Date: 2019-08-05 00:00:00
Tasks: Management, quantile regression

Farmers use pesticides to reduce yield losses. The efficacies of pesticide treatments are often evaluated by analyzing the average treatment effects and risks. The stochastic efficiency with respect to a function is often employed in such evaluations through ranking the certainty equivalents of each treatment. The main challenge of using this method is gathering an adequate number of observations to produce results with statistical power. However, in many cases, only a limited number of trials are replicated in field experiments, leaving an inadequate number of observations. In addition, this method focuses only on the farmer's profit without incorporating the impact of disease pressure on yield and profit. The objective of our study is to propose a methodology to address the issue of an insufficient number of observations using simulations and take into account the effect of disease pressure on yield through a quantile regression model. We apply this method to the case of strawberry disease management in Florida.
Two-sided matching with firms' complementary preferences
Authors: Chao Huang
Date: 2022-05-11 00:00:00
Tasks: Vocal Bursts Valence Prediction

This paper studies two-sided many-to-one matching in which firms have complementary preferences. We show that stable matchings exist under a balancedness condition that rules out a specific type of odd-length cycles formed by firms' acceptable sets. We also provide a class of preference profiles that satisfy this condition. Our results indicate that stable matching is compatible with a wide range of firms' complementary preferences.
The Limits of Limited Commitment
Authors: Jacopo Bizzotto, Toomas Hinnosaar, Adrien Vigier
Date: 2022-05-11 00:00:00

We study limited strategic leadership. A collection of subsets covering the leader's action space determines her commitment opportunities. We characterize the outcomes resulting from all possible commitment structures of this kind. If the commitment structure is an interval partition, then the leader's payoff is bounded by her Stackelberg and Cournot payoffs. Under general commitment structures the leader may obtain a payoff that is less than her lowest Cournot payoff. We apply our results to a textbook duopoly model and elicit the commitment structures leading to consumer- and producer-optimal outcomes.
Wasserstein Image Local Analysis: Histogram of Orientations, Smoothing and Edge Detection
Authors: Jiening Zhu, Harini Veeraraghavan, Larry Norton, Joseph O. Deasy, Allen Tannenbaum
Date: 2022-05-11 00:00:00
Tasks: Edge Detection

The Histogram of Oriented Gradient is a widely used image feature, which describes local image directionality based on numerical differentiation. Due to its ill-posed nature, small noise may lead to large errors. Conventional HOG may fail to produce meaningful directionality results in the presence of noise, which is common in medical radiographic imaging. We approach the directionality problem from a novel perspective by the use of the optimal transport map of a local image patch to a uni-color patch of its mean. We decompose the transport map into sub-work costs in different directions. We evaluated the ability of the optimal transport to quantify tumor heterogeneity from brain MRI images of patients with glioblastoma multiforme from the TCIA. By considering the entropy difference of the extracted local directionality within tumor regions, we found that patients with higher entropy in their images, had statistically significant worse overall survival (p $=0.008$), which indicates that tumors exhibiting flows in many directions may be more malignant, perhaps reflecting high tumor histologic grade, a reflection of histologic disorganization. We also explored the possibility of solving classical image processing problems such as smoothing and edge detection via optimal transport. By looking for a 2-color patch with minimum transport distance to a local patch, we derive a nonlinear shock filter, which preserves edges. Moreover, we found that the color difference of the computed 2-color patch indicates whether there is a large change in color, i.e., an edge in the given patch. In summary, we expand the usefulness of optimal transport as an image local analysis tool, to extract robust measures of imaging tumor heterogeneity for outcomes prediction as well as image pre-processing. Because of its robust nature, we find it offers several advantages over the classical approaches.
A Memory-Efficient Dynamic Image Reconstruction Method using Neural Fields
Authors: Luke Lozenski, Mark A. Anastasio, Umberto Villa
Date: 2022-05-11 00:00:00
Tasks: Image Reconstruction, Object

