pwc-archive/papers-with-abstracts huggingface dataset:
Why Do Students Lie and Should We Worry? An Analysis of Non-truthful Reporting
Authors: Emil Chrisander, Andreas Bjerre-Nielsen
Date: 2023-02-27 00:00:00
Tasks: Fairness

A core aspect in market design is to encourage participants to truthfully report their preferences to ensure efficiency and fairness. Our research paper analyzes the factors that contribute to and the consequences of students reporting non-truthfully in admissions applications. We survey college applicants in Denmark about their perceptions of the admission process and personality to examine recent theories of misreporting preferences. Our analysis reveals that omissions in reports are largely driven by students' pessimistic beliefs about their chances of admission. Moreover, such erroneous beliefs largely account for whether an omission led to a missed opportunity for admission. However, the low frequency of these errors suggests that most non-truthful reports are "white lies" with minimal negative impact. We find a novel role of personality and individual circumstances that co-determine the extent of omissions. We also find that estimates of students' demand are biased if it is assumed that students report truthfully, and demonstrate that this bias can be reduced by making a less restrictive assumption. Our results have implications for the modeling of preferences, information acquisition, and subjective admission beliefs in strategy-proof mechanisms
Accounting for Cross-Location Technological Heterogeneity in the Measurement of Operations Efficiency and Productivity
Authors: Emir Malikov, Jingfang Zhang, Shunan Zhao, Subal C. Kumbhakar
Date: 2023-02-26 00:00:00

Motivated by the long-standing interest in understanding the role of location for firm performance, this paper provides a semiparametric methodology to accommodate locational heterogeneity in production analysis. Our approach is novel in that we explicitly model spatial variation in parameters in the production-function estimation. We accomplish this by allowing both the input-elasticity and productivity parameters to be unknown functions of the firm's geographic location and estimate them via local kernel methods. This allows the production technology to vary across space, thereby accommodating neighborhood influences on firm production. In doing so, we are also able to examine the role of cross-location differences in explaining the variation in operational productivity among firms. Our model is superior to the alternative spatial production-function formulations because it (i) explicitly estimates the cross-locational variation in production functions, (ii) is readily reconcilable with the conventional production axioms and, more importantly, (iii) can be identified from the data by building on the popular proxy-variable methods, which we extend to incorporate locational heterogeneity. Using our methodology, we study China's chemicals manufacturing industry and find that differences in technology (as opposed to in idiosyncratic firm heterogeneity) are the main source of the cross-location differential in total productivity in this industry.
A System Approach to Structural Identification of Production Functions with Multi-Dimensional Productivity
Authors: Emir Malikov, Shunan Zhao, Jingfang Zhang
Date: 2023-02-26 00:00:00

There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This paper extends Gandhi et al.'s (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, our model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, we achieve point identification by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. We also show how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.
Detecting Learning by Exporting and from Exporters
Authors: Jingfang Zhang, Emir Malikov
Date: 2023-02-26 00:00:00

Existing literature at the nexus of firm productivity and export behavior mostly focuses on "learning by exporting," whereby firms can improve their performance by engaging in exports. Whereas, the secondary channel of learning via cross-firm spillovers from exporting peers, or "learning from exporters," has largely been neglected. Omitting this important mechanism, which can benefit both exporters and non-exporters, may provide an incomplete assessment of the total productivity benefits of exporting. In this paper, we develop a unified empirical framework for productivity measurement that explicitly accommodates both channels. To do this, we formalize the evolution of firm productivity as an export-controlled process, allowing future productivity to be affected by both the firm's own export behavior as well as export behavior of spatially proximate, same-industry peers. This facilitates a simultaneous, "internally consistent" identification of firm productivity and the corresponding effects of exporting. We apply our methodology to a panel of manufacturing plants in Chile in 1995-2007 and find significant evidence in support of both direct and spillover effects of exporting that substantially boost the productivity of domestic firms.
Price Discovery for Derivatives
Authors: Christian Keller, Michael C. Tseng
Date: 2023-02-26 00:00:00

We consider price discovery across derivative markets in a general framework where an agent has private information regarding state probabilities and trades state-contingent claims. In an equivalent options formulation, the informed agent has private information regarding arbitrary aspects of an underlying asset's payoff distribution and trades option portfolios. We characterize the informed demand, price impact, and information efficiency of prices. The informed demand formula prescribes option strategies for trading on any given aspect of the underlying payoff, thereby rationalizing and extending those used in practice for e.g. volatility trading.
The Contribution of Tourism in National Economies: Evidence of Greece
Authors: Olga Kalantzi, Dimitrios Tsiotas, Serafeim Polyzos
Date: 2023-02-25 00:00:00

