pwc-archive/papers-with-abstracts huggingface dataset:
Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach with Safe Gradient Flow
Authors: Jie Feng, Wenqi Cui, Jorge Cortés, Yuanyuan Shi
Date: 2023-03-20 00:00:00
Tasks: Deep Reinforcement Learning

Deep reinforcement learning approaches are becoming appealing for the design of nonlinear controllers for voltage control problems, but the lack of stability guarantees hinders their deployment in real-world scenarios. This paper constructs a decentralized RL-based controller featuring two components: a transient control policy and a steady-state performance optimizer. The transient policy is parameterized as a neural network, and the steady-state optimizer represents the gradient of the long-term operating cost function. The two parts are synthesized through a safe gradient flow framework, which prevents the violation of reactive power capacity constraints. We prove that if the output of the transient controller is bounded and monotonically decreasing with respect to its input, then the closed-loop system is asymptotically stable and converges to the optimal steady-state solution. We demonstrate the effectiveness of our method by conducting experiments with IEEE 13-bus and 123-bus distribution system test feeders.
Parameterized Barrier Functions to Guarantee Safety under Uncertainty
Authors: Anil Alan, Tamas G. Molnar, Aaron D. Ames, Gábor Orosz
Date: 2023-03-20 00:00:00

Deploying safety-critical controllers in practice necessitates the ability to modulate uncertainties in control systems. In this context, robust control barrier functions -- in a variety of forms -- have been used to obtain safety guarantees for uncertain systems. Yet the differing types of uncertainty experienced in practice have resulted in a fractured landscape of robustification -- with a variety of instantiations depending on the structure of the uncertainty. This paper proposes a framework for generalizing these variations into a single form: parameterized barrier functions (PBFs), which yield safety guarantees for a wide spectrum of uncertainty types. This leads to controllers that enforce robust safety guarantees while their conservativeness scales by the parameterization. To illustrate the generality of this approach, we show that input-to-state safety (ISSf) is a special case of the PBF framework, whereby improved safety guarantees can be given relative to ISSf.
Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review
Authors: Matteo Barigozzi
Date: 2023-03-21 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We review Quasi Maximum Likelihood estimation of factor models for high-dimensional panels of time series. We consider two cases: (1) estimation when no dynamic model for the factors is specified (Bai and Li, 2012, 2016); (2) estimation based on the Kalman smoother and the Expectation Maximization algorithm thus allowing to model explicitly the factor dynamics (Doz et al., 2012, Barigozzi and Luciani, 2019). Our interest is in approximate factor models, i.e., when we allow for the idiosyncratic components to be mildly cross-sectionally, as well as serially, correlated. Although such setting apparently makes estimation harder, we show, in fact, that factor models do not suffer of the {\it curse of dimensionality} problem, but instead they enjoy a {\it blessing of dimensionality} property. In particular, given an approximate factor structure, if the cross-sectional dimension of the data, $N$, grows to infinity, we show that: (i) identification of the model is still possible, (ii) the mis-specification error due to the use of an exact factor model log-likelihood vanishes. Moreover, if we let also the sample size, $T$, grow to infinity, we can also consistently estimate all parameters of the model and make inference. The same is true for estimation of the latent factors which can be carried out by weighted least-squares, linear projection, or Kalman filtering/smoothing. We also compare the approaches presented with: Principal Component analysis and the classical, fixed $N$, exact Maximum Likelihood approach. We conclude with a discussion on efficiency of the considered estimators.
Using Forests in Multivariate Regression Discontinuity Designs
Authors: Yiqi Liu, Yuan Qi
Date: 2023-03-21 00:00:00
Tasks: regression