Dynamic imaging is essential for analyzing various biological systems and behaviors but faces two main challenges: data incompleteness and computational burden. For many imaging systems, high frame rates and short acquisition times require severe undersampling, which leads to data incompleteness. Multiple images may then be compatible with the data, thus requiring special techniques (regularization) to ensure the uniqueness of the reconstruction. Computational and memory requirements are particularly burdensome for three-dimensional dynamic imaging applications requiring high resolution in both space and time. Exploiting redundancies in the object's spatiotemporal features is key to addressing both challenges. This contribution investigates neural fields, or implicit neural representations, to model the sought-after dynamic object. Neural fields are a particular class of neural networks that represent the dynamic object as a continuous function of space and time, thus avoiding the burden of storing a full resolution image at each time frame. Neural field representation thus reduces the image reconstruction problem to estimating the network parameters via a nonlinear optimization problem (training). Once trained, the neural field can be evaluated at arbitrary locations in space and time, allowing for high-resolution rendering of the object. Key advantages of the proposed approach are that neural fields automatically learn and exploit redundancies in the sought-after object to both regularize the reconstruction and significantly reduce memory storage requirements. The feasibility of the proposed framework is illustrated with an application to dynamic image reconstruction from severely undersampled circular Radon transform data.
Design and manufacturing of an optimized retro reflective marker for photogrammetric pose estimation in ITER
Authors: Laura Goncalves Ribeiro, Olli J. Suominen, Philip Bates, Sari Peltonen, Emilio Ruiz Morales, Atanas Gotchev
Date: 2022-05-11 00:00:00
Tasks: Pose Estimation

Retro reflective markers can remarkably aid photogrammetry tasks in challenging visual environments. They have been demonstrated to be key enablers of pose estimation for remote handling in ITER. However, the strict requirements of the ITER environment have previously markedly constrained the design of such elements and limited their performance. In this work, we identify several retro reflector designs based on the cat's eye principle that are applicable to the ITER usecase and propose a methodology for optimizing their performance. We circumvent some of the environmental constraints by changing the curvature radius and distance to the reflective surface. We model, manufacture and test a marker that fulfils all the application requirements while achieving a gain of around 100\% in performance over the previous solution in the targeted working range.
Limited-memory BFGS Optimisation of Phase-Only Computer-Generated Hologram for Fraunhofer Diffraction
Authors: Jinze Sha, Andrew Kadis, Fan Yang, Timothy D. Wilkinson
Date: 2022-05-10 00:00:00

We implement a novel limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimisation algorithm with cross entropy (CE) loss function, to produce phase-only computer-generated hologram (CGH) for holographic displays, with validation on a binary-phase modulation holographic projector.
Joint OAM Radar-Communication Systems: Target Recognition and Beam Optimization
Authors: Wen-Xuan Long, Rui Chen, Marco Moretti, Wei zhang, Jiandong Li
Date: 2022-05-11 00:00:00
Tasks: Position, Super-Resolution

Orbital angular momentum (OAM) radars are able to estimate the azimuth angle and the rotation velocity of multiple targets without relative motion or beam scanning. Moreover, OAM wireless communications can achieve high spectral efficiency (SE) by utilizing a set of information-bearing modes on the same frequency channel. Benefitting from the above advantages, in this paper, we design a novel radar-centric joint OAM radar-communication (RadCom) scheme based on uniform circular arrays (UCAs), which modulates information signals on the existing OAM radar waveform. In details, we first propose an OAM-based three-dimensional (3-D) super-resolution position estimation and rotation velocity detection method, which can accurately estimate the 3-D position and rotation velocity of multiple targets. Then, we derive the posterior Cramer-Rao bound (PCRB) of the OAM-based estimates and, finally, we analyze the transmission rate of the integrated communication system. To achieve the best trade-off between imaging and communication, the transmitted integrated OAM beams are optimized by means of an exhaustive search method. Both mathematical analysis and simulation results show that the proposed radar-centric joint OAM RadCom scheme can accurately estimate the 3-D position and rotation velocity of multiple targets while ensuring the transmission rate of the communication receiver, which can be regarded as an effective supplement to existing joint RadCom schemes.
Parametrization and Estimation of High-Rank Line-of-Sight MIMO Channels with Reflected Paths
Authors: Yaqi Hu, Mingsheng Yin, Sundeep Rangan, Marco Mezzavilla
Date: 2022-05-11 00:00:00
Tasks: valid