Greece constitutes a coastal country with a lot of geomorphologic, climatic, cultural and historic peculiarities favoring the development of many aspects of tourism. Within this framework, this article examines what are the effects of tourism in Greece and how determinative these effects are, by applying a macroscopic analysis on empirical data for the estimation of the contribution of tourism in the Greek Economy. The available data regard records of the Balance of Payments in Greece and of the major components of the Balance of the Invisible Revenues, where a measurable aspect of tourism, the Travel or Tourism Exchange, is included. At the time period of the available data (2000-2012) two events of the recent Greek history are distinguished as the most significant (the Olympic Games in the year 2004 and the economic crisis initiated in the year 2009) and their impact on the diachronic evolution in the tourism is discussed. Under an overall assessment, the analysis illustrated that tourism is a sector of the Greek economy, which is described by a significant resilience, but it seems that it has not yet been submitted to an effective developmental plan exploiting the endogenous tourism dynamics of the country, suggesting currently a promising investment of low risk for the economic growth of country and the exit of the economic crisis
Informality, Education-Occupation Mismatch, and Wages: Evidence from India
Authors: Shweta Bahl, Ajay Sharma
Date: 2023-02-25 00:00:00

This article examines the intertwining relationship between informality and education-occupation mismatch and the consequent impact on wages. In particular, we discuss two issues: first, the relative importance of informality and education-occupation mismatch in determining wages, and second, the relevance of EOM for formal and informal workers. The analysis reveals that although both informality and EOM are significant determinants of wages, the former is more crucial for a developing country like India. Further, we find that EOM is one of the crucial determinants of wages for formal workers, but it is not critical for informal workers. The study highlights the need for considering the bifurcation of formal-informal workers to understand the complete dynamics of EOM, especially for developing countries where informality is predominant.
Blackwell-Monotone Updating Rules
Authors: Mark Whitmeyer
Date: 2023-02-27 00:00:00

An updating rule specifies how an agent reacts to information. An updating rule is Blackwell monotone if more information is always better for an agent in a decision problem and strictly Blackwell monotone if, in addition, there is always a decision problem in which more information is strictly better for an agent. Bayes' law is strictly Blackwell monotone, and I show that within a broad class of updating rules--those that distort the Bayesian posteriors in a signal-independent manner--it is the only strictly Blackwell-monotone updating rule. Moreover, when the state is non-binary, I show that Bayes' law and the trivial updating rule in which an agent dogmatically holds a single belief are the only continuous Blackwell-monotone updating rules.
A rational measure of irrationality
Authors: Davide Carpentiere, Alfio Giarlotta, Stephen Watson
Date: 2023-02-27 00:00:00

All possible types of deterministic choice behavior are classified by their degree of irrationality. This classification is performed in three steps: (1) select a benchmark of rationality, for which this degree is zero; (2) endow the set of choices with a metric to measure deviations from rationality; and (3) compute the distance of any choice behavior from the selected benchmark. The natural candidate for step 1 is the family of all rationalizable behaviors. A possible candidate for step 2 is a suitable variation of the metric described by Klamler (2008), which displays a sharp discerning power among different types of choice behaviors. In step 3 we use this new metric to establish the minimum distance of any choice behavior from the benchmark of rationality. Finally we describe a measure of stochastic irrationality, which employs the random utility model as a benchmark of rationality, and the Block-Marschak polynomials to measure deviations from it.
Complex Clipping for Improved Generalization in Machine Learning
Authors: Les Atlas, Nicholas Rasmussen, Felix Schwock, Mert Pilanci
Date: 2023-02-27 00:00:00

For many machine learning applications, a common input representation is a spectrogram. The underlying representation for a spectrogram is a short time Fourier transform (STFT) which gives complex values. The spectrogram uses the magnitude of these complex values, a commonly used detector. Modern machine learning systems are commonly overparameterized, where possible ill-conditioning problems are ameliorated by regularization. The common use of rectified linear unit (ReLU) activation functions between layers of a deep net has been shown to help this regularization, improving system performance. We extend this idea of ReLU activation to detection for the complex STFT, providing a simple-to-compute modified and regularized spectrogram, which potentially results in better behaved training. We then confirmed the benefit of this approach on a noisy acoustic data set used for a real-world application. Generalization performance improved substantially. This approach might benefit other applications which use time-frequency mappings, for acoustic, audio, and other applications.
DFSNet: A Steerable Neural Beamformer Invariant to Microphone Array Configuration for Real-Time, Low-Latency Speech Enhancement
Authors: Anton Kovalyov, Kashyap Patel, Issa Panahi
Date: 2023-02-26 00:00:00
Tasks: Attribute, Speech Enhancement