We discuss estimation and inference of conditional treatment effects in regression discontinuity (RD) designs with multiple scores. In addition to local linear regressions and the minimax-optimal estimator more recently proposed by Imbens and Wager (2019), we argue that two variants of random forests, honest regression forests and local linear forests, should be added to the toolkit of applied researchers working with multivariate RD designs; their validity follows from results in Wager and Athey (2018) and Friedberg et al. (2020). We design a systematic Monte Carlo study with data generating processes built both from functional forms that we specify and from Wasserstein Generative Adversarial Networks that closely mimic the observed data. We find no single estimator dominates across all specifications: (i) local linear regressions perform well in univariate settings, but the common practice of reducing multivariate scores to a univariate one can incur under-coverage, possibly due to vanishing density at the transformed cutoff; (ii) good performance of the minimax-optimal estimator depends on accurate estimation of a nuisance parameter and its current implementation only accepts up to two scores; (iii) forest-based estimators are not designed for estimation at boundary points and are susceptible to finite-sample bias, but their flexibility in modeling multivariate scores opens the door to a wide range of empirical applications, as illustrated by an empirical study of COVID-19 hospital funding with three eligibility criteria.
On the Existence and Information of Orthogonal Moments
Authors: Facundo Argañaraz, Juan Carlos Escanciano
Date: 2023-03-20 00:00:00

Locally Robust (LR)/Orthogonal/Debiased moments have proven useful with machine learning first steps, but their existence has not been investigated for general parameters. In this paper, we provide a necessary and sufficient condition, referred to as Restricted Local Non-surjectivity (RLN), for the existence of such orthogonal moments to conduct robust inference on general parameters of interest in regular semiparametric models. Importantly, RLN does not require either identification of the parameters of interest or the nuisance parameters. However, for orthogonal moments to be informative, the efficient Fisher Information matrix for the parameter must be non-zero (though possibly singular). Thus, orthogonal moments exist and are informative under more general conditions than previously recognized. We demonstrate the utility of our general results by characterizing orthogonal moments in a class of models with Unobserved Heterogeneity (UH). For this class of models our method delivers functional differencing as a special case. Orthogonality for general smooth functionals of the distribution of UH is also characterized. As a second major application, we investigate the existence of orthogonal moments and their relevance for models defined by moment restrictions with possibly different conditioning variables. We find orthogonal moments for the fully saturated two stage least squares, for heterogeneous parameters in treatment effects, for sample selection models, and for popular models of demand for differentiated products. We apply our results to the Oregon Health Experiment to study heterogeneous treatment effects of Medicaid on different health outcomes.
How Much Should We Trust Instrumental Variable Estimates in Political Science? Practical Advice Based on Over 60 Replicated Studies
Authors: Apoorva Lal, Mac Lockhart, Yiqing Xu, Ziwen Zu
Date: 2023-03-20 00:00:00

Instrumental variable (IV) strategies are widely used in political science to establish causal relationships. However, the identifying assumptions required by an IV design are demanding, and it remains challenging for researchers to assess their validity. In this paper, we replicate 67 papers published in three top journals in political science during 2010-2022 and identify several troubling patterns. First, researchers often overestimate the strength of their IVs due to non-i.i.d. errors, such as a clustering structure. Second, the most commonly used t-test for the two-stage-least-squares (2SLS) estimates often severely underestimates uncertainty. Using more robust inferential methods, we find that around 19-30% of the 2SLS estimates in our sample are underpowered. Third, in the majority of the replicated studies, the 2SLS estimates are much larger than the ordinary-least-squares estimates, and their ratio is negatively correlated with the strength of the IVs in studies where the IVs are not experimentally generated, suggesting potential violations of unconfoundedness or the exclusion restriction. To help researchers avoid these pitfalls, we provide a checklist for better practice.
Large-Scale Education Reform in General Equilibrium: Regression Discontinuity Evidence from India: Comment
Authors: David Roodman
Date: 2023-03-21 00:00:00
Tasks: Clustering, regression

This paper reanalyzes Khanna (2023), which studies labor market effects of schooling in India through regression discontinuity designs. Absent from the data are four districts close to the discontinuity; restoring them cuts the reduced-form impacts on schooling and log wages by 57% and 63%. Using regression-specific optimal band-widths and a robust variance estimator clustered at the geographic unit of treatment makes impacts statistically indistinguishable from 0. That finding is robust to varying the identifying threshold and the bandwidth. The estimates of general equilibrium effects and elasticities of substitution are not unbiased and have effectively infinite first and second moments.
Towards a Characterization of Random Serial Dictatorship
Authors: Felix Brandt, Matthias Greger, René Romen
Date: 2023-03-21 00:00:00

