pwc-archive/papers-with-abstracts huggingface dataset:
Quantum-Enhanced Detection of Viral cDNA via Luminescence Resonance Energy Transfer Using Upconversion and Gold Nanoparticles
Authors: Shahriar Esmaeili, Navid Rajil, Ayla Hazrathosseini, Benjamin W. Neuman, Masfer H. Alkahtani, Dipankar Sen, Qiang Hu, Hung-Jen Wu, Zhenhuan Yi, Robert W. Brick, Alexei V. Sokolov, Philip R. Hemmer, Marlan O. Scully
Date: 2024-10-14 00:00:00
Tasks: Diagnostic, Sensitivity, Specificity

The COVID-19 pandemic has profoundly impacted global economies and healthcare systems, revealing critical vulnerabilities in both. In response, our study introduces a groundbreaking method for the detection of SARS-CoV-2 cDNA, leveraging Luminescence resonance energy transfer (LRET) between upconversion nanoparticles (UCNPs) and gold nanoparticles (AuNPs) to achieve an unprecedented detection limit of 242 femtomolar (fM). This innovative sensing platform utilizes UCNPs conjugated with one primer and AuNPs with another, targeting the 5' and 3' ends of the SARS-CoV-2 cDNA, respectively, enabling precise differentiation of mismatched DNA sequences and significantly enhancing detection specificity. Through rigorous experimental analysis, we established a quenching efficiency range from 10.4\% to 73.6\%, with an optimal midpoint of 42\%, thereby demonstrating the superior sensitivity of our method. By comparing the quenching efficiency of mismatched DNAs to the target DNA, we identified an optimal DNA:UCNP:AuNP ratio that ensures accurate detection. Our comparative analysis with existing SARS-CoV-2 detection methods revealed that our approach not only provides a lower detection limit but also offers higher specificity and potential for rapid, on-site testing. This study demonstrates the superior sensitivity and specificity of using UCNPs and AuNPs for SARS-CoV-2 cDNA detection, offering a significant advancement in rapid, accessible diagnostic technologies. Our method, characterized by its low detection limit and high precision, represents a critical step forward in managing current and future viral outbreaks, contributing to the enhancement of global healthcare responsiveness and infectious disease control.
In-vivo high-resolution χ-separation at 7T
Authors: Jiye Kim, Minjun Kim, Sooyeon Ji, Kyeongseon Min, Hwihun Jeong, Hyeong-Geol Shin, Chungseok Oh, Sina Straub, Seong-Gi Kim, Jongho Lee
Date: 2024-10-16 00:00:00

A recently introduced quantitative susceptibility mapping (QSM) technique, $\chi$-separation, offers the capability to separate paramagnetic ($\chi_{\text{para}}$) and diamagnetic ($\chi_{\text{dia}}$) susceptibility distribution within the brain. In-vivo high-resolution mapping of iron and myelin distribution, estimated by $\chi$-separation, could provide a deeper understanding of brain substructures, assisting the investigation of their functions and alterations. This can be achieved using 7T MRI, which benefits from a high signal-to-noise ratio and susceptibility effects. However, applying $\chi$-separation at 7T presents difficulties due to the requirement of an $R_2$ map, coupled with issues such as high specific absorption rate (SAR), large $B_1$ transmit field inhomogeneities, and prolonged scan time. To address these challenges, we developed a novel deep neural network, R2PRIMEnet7T, designed to convert a 7T $R_2^*$ map into a 3T $R_2'$ map. Building on this development, we present a new pipeline for $\chi$-separation at 7T, enabling us to generate high-resolution $\chi$-separation maps from multi-echo gradient-echo data. The proposed method is compared with alternative pipelines, such as an end-to-end network and linearly-scaled $R_2'$, and is validated against $\chi$-separation maps at 3T, demonstrating its accuracy. The 7T $\chi$-separation maps generated by the proposed method exhibit similar contrasts to those from 3T, while 7T high-resolution maps offer enhanced clarity and detail. Quantitative analysis confirms that the proposed method surpasses the alternative pipelines. The proposed method results well delineate the detailed brain structures associated with iron and myelin. This new pipeline holds promise for analyzing iron and myelin concentration changes in various neurodegenerative diseases through precise structural examination.
Monitoring Drug-Induced Brain Activity Changes with Functional Ultrasound Imaging and Convolutional Neural Networks
Authors: Jared Deighton, Shan Zhong, Kofi Agyeman, Wooseong Choi, Charles Liu, Darrin Lee, Vasileios Maroulas, Vasileios Christopoulos
Date: 2024-10-12 00:00:00
Tasks: Hippocampus