High-rank line-of-sight (LOS) MIMO systems have attracted considerable attention for millimeter wave and THz communications. The small wavelengths in these frequencies enable spatial multiplexing with massive data rates at long distances. Such systems are also being considered for multi-path non-LOS (NLOS) environments. In these scenarios, standard channel models based on plane waves cannot capture the curvature of each wave front necessary to model spatial multiplexing. This work presents a novel and simple multi-path wireless channel parametrization where each path is replaced by a LOS path with a reflected image source. The model fully is valid for all paths with specular planar reflections and captures the spherical nature of each wave front. Importantly, it is shown that the model uses only two additional parameters relative to the standard plane wave model. Moreover, the parameters can be easily captured in standard ray tracing. The accuracy of the approach is demonstrated on detailed ray tracing simulations at 28 GHz and 140 GHz in a dense urban area.
Experimental Validation of Sequence-Wise Predistorter for Evaluation of Geometrically Shaped 128-QAM
Authors: Menno van den Hout, Sjoerd van der Heide, Sebastiaan Goossens, Chigo Okonkwo
Date: 2022-05-11 00:00:00

A predistorter for transmitter nonlinearities is applied to the evaluation of a geometrically shaped constellation, such that constellation points are transmitted correctly during the evaluation of the geometrically shaped constellation.
Data-Driven Optimal Sensor Placement for High-Dimensional System Using Annealing Machine
Authors: Tomoki Inoue, Tsubasa Ikami, Yasuhiro Egami, Hiroki Nagai, Yasuo Naganuma, Koichi Kimura, Yu Matsuda
Date: 2022-05-11 00:00:00
Tasks: Optical Flow Estimation

We propose a novel method for solving optimal sensor placement problem for high-dimensional system using an annealing machine. The sensor points are calculated as a maximum clique problem of the graph, the edge weight of which is determined by the proper orthogonal decomposition (POD) mode obtained from data based on the fact that a high-dimensional system usually has a low-dimensional representation. Since the maximum clique problem is equivalent to the independent set problem of the complement graph, the independent set problem is solved using Fujitsu Digital Annealer. As a demonstration of the proposed method, the pressure distribution induced by the K\'arm\'an vortex street behind a square cylinder is reconstructed based on the pressure data at the calculated sensor points. The pressure distribution is measured by pressure-sensitive paint (PSP) technique, which is an optical flow diagnose method. The root mean square errors (RMSEs) between the pressure measured by pressure transducer and the reconstructed pressures (calculated from the proposed method and an existing greedy method) at the same place are compared. As the result, the similar RMSE is achieved by the proposed method using approximately 1/5 number of sensor points obtained by the existing method. This method is of great importance as a novel approach for optimal sensor placement problem and a new engineering application of an annealing machine.
Acceleration-guided Acoustic Signal Denoising Framework Based on Learnable Wavelet Transform Applied to Slab Track Condition Monitoring
Authors: Baorui Dai, Gaëtan Frusque, Qi Li, Olga Fink
Date: 2022-05-11 00:00:00
Tasks: Denoising

Acoustic monitoring has recently shown great potential in the diagnosis of infrastructure condition. However, due to the severe noise interference in acoustic signals, meaningful features tend to be difficult to infer. It creates a considerable obstacle for an extensive application of acoustic monitoring. To tackle this problem, we propose an acceleration-guided acoustic signal denoising framework (AG-ASDF) based on learnable wavelet transform to automatically denoise the acoustic signal and extract the relevant features based on the acceleration signal. This denoising framework requires the acceleration signal only for the training stage. Therefore, only acoustic sensors (non-intrusive) need to be installed during the application phase, which is convenient and crucial for the condition monitoring of safety-critical infrastructure. A comparative study is conducted among the proposed AG-ASDF and other feature learning / extraction methods, by using a multi-class support vector machine to evaluate the detection effectiveness of slab track condition based on acoustic signals. Different healthy and unhealthy states of slab tracks are imitated with three types of slab track supporting conditions in a railway test line. The classification based on the proposed AG-ASDF features outperforms other feature extraction and learning methods with a significant accuracy improvement.
Empirical Fading Model and Bayesian Calibration for Multipath-Enhanced Device-Free Localization
Authors: Martin Schmidhammer, Christian Gentner, Michael Walter, Stephan Sand, Benjamin Siebler, Uwe-Carsten Fiebig
Date: 2022-05-11 00:00:00