Random serial dictatorship (RSD) is a randomized assignment rule that - given a set of $n$ agents with strict preferences over $n$ houses - satisfies equal treatment of equals, ex post efficiency, and strategyproofness. For $n \le 3$, Bogomolnaia and Moulin (2001) have shown that RSD is characterized by these axioms. Extending this characterization to arbitrary $n$ is a long-standing open problem. By weakening ex post efficiency and strategyproofness, we reduce the question of whether RSD is characterized by these axioms for fixed $n$ to determining whether a matrix has rank $n^2 n!^n$. We provide computer-generated counterexamples to show that two other approaches for proving the characterization (using deterministic extreme points or restricted domains of preferences) are inadequate.
Non-Market Allocation Mechanisms: Optimal Design and Investment Incentives
Authors: Victor Augias, Eduardo Perez-Richet
Date: 2023-03-21 00:00:00

We study how to optimally design selection mechanisms, accounting for agents' investment incentives. A principal wishes to allocate a resource of homogeneous quality to a heterogeneous population of agents. The principal commits to a possibly random selection rule that depends on a one-dimensional characteristic of the agents she intrinsically values. Agents have a strict preference for being selected by the principal and may undertake a costly investment to improve their characteristic before it is revealed to the principal. We show that even if random selection rules foster agents' investments, especially at the top of the characteristic distribution, deterministic "pass-fail" selection rules are in fact optimal.
Both invariant principles implied by Marx's law of value are necessary and sufficient to solve the transformation problem through Morishima's formalism
Authors: Norbert Ankri, Païkan Marcaggi
Date: 2023-03-20 00:00:00

The unit value of a commodity that Michio Morishima's method and its variations enable to determine correctly, is the sum of the value of the commodities it contains (inputs) and the quantity of labor required for its production. However, goods are sold at their price of production only when they meet a solvent social need that involves the entire economy with its interconnections between the different industrial sectors. This condition gives full meaning to Marx's fundamental equalities, which constitute invariants that apply to the economy as a whole. These equalities are necessary to determine prices of production. We demonstrate that they also enable to solve the transformation problem by starting from Morishima's formalism and returning to a formalism closer to that used by Marx.
Housing Bubbles with Phase Transitions
Authors: Tomohiro Hirano, Alexis Akira Toda
Date: 2023-03-20 00:00:00

We analyze how equilibrium housing prices are determined in the process of economic development within an overlapping generations model with perfect housing and rental markets. We characterize the rent growth rate in all equilibria. The economy exhibits a two-stage phase transition: as incomes of home buyers rise, the equilibrium regime changes from fundamental to bubble possibility, where fundamental and bubbly equilibria coexist. With even higher incomes, fundamental equilibria disappear and housing bubbles become a necessity. We also discuss extensions and refinements such as equilibrium uniqueness, multiple savings vehicles, welfare implications, credit- and expectation-driven bubbles, and testable implications of our theory.
Extended Depth-of-Field Lensless Imaging using an Optimized Radial Mask
Authors: Jose Reinaldo da Cunha Santos Aroso Vieira da Silva Neto, Tomoya Nakamura, Yasushi Makihara, Yasushi Yagi
Date: 2023-03-21 00:00:00

The freedom of design of coded masks used by mask-based lensless cameras is an advantage these systems have when compared to lens-based ones. We leverage this freedom of design to propose a shape-preserving optimization scheme for a radial-type amplitude coded mask, used for extending the depth of field (DOF) of a lensless camera. Our goal is to identify the best parameters for the coded mask, while retaining its radial characteristics and therefore extended-DOF capabilities. We show that our optimized radial mask achieved better overall frequency response when compared to a naive implementation of a radial mask. We also quantitatively and qualitatively demonstrated the extended DOF imaging achieved by our optimized radial mask in simulations by comparing it to different non-radial coded masks. Finally, we built a prototype camera to validate the extended DOF capabilities of our coded mask in real scenarios.
Experimental Validation of Single BS 5G mmWave Positioning and Mapping for Intelligent Transport
Authors: Yu Ge, Hedieh Khosravi, Fan Jiang, Hui Chen, Simon Lindberg, Peter Hammarberg, Hyowon Kim, Oliver Brunnegård, Olof Eriksson, Bengt-Erik Olsson, Fredrik Tufvesson, Lennart Svensson, Henk Wymeersch
Date: 2023-03-21 00:00:00
Tasks: Diversity, parameter estimation, Position

