pwc-archive/papers-with-abstracts huggingface dataset:
Minimizing Sensitivity to Model Misspecification
Authors: Stéphane Bonhomme, Martin Weidner
Date: 2018-07-05 00:00:00
Tasks: model, Sensitivity

We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model, based on one-step adjustments. In addition, we provide confidence intervals that contain the true parameter under local misspecification. As a tool to interpret the degree of misspecification, we map it to the local power of a specification test of the reference model. Our approach allows for systematic sensitivity analysis when the parameter of interest may be partially or irregularly identified. As illustrations, we study three applications: an empirical analysis of the impact of conditional cash transfers in Mexico where misspecification stems from the presence of stigma effects of the program, a cross-sectional binary choice model where the error distribution is misspecified, and a dynamic panel data binary choice model where the number of time periods is small and the distribution of individual effects is misspecified.
Crime and Mismeasured Punishment: Marginal Treatment Effect with Misclassification
Authors: Vitor Possebom
Date: 2021-05-29 00:00:00

I partially identify the marginal treatment effect (MTE) when the treatment is misclassified. I explore two restrictions, allowing for dependence between the instrument and the misclassification decision. If the signs of the derivatives of the propensity scores are equal, I identify the MTE sign. If those derivatives are similar, I bound the MTE. To illustrate, I analyze the impact of alternative sentences (fines and community service v. no punishment) on recidivism in Brazil, where Appeals processes generate misclassification. The estimated misclassification bias may be as large as 10% of the largest possible MTE, and the bounds contain the correctly estimated MTE.
Difference-in-Differences Estimators of Intertemporal Treatment Effects
Authors: Clément de Chaisemartin, Xavier D'Haultfoeuille
Date: 2020-07-08 00:00:00
Tasks: Form

We study treatment-effect estimation, with a panel where groups may experience multiple changes of their treatment dose. We make parallel trends assumptions, but do not restrict treatment effect heterogeneity, unlike the linear regressions that have been used in such designs. We extend the event-study approach for binary-and-staggered treatments, by redefining the event as the first time a group's treatment changes. This yields an event-study graph, with reduced-form estimates of the effect of having been exposed to a weakly higher amount of treatment for $\ell$ periods. We show that the reduced-form estimates can be combined into an economically interpretable cost-benefit ratio.
The causal effect of political power on the provision of public education: Evidence from a weighted voting system
Authors: Lindgren Erik, Per Pettersson-Lidbom, Bjorn Tyrefors
Date: 2021-06-01 00:00:00

In this paper, we estimate the causal effect of political power on the provision of public education. We use data from a historical nondemocratic society with a weighted voting system where eligible voters received votes in proportion to their taxable income and without any limit on the maximum of votes, i.e., the political system used in Swedish local governments during the period 1862-1909. We use a novel identification strategy where we combine two different identification strategies, i.e., a threshold regression analysis and a generalized event-study design, both of which exploit nonlinearities or discontinuities in the effect of political power between two opposing local elites: agricultural landowners and emerging industrialists. The results suggest that school spending is approximately 90-120% higher if the nonagrarian interest controls all of the votes compared to when landowners have more than a majority of votes. Moreover, we find no evidence that the concentration of landownership affected this relationship
The Causal Effect of Transport Infrastructure: Evidence from a New Historical Database
Authors: Lindgren Erik, Per Pettersson-Lidbom, Bjorn Tyrefors
Date: 2021-06-01 00:00:00