Invariance to microphone array configuration is a rare attribute in neural beamformers. Filter-and-sum (FS) methods in this class define the target signal with respect to a reference channel. However, this not only complicates formulation in reverberant conditions but also the network, which must have a mechanism to infer what the reference channel is. To address these issues, this study presents Delay Filter-and-Sum Network (DFSNet), a steerable neural beamformer invariant to microphone number and array geometry for causal speech enhancement. In DFSNet, acquired signals are first steered toward the speech source direction prior to the FS operation, which simplifies the task into the estimation of delay-and-summed reverberant clean speech. The proposed model is designed to incur low latency, distortion, and memory and computational burden, giving rise to high potential in hearing aid applications. Simulation results reveal comparable performance to noncausal state-of-the-art.
I-MSV 2022: Indic-Multilingual and Multi-sensor Speaker Verification Challenge
Authors: Jagabandhu Mishra, Mrinmoy Bhattacharjee, S. R. Mahadeva Prasanna
Date: 2023-02-26 00:00:00
Tasks: Speaker Verification

Speaker Verification (SV) is a task to verify the claimed identity of the claimant using his/her voice sample. Though there exists an ample amount of research in SV technologies, the development concerning a multilingual conversation is limited. In a country like India, almost all the speakers are polyglot in nature. Consequently, the development of a Multilingual SV (MSV) system on the data collected in the Indian scenario is more challenging. With this motivation, the Indic- Multilingual Speaker Verification (I-MSV) Challenge 2022 has been designed for understanding and comparing the state-of-the-art SV techniques. For the challenge, approximately $100$ hours of data spoken by $100$ speakers has been collected using $5$ different sensors in $13$ Indian languages. The data is divided into development, training, and testing sets and has been made publicly available for further research. The goal of this challenge is to make the SV system robust to language and sensor variations between enrollment and testing. In the challenge, participants were asked to develop the SV system in two scenarios, viz. constrained and unconstrained. The best system in the constrained and unconstrained scenario achieved a performance of $2.12\%$ and $0.26\%$ in terms of Equal Error Rate (EER), respectively.
Autonomous Polycrystalline Material Decomposition for Hyperspectral Neutron Tomography
Authors: Mohammad Samin Nur Chowdhury, Diyu Yang, Shimin Tang, Singanallur V. Venkatakrishnan, Hassina Z. Bilheux, Gregery T. Buzzard, Charles A. Bouman
Date: 2023-02-27 00:00:00

Hyperspectral neutron tomography is an effective method for analyzing crystalline material samples with complex compositions in a non-destructive manner. Since the counts in the hyperspectral neutron radiographs directly depend on the neutron cross-sections, materials may exhibit contrasting neutron responses across wavelengths. Therefore, it is possible to extract the unique signatures associated with each material and use them to separate the crystalline phases simultaneously. We introduce an autonomous material decomposition (AMD) algorithm to automatically characterize and localize polycrystalline structures using Bragg edges with contrasting neutron responses from hyperspectral data. The algorithm estimates the linear attenuation coefficient spectra from the measured radiographs and then uses these spectra to perform polycrystalline material decomposition and reconstructs 3D material volumes to localize materials in the spatial domain. Our results demonstrate that the method can accurately estimate both the linear attenuation coefficient spectra and associated reconstructions on both simulated and experimental neutron data.
Saliency-Driven Hierarchical Learned Image Coding for Machines
Authors: Kristian Fischer, Fabian Brand, Christian Blum, André Kaup
Date: 2023-02-27 00:00:00
Tasks: All, Decoder, Image Compression, object-detection, Object Detection