Positioning with 5G signals generally requires connection to several base stations (BSs), which makes positioning more demanding in terms of infrastructure than communications. To address this issue, there have been several theoretical studies on single BS positioning, leveraging high-resolution angle and delay estimation and multipath exploitation possibilities at mmWave frequencies. This paper presents the first realistic experimental validation of such studies, involving a commercial 5G mmWave BS and a user equipment (UE) development kit mounted on a test vehicle. We present the relevant signal models, signal processing methods (including channel parameter estimation and position estimation), and validate these based on real data collected in an outdoor science park environment. Our results indicate that positioning is possible, but the performance with a single BS is limited by the knowledge of the position and orientation of the infrastructure and the multipath visibility and diversity.
Resource Allocation for Cell-Free Massive MIMO-aided URLLC Systems Relying on Pilot Sharing
Authors: Qihao Peng, Hong Ren, Mianxiong Dong, Maged Elkashlan, Kai-Kit Wong, Lajos Hanzo
Date: 2023-03-21 00:00:00

Resource allocation is conceived for cell-free (CF) massive multi-input multi-output (MIMO)-aided ultra-reliable and low latency communication (URLLC) systems. Specifically, to support multiple devices with limited pilot overhead, pilot reuse among the users is considered, where we formulate a joint pilot length and pilot allocation strategy for maximizing the number of devices admitted. Then, the pilot power and transmit power are jointly optimized while simultaneously satisfying the devices' decoding error probability, latency, and data rate requirements. Firstly, we derive the lower bounds (LBs) of ergodic data rate under finite channel blocklength (FCBL). Then, we propose a novel pilot assignment algorithm for maximizing the number of devices admitted. Based on the pilot allocation pattern advocated, the weighted sum rate (WSR) is maximized by jointly optimizing the pilot power and payload power. To tackle the resultant NP-hard problem, the original optimization problem is first simplified by sophisticated mathematical transformations, and then approximations are found for transforming the original problems into a series of subproblems in geometric programming (GP) forms that can be readily solved. Simulation results demonstrate that the proposed pilot allocation strategy is capable of significantly increasing the number of admitted devices and the proposed power allocation achieves substantial WSR performance gain.
Rethinking Estimation Rate for Wireless Sensing: A Rate-Distortion Perspective
Authors: Fuwang Dong, Fan Liu, Shihang Lu, Yifeng Xiong
Date: 2023-03-21 00:00:00

Wireless sensing has been recognized as a key enabling technology for numerous emerging applications. For decades, the sensing performance was mostly evaluated from a reliability perspective, with the efficiency aspect widely unexplored. Motivated from both backgrounds of rate-distortion theory and optimal sensing waveform design, a novel efficiency metric, namely, the sensing estimation rate (SER), is defined to unify the information- and estimation- theoretic perspectives of wireless sensing. Specifically, the active sensing process is characterized as a virtual lossy data transmission through non-cooperative joint source-channel coding. The bounds of SER are analyzed based on the data processing inequality, followed by a detailed derivation of achievable bounds under the special cases of the Gaussian linear model (GLM) and semi-controllable GLM. As for the intractable non-linear model, a computable upper bound is also given in terms of the Bayesian Cram\'er-Rao bound (BCRB). Finally, we show the rationality and effectiveness of the SER defined by comparing to the related works.
A novel implementation of symmetric boundary condition in harmonic and transient analysis of electromagnetic wave propagation
Authors: Durgarao Kamireddy, Sreekanth Karanam, Arup Nandy
Date: 2022-11-28 00:00:00