Functional ultrasound imaging (fUSI) is a cutting-edge technology that measures changes in cerebral blood volume (CBV) by detecting backscattered echoes from red blood cells moving within its field of view (FOV). It offers high spatiotemporal resolution and sensitivity, allowing for detailed visualization of cerebral blood flow dynamics. While fUSI has been utilized in preclinical drug development studies to explore the mechanisms of action of various drugs targeting the central nervous system, many of these studies have primarily focused on predetermined regions of interest (ROIs). This focus may overlook relevant brain activity outside these specific areas, which could influence the results. To address this limitation, we combined convolutional neural networks (CNNs) with fUSI to comprehensively understand the pharmacokinetic process of Dizocilpine, also known as MK-801, a drug that blocks the N-Methyl-D-aspartate (NMDA) receptor in the central nervous system. CNN and class activation mapping (CAM) revealed the spatiotemporal effects of MK-801, which originated in the cortex and propagated to the hippocampus, demonstrating the ability to detect dynamic drug effects over time. Additionally, CNN and CAM assessed the impact of anesthesia on the spatiotemporal hemodynamics of the brain, revealing no distinct patterns between early and late stages. The integration of fUSI and CNN provides a powerful tool to gain insights into the spatiotemporal dynamics of drug action in the brain. This combination enables a comprehensive and unbiased assessment of drug effects on brain function, potentially accelerating the development of new therapies in neuropharmacological studies.
Design of Fiber-Longitudinal Optical Power Monitor
Authors: Takeo Sasai, Sze Yun Set, Shinji Yamashita
Date: 2024-10-16 00:00:00

This paper presents analytical results on the accuracy of fiber-longitudinal optical power monitoring (LPM) at arbitrary positions. To quantify the accuracy, the position-wise variance and power-profile SNR of LPM are defined and analyzed, yielding formulas for these metrics. Using these metrics, we show that various designs and performance predictions of LPM for a given link and estimation conditions are possible in a unified manner. Specifically, the required SNR to detect a given loss event is first presented. Based on this relation, the design parameters of LPM, such as the sample size and optical power required to detect the loss, are explicitly determined. The performance such as the detectable limit of loss events at individual positions and maximum dynamic range are also specified. These results can be used as a basis for establishing a design principle of LPM.
Edge-based Modeling for Disease Transmission on Random Graphs: An Application to Mitigate a Syphilis Outbreak
Authors: S. Zhao, S. Saeed, M. Carter, B. Stoner, M. Hoover, H. Guan, F. M. G. Magpantay
Date: 2024-10-16 00:00:00

Edge-based network models, especially those based on bond percolation methods, can be used to model disease transmission on complex networks and accommodate social heterogeneity while keeping tractability. Here we present an application of an edge-based network model to the spread of syphilis in the Kingston, Frontenac and Lennox & Addington (KFL&A) region of Southeastern Ontario, Canada. We compared the results of using a network-based susceptible-infectious-recovered (SIR) model to those generated from using a traditional mass action SIR model. We found that the network model yields very different predictions, including a much lower estimate of the final epidemic size. We also used the network model to estimate the potential impact of introducing a rapid syphilis point of care test (POCT) and treatment intervention strategy that has recently been implemented by the public health unit to mitigate syphilis transmission.
The Subtlety of