In this paper, we analyze the effect of transport infrastructure investments in railways. As a testing ground, we use data from a new historical database that includes annual panel data on approximately 2,400 Swedish rural geographical areas during the period 1860-1917. We use a staggered event study design that is robust to treatment effect heterogeneity. Importantly, we find extremely large reduced-form effects of having access to railways. For real nonagricultural income, the cumulative treatment effect is approximately 120% after 30 years. Equally important, we also show that our reduced-form effect is likely to reflect growth rather than a reorganization of existing economic activity since we find no spillover effects between treated and untreated regions. Specifically, our results are consistent with the big push hypothesis, which argues that simultaneous/coordinated investment, such as large infrastructure investment in railways, can generate economic growth if there are strong aggregate demand externalities (e.g., Murphy et al. 1989). We used plant-level data to further corroborate this mechanism. Indeed, we find that investments in local railways dramatically, and independent of initial conditions, increase local industrial production and employment on the order of 100-300% across almost all industrial sectors.
Cash versus Kind: Benchmarking a Child Nutrition Program against Unconditional Cash Transfers in Rwanda
Authors: Craig McIntosh, Andrew Zeitlin
Date: 2021-06-01 00:00:00
Tasks: Benchmarking, Diversity, Nutrition

We benchmark a multi-dimensional child nutrition intervention against an unconditional cash transfer of equal cost. Randomized variation in transfer amounts allows us to estimate impacts of cash transfers at expenditure levels equivalent to the in-kind program, as well as to estimate the return to increasing cash transfer values. While neither the in-kind program nor a cost-equivalent transfer costing \$124 per household moves core child outcomes within a year, cash transfers create significantly greater consumption than the in-kind alternative. A larger cash transfer costing \$517 substantially improves consumption and investment outcomes and drives modest improvements in dietary diversity and child growth.
The Green Management Towards a Green Industrial Revolution
Authors: Malgorzata Rutkowska, Adam Sulich
Date: 2021-05-28 00:00:00
Tasks: Management

Green Management (GM) is now one of many methods proposed to achieve new, more ecological, and sustainable economic models. The paper is focused on the impact of the developing human population on the environment measured by researched variables. Anthropopressure can have both a positive and a negative dimension. This paper aims to present an econometric model of the Green Industrial Revolution (GIR) impact on the Labour Market. The GIR is similar to the Fourth Industrial Revolution (FIR) and takes place as the next stage in the development of humanity in the perception of both machines and devices and the natural environment. The processes of the GIR in the European Union can be identified based on selected indicators of Sustainable Development (SD), in particular with the use of indicators of the Green Economy (GE) using taxonomic methods and regression analysis. The GM strives to implement the idea of the SD in many areas, to transform the whole economy, and elements of this process are visible Green Labour Market (GLM). The adopted direction of economic development depends on the as-sumptions of strategic management, which can be defined, for example, with green management, which is mainly manifested in the creation of green jobs.
Decision Towards Green Careers and Sustainable Development
Authors: Adam Sulich, Malgorzata Rutkowska, Uma Shankar Singh
Date: 2021-05-28 00:00:00

The graduates careers are the most spectacular and visible outcome of excellent university education. This is also important for the university performance assessment when its graduates can easily find jobs in the labor market. The information about graduates matching their qualifications and fields of studies versus undertaken employment, creates an important set of data for future students and employers to be analyzed. Additionally, there is business environment pressure to transform workplaces and whole organizations towards a more green and sustainable form. Green Jobs (GJ) are the elements of the whole economic transformation. This change is based on the green qualifications and green careers which translate theoretical assumptions into business language. Therefore, the choice of future career path is based on specified criteria, which were examined by surveys performed among graduates by the career office at Wroclaw University of Technology (WUT) in Poland. The aim of this article was to address the question about the most significant criteria of green career paths among graduates of WUT in 2019. Special attention was paid to the GJ understood as green careers. In this article, the multi-criteria Bellinger method was explained, presented, and then used to analyze chosen factors of choice graduates career paths and then compared with Gale-Shapley algorithm results in a comparative analysis. Future research can develop a graduate profile willing to be employed in GJ.
Modeling the out-of-equilibrium dynamics of bounded rationality and economic constraints
Authors: Oliver Richters
Date: 2021-06-01 00:00:00