We propose to employ a saliency-driven hierarchical neural image compression network for a machine-to-machine communication scenario following the compress-then-analyze paradigm. By that, different areas of the image are coded at different qualities depending on whether salient objects are located in the corresponding area. Areas without saliency are transmitted in latent spaces of lower spatial resolution in order to reduce the bitrate. The saliency information is explicitly derived from the detections of an object detection network. Furthermore, we propose to add saliency information to the training process in order to further specialize the different latent spaces. All in all, our hierarchical model with all proposed optimizations achieves 77.1 % bitrate savings over the latest video coding standard VVC on the Cityscapes dataset and with Mask R-CNN as analysis network at the decoder side. Thereby, it also outperforms traditional, non-hierarchical compression networks.
X-ray Spectral Estimation using Dictionary Learning
Authors: Wenrui Li, Venkatesh Sridhar, K. Aditya Mohan, Saransh Singh, Jean-Baptiste Forien, Xin Liu, Gregery T. Buzzard, Charles A. Bouman
Date: 2023-02-27 00:00:00
Tasks: Computed Tomography (CT), Dictionary Learning

As computational tools for X-ray computed tomography (CT) become more quantitatively accurate, knowledge of the source-detector spectral response is critical for quantitative system-independent reconstruction and material characterization capabilities. Directly measuring the spectral response of a CT system is hard, which motivates spectral estimation using transmission data obtained from a collection of known homogeneous objects. However, the associated inverse problem is ill-conditioned, making accurate estimation of the spectrum challenging, particularly in the absence of a close initial guess. In this paper, we describe a dictionary-based spectral estimation method that yields accurate results without the need for any initial estimate of the spectral response. Our method utilizes a MAP estimation framework that combines a physics-based forward model along with an $L_0$ sparsity constraint and a simplex constraint on the dictionary coefficients. Our method uses a greedy support selection method and a new pair-wise iterated coordinate descent method to compute the above estimate. We demonstrate that our dictionary-based method outperforms a state-of-the-art method as shown in a cross-validation experiment on four real datasets collected at beamline 8.3.2 of the Advanced Light Source (ALS).
Extended Version of "New Theory and Faster Computations for Subspace-Based Sensitivity Map Estimation in Multichannel MRI''
Authors: Rodrigo A. Lobos, Chin-Cheng Chan, Justin P. Haldar
Date: 2023-02-26 00:00:00
Tasks: Sensitivity

This is an unabridged version of a journal manuscript that has been submitted for publication [1]. (Due to length restrictions, we were forced to remove substantial amounts of content from the version that was submitted to the journal, including more detailed theoretical explanations, additional figures, and a more comprehensive bibliography. This content remains intact in this version of the document). Sensitivity map estimation is important in many multichannel MRI applications. Subspace-based sensitivity map estimation methods like ESPIRiT are popular and perform well, though can be computationally expensive and their theoretical principles can be nontrivial to understand. In the first part of this work, we present a novel theoretical derivation of subspace-based sensitivity map estimation based on a linear-predictability/structured low-rank modeling perspective. This results in an estimation approach that is equivalent to ESPIRiT, but with distinct theory that may be more intuitive for some readers. In the second part of this work, we propose and evaluate a set of computational acceleration approaches (collectively known as PISCO) that can enable substantial improvements in computation time (up to ~100x in the examples we show) and memory for subspace-based sensitivity map estimation.
Self-Supervised and Supervised Deep Learning for PET Image Reconstruction
Authors: Andrew J. Reader
Date: 2023-02-25 00:00:00
Tasks: Deep Learning, Image Reconstruction

A unified self-supervised and supervised deep learning framework for PET image reconstruction is presented, including deep-learned filtered backprojection (DL-FBP) for sinograms, deep-learned backproject then filter (DL-BPF) for backprojected images, and a more general mapping using a deep network in both the sinogram and image domains (DL-FBP-F). The framework allows varying amounts and types of training data, from the case of having only one single dataset to reconstruct through to the case of having numerous measured datasets, which may or may not be paired with high-quality references. For purely self-supervised mappings, no reference or ground truth data are needed. The self-supervised deep-learned reconstruction operators all use a conventional image reconstruction objective within the loss function (e.g. maximum Poisson likelihood, maximum a posteriori). If it is desired for the reconstruction networks to generalise (i.e. to need either no or minimal retraining for a new measured dataset, but to be fast, ready to reuse), then these self-supervised networks show potential even when previously trained from just one single dataset. For any given new measured dataset, finetuning is however often necessary, and of course the initial training set should ideally go beyond just one dataset if a generalisable network is sought. Example results for the purely self-supervised single-dataset case are shown, but the networks can be i) trained uniquely for any measured dataset to reconstruct from, ii) pretrained on multiple datasets and then used with no retraining for new measured data, iii) pretrained and then finetuned for new measured data, iv) optionally trained with high-quality references. The framework, with its optional inclusion of supervised learning, provides a spectrum of reconstruction approaches by making use of whatever (if any) training data quantities and types are available.