The performance of multipath-enhanced device-free localization severely depends on the information about the propagation paths within the network. While known for the line-of-sight, the propagation paths have yet to be determined for multipath components. This work provides a novel Bayesian calibration approach for determining the propagation paths by estimating reflection points. Therefore, first a statistical fading model is presented, that describes user-induced changes in the received signal of multipath components. The model is derived and validated empirically using an extensive set of wideband and ultra-wideband measurement data. Second, the Bayesian approach is presented, which, based on the derived empirical fading model, relates measured changes in the power of a multipath component to the location of the reflection point. Exploiting the geometric properties of multipath components caused by single-bounce reflections, the solution space of possible locations of reflection points is constrained to the delay ellipse. Thus, a one-dimensional elliptic estimation problem can be formulated, which is solved using a point mass filter. The applicability of the proposed approach is demonstrated and evaluated based on measurement data. Independent of the underlying measurement system, the Bayesian calibration approach is shown to robustly estimate the locations of the reflection points in different environments.
A Unified Theory of Adaptive Subspace Detection. Part II: Numerical Examples
Authors: Pia Addabbo, Danilo Orlando, Giuseppe Ricci, Louis L. Scharf
Date: 2022-05-11 00:00:00

This paper is devoted to the performance analysis of the detectors proposed in the companion paper where a comprehensive design framework is presented for the adaptive detection of subspace signals. The framework addresses four variations on subspace detection: the subspace may be known or known only by its dimension; consecutive visits to the subspace may be unconstrained or they may be constrained by a prior probability distribution. In this paper, Monte Carlo simulations are used to compare the generalized likelihood ratio (GLR) detectors derived in [1] with estimate-and-plug (EP) approximations of the GLR detectors. Remarkably, the EP approximations appear here for the first time (at least to the best of the authors' knowledge). The numerical examples indicate that GLR detectors are effective for the detection of partially-known signals affected by inherent uncertainties due to the system or the operating environment. In particular, if the signal subspace is known, GLR detectors tend to outperform EP detectors. If, instead, the signal subspace is known only by its dimension, the performance of GLR and EP detectors is very similar.
Intelligent Reflecting Surface Configurations for Smart Radio Using Deep Reinforcement Learning
Authors: Wei Wang, Wei zhang
Date: 2022-05-11 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Intelligent reflecting surface (IRS) is envisioned to change the paradigm of wireless communications from "adapting to wireless channels" to "changing wireless channels". However, current IRS configuration schemes, consisting of sub-channel estimation and passive beamforming in sequence, conform to the conventional model-based design philosophies and are difficult to be realized practically in the complex radio environment. To create the smart radio environment, we propose a model-free design of IRS control that is independent of the sub-channel channel state information (CSI) and requires the minimum interaction between IRS and the wireless communication system. We firstly model the control of IRS as a Markov decision process (MDP) and apply deep reinforcement learning (DRL) to perform real-time coarse phase control of IRS. Then, we apply extremum seeking control (ESC) as the fine phase control of IRS. Finally, by updating the frame structure, we integrate DRL and ESC in the model-free control of IRS to improve its adaptivity to different channel dynamics. Numerical results show the superiority of our proposed joint DRL and ESC scheme and verify its effectiveness in model-free IRS control without sub-channel CSI.
Frequency-domain digital predistortion for Massive MU-MIMO-OFDM Downlink
Authors: Yibo Wu, Ulf Gustavsson, Mikko Valkama, Alexandre Graell i Amat, Henk Wymeersch
Date: 2022-05-10 00:00:00

Digital predistortion (DPD) is a method commonly used to compensate for the nonlinear effects of power amplifiers (PAs). However, the computational complexity of most DPD algorithms becomes an issue in the downlink of massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM), where potentially up to several hundreds of PAs in the base station (BS) require linearization. In this paper, we propose a convolutional neural network (CNN)-based DPD in the frequency domain, taking place before the precoding, where the dimensionality of the signal space depends on the number of users, instead of the number of BS antennas. Simulation results on generalized memory polynomial (GMP)-based PAs show that the proposed CNN-based DPD can lead to very large complexity savings as the number of BS antenna increases at the expense of a small increase in power to achieve the same symbol error rate (SER).