The analogies between economics and classical mechanics can be extended from constrained optimization to constrained dynamics by formalizing economic (constraint) forces and economic power in analogy to physical (constraint) forces in Lagrangian mechanics. In the differential-algebraic equation framework of General Constrained Dynamics (GCD), households, firms, banks, and the government employ forces to change economic variables according to their desire and their power to assert their interest. These ex-ante forces are completed by constraint forces from unanticipated system constraints to yield the ex-post dynamics. The flexible out-of-equilibrium model can combine Keynesian concepts such as the balance sheet approach and slow adaptation of prices and quantities with bounded rationality (gradient climbing) and interacting agents discussed in behavioral economics and agent-based models. The framework integrates some elements of different schools of thought and overcomes some restrictions inherent to optimization approaches, such as the assumption of markets operating in or close to equilibrium. Depending on the parameter choice for power relations and adaptation speeds, the model nevertheless can converge to a neoclassical equilibrium, and reacts to an austerity shock in a neoclassical or post-Keynesian way.
Robust Aggregation of Correlated Information
Authors: Henrique de Oliveira, Yuhta Ishii, Xiao Lin
Date: 2021-05-31 00:00:00

An agent makes decisions based on multiple sources of information. In isolation, each source is well understood, but their correlation is unknown. We study the agent's robustly optimal strategies -- those that give the best possible guaranteed payoff, even under the worst possible correlation. With two states and two actions, we show that a robustly optimal strategy uses a single information source, ignoring all others. In general decision problems, robustly optimal strategies combine multiple sources of information, but the number of information sources that are needed has a bound that only depends on the decision problem. These findings provide a new rationale for why information is ignored.
Single-Pixel Compressive Imaging in Shift-Invariant Spaces via Exact Wavelet Frames
Authors: Tin Vlašić, Damir Seršić
Date: 2021-06-01 00:00:00
Tasks: Compressive Sensing

This paper introduces a novel framework for single-pixel imaging via compressive sensing (CS) in shift-invariant (SI) spaces by exploiting the sparsity property of a wavelet representation. We reinterpret the acquisition procedure of a single-pixel camera as filtering of the observed signal with continuous-domain functions that lie in an SI subspace spanned by the integer shifts of the box function. The signal is modeled by an arbitrary SI generator whose special case is the box function, which, as we show in the paper, is conventionally used in single-pixel imaging. We propose to use separable B-spline generators which are intuitively complemented by sparsity-inducing spline wavelets. The SI models of the acquisition and the underlying signal lead to an exact discretization of an inherently continuous-domain inverse problem to a finite-dimensional problem of CS type. By solving the CS optimization problem, a parametric representation of the signal is obtained. Such a representation offers many practical advantages in image processing applications. We propose an efficient matrix-free implementation of the framework and conduct it on the standard test images and real-world measurement data. Experimental results show that the proposed framework achieves a significant improvement of the reconstruction quality relative to the conventional discretization in CS setups. MATLAB implementation of the method described in this paper has been made publicly available on https://github.com/retiro/compressive_imaging_in_si_spaces.
Image Restoration by Combined Order Regularization with Optimal Spatial Adaptation
Authors: Sanjay Viswanath, Simon de Beco, Maxime Dahan, Muthuvel Arigovindan
Date: 2019-03-07 00:00:00
Tasks: Image Restoration, MRI Reconstruction