While doing electromagnetic analysis using FEM (Finite element method), if we can implement the underlying symmetric nature of the problem, there will be significant reduction in the computational cost. Symmetric nature of the problem can be identified from the given physical loading and boundary conditions of the problem. But for electromagnetic analysis in potential formulation, it is not very straight forward to implement the symmetric boundary condition. In the present work, a novel implementation of symmetric boundary condition in potential formulation within nodal framework, has been demonstrated. The implementation has been carried out in both electromagnetic harmonic and transient analysis for a wide range of radiation and scattering problems. A significant reduction in computational cost is achieved as compared to the existing formulation.
Detection in Human-sensor Systems under Quantum Prospect Theory using Bayesian Persuasion Frameworks
Authors: Yinan Hu, Quanyan Zhu
Date: 2023-03-21 00:00:00
Tasks: Human Detection

Human-sensor systems have a wide range of applications in fields such as robotics, healthcare, and finance. These systems utilize sensors to observe the true state of nature and generate strategically designed signals, aiding humans in making more accurate decisions regarding the state of nature. We adopt a Bayesian persuasion framework that is integrated with quantum prospect theories. In this framework, we develop a detection scheme where humans aim to determine the true state by observing the realization of quantum states from the sensor. We derive the optimal signaling rule for the sensor and the optimal decision rule for humans. We discover that this scenario violates the total law of probability. Furthermore, we examine how the concepts of rationality can influence the human detection performance and the signaling rules employed by the sensor.
DeepMA: End-to-end Deep Multiple Access for Wireless Image Transmission in Semantic Communication
Authors: Wenyu Zhang, Kaiyuan Bai, Sherali Zeadally, Haijun Zhang, Hua Shao, Hui Ma, Victor C. M. Leung
Date: 2023-03-21 00:00:00
Tasks: Decoder, Privacy Preserving, Semantic Communication

Semantic communication is a new paradigm that exploits deep learning models to enable end-to-end communications processes, and recent studies have shown that it can achieve better noise resiliency compared with traditional communication schemes in a low signal-to-noise (SNR) regime. To achieve multiple access in semantic communication, we propose a deep learning-based multiple access (DeepMA) method by training semantic communication models with the abilities of joint source-channel coding (JSCC) and orthogonal signal modulation. DeepMA is achieved by a DeepMA network (DMANet), which is comprised of several independent encoder-decoder pairs (EDPs), and the DeepMA encoders can encode the input data as mutually orthogonal semantic symbol vectors (SSVs) such that the DeepMA decoders can detect and recover their own target data from a received mixed SSV (MSSV) superposed by multiple SSV components transmitted from different encoders. We describe frameworks of DeepMA in wireless device-to-device (D2D), downlink, and uplink channel multiplexing scenarios, along with the training algorithm. We evaluate the performance of the proposed DeepMA in wireless image transmission tasks and compare its performance with the attention module-based deep JSCC (ADJSCC) method and conventional communication schemes using better portable graphics (BPG) and Low-density parity-check code (LDPC). The results obtained show that the proposed DeepMA can achieve effective, flexible, and privacy-preserving channel multiplexing process, and demonstrate that our proposed DeepMA approach can yield comparable bandwidth efficiency compared with conventional multiple access schemes.
Identifiable Solutions to Foreground Signature Extraction from Hyperspectral Images in an Intimate Mixing Scenario
Authors: Jarrod Hollis, Raviv Raich, Jinsub Kim, Barak Fishbain, Shai Kendler
Date: 2023-03-20 00:00:00

The problem of foreground material signature extraction in an intimate (nonlinear) mixing setting is considered. It is possible for a foreground material signature to appear in combination with multiple background material signatures. We explore a framework for foreground material signature extraction based on a patch model that accounts for such background variation. We identify data conditions under which a foreground material signature can be extracted up to scaling and elementwise-inverse variations. We present algorithms based on volume minimization and endpoint member identification to recover foreground material signatures under these conditions. Numerical experiments on real and synthetic data illustrate the efficacy of the proposed algorithms.