Total Variation (TV) and related extensions have been popular in image restoration due to their robust performance and wide applicability. While the original formulation is still relevant after two decades of extensive research, its extensions that combine derivatives of first- and second-order are now being explored for better performance, with examples being Combined Order TV (COTV) and Total Generalized Variation (TGV). As an improvement over such multi-order convex formulations, we propose a novel non-convex regularization functional which adaptively combines Hessian-Schatten (HS) norm and first order TV (TV1) functionals with spatially varying weight. This adaptive weight itself is controlled by another regularization term; the total cost becomes the sum of this adaptively weighted HS-TV1 term, the regularization term for the adaptive weight, and the data-fitting term. The reconstruction is obtained by jointly minimizing w.r.t. the required image and the adaptive weight. We construct a block coordinate descent method for this minimization with proof of convergence, which alternates between minimization w.r.t. the required image and the adaptive weights. We derive exact computational formula for minimization w.r.t. the adaptive weight, and construct an ADMM algorithm for minimization w.r.t. to the required image. We compare the proposed method using image recovery examples including MRI reconstruction and microscopy deconvolution.
Regularization by Adversarial Learning for Ultrasound Elasticity Imaging
Authors: Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Date: 2021-06-01 00:00:00
Tasks: Generative Adversarial Network

Classical model-based imaging methods for ultrasound elasticity inverse problem require prior constraints about the underlying elasticity patterns, while finding the appropriate hand-crafted prior for each tissue type is a challenge. In contrast, standard data-driven methods count solely on supervised learning on the training data pairs leading to massive network parameters for unnecessary physical model relearning which might not be consistent with the governing physical models of the imaging system. Fusing the physical forward model and noise statistics with data-adaptive priors leads to a united reconstruction framework that guarantees the learned reconstruction agrees with the physical models while coping with the limited training data. In this paper, we propose a new methodology for estimating the elasticity image by solving a regularized optimization problem which benefits from the physics-based modeling via a data-fidelity term and adversarially learned priors via a regularization term. In this method, the regularizer is trained based on the Wasserstein Generative Adversarial Network (WGAN) objective function which tries to distinguish the distribution of clean and noisy images. Leveraging such an adversarial regularizer for parameterizing the distribution of latent images and using gradient descent (GD) for solving the corresponding regularized optimization task leads to stability and convergence of the reconstruction compared to pixel-wise supervised learning schemes. Our simulation results verify the effectiveness and robustness of the proposed methodology with limited training datasets.
High Resolution Time-Frequency Generation with Generative Adversarial Networks
Authors: Zeynel Deprem, A. Enis Çetin
Date: 2021-06-01 00:00:00
Tasks: Generative Adversarial Network, Vocal Bursts Intensity Prediction

Signal representation in Time-Frequency (TF) domain is valuable in many applications including radar imaging and inverse synthetic aparture radar. TF representation allows us to identify signal components or features in a mixed time and frequency plane. There are several well-known tools, such as Wigner-Ville Distribution (WVD), Short-Time Fourier Transform (STFT) and various other variants for such a purpose. The main requirement for a TF representation tool is to give a high-resolution view of the signal such that the signal components or features are identifiable. A commonly used method is the reassignment process which reduces the cross-terms by artificially moving smoothed WVD values from their actual location to the center of the gravity for that region. In this article, we propose a novel reassignment method using the Conditional Generative Adversarial Network (CGAN). We train a CGAN to perform the reassignment process. Through examples, it is shown that the method generates high-resolution TF representations which are better than the current reassignment methods.
End-to-end Learning of a Constellation Shape Robust to Variations in SNR and Laser Linewidth
Authors: Ognjen Jovanovic, Metodi P. Yankov, Francesco Da Ros, Darko Zibar
Date: 2021-06-01 00:00:00

We propose an autoencoder-based geometric shaping that learns a constellation robust to SNR and laser linewidth estimation errors. This constellation maintains shaping gain in mutual information (up to 0.3 bits/symbol) with respect to QAM over various SNR and laser linewidth values.
Time Division Multiplexing: From a Co-Prime Sampling Point of View
Authors: Usham V. Dias
Date: 2021-06-01 00:00:00

Co-prime sampling is a strategy for acquiring the signal below the Nyquist rate. The prototype and extended co-prime samplers require two low rate sub-samplers. One of the sub-samplers in the extended co-prime scheme is not utilized for every alternate co-prime period. Therefore, this paper proposes a time multiplexing strategy to utilize the vacant slots. It describes the deviation from the existing theory. Closed-form expressions for weight functions are provided. Acquisition of two signals is described with three samplers as well as two samplers. A generalized structure is also proposed with an extremely sparse co-prime sampling strategy.
Robust multi-sensor Generalized Labeled Multi-Bernoulli filter
Authors: Cong-Thanh Do, Tran Thien Dat Nguyen, Hoa Van Nguyen
Date: 2021-06-01 00:00:00

This paper proposes an efficient and robust algorithm to estimate target trajectories with unknown target detection profiles and clutter rates using measurements from multiple sensors. In particular, we propose to combine the multi-sensor Generalized Labeled Multi-Bernoulli (MS-GLMB) filter to estimate target trajectories and robust Cardinalized Probability Hypothesis Density (CPHD) filters to estimate the clutter rates. The target detection probability is augmented to the filtering state space for joint estimation. Experimental results show that the proposed robust filter exhibits near-optimal performance in the sense that it is comparable to the optimal MS-GLMB operating with true clutter rate and detection probability. More importantly, it outperforms other studied filters when the detection profile and clutter rate are unknown and time-variant. This is attributed to the ability of the robust filter to learn the background parameters on-the-fly.
AIRIS: Artificial Intelligence Enhanced Signal Processing in Reconfigurable Intelligent Surface Communications
Authors: Shun Zhang, Muye Li, Mengnan Jian, Yajun Zhao, Feifei Gao
Date: 2021-06-01 00:00:00
Tasks: Scheduling

Reconfigurable intelligent surface (RIS) is an emerging meta-surface that can provide additional communications links through reflecting the signals, and has been recognized as a strong candidate of 6G mobile communications systems. Meanwhile, it has been recently admitted that implementing artificial intelligence (AI) into RIS communications will extensively benefit the reconfiguration capacity and enhance the robustness to complicated transmission environments. Besides the conventional model-driven approaches, AI can also deal with the existing signal processing problems in a data-driven manner via digging the inherent characteristic from the real data. Hence, AI is particularly suitable for the signal processing problems over RIS networks under unideal scenarios like modeling mismatching, insufficient resource, hardware impairment, as well as dynamical transmissions. As one of the earliest survey papers, we will introduce the merging of AI and RIS, called AIRIS, over various signal processing topics, including environmental sensing, channel acquisition, beamforming design, and resource scheduling, etc. We will also discuss the challenges of AIRIS and present some interesting future directions.
A Schottky-Diode-Based Wake-Up Receiver for IoT Applications
Authors: Mahmoud Elhebeary, Samer Hanna, Sudhakar Pamarti, Danijela Cabric, Chih-Kong Ken Yang
Date: 2021-05-31 00:00:00

This paper presents an always-on low-power wake-up receiver (WuRx) that activates the remainder of the system when a wake-up signal is detected. The proposed receiver has two phases of waking up. The first phase uses an integrated CMOS Schottky diodes to detect the signal power at a low bias current. The approach dissipates low quiescent power and allows the reuse of the design in multiple frequency bands with only modifying the matching network. In the second phase, a data-locked startable oscillator is proposed to correlate the received data with a target signature. This design eliminates the area and power dissipation of an external crystal oscillator and only turns on when the second phase is activated. By correlating to a target signature, the second phase also reduces the probability of a false alarm (PFA) that would otherwise wake up the high-power bulk of the system. The two-phase approach leads to significant reduction in average power consumption when compared to a single-phase design. This implementation targets sub-ms wake-up latency and operates in the unlicensed band at a 750-MHz carrier frequency with a data rate of 200 kbps. The design achieves $\sim$8.45pJ/bit and $<$-50 dBm of input sensitivity and average power of 1.69$\mu$W. The system is implemented in 65-nm CMOS technology and occupies an area of 1mm$\times$0.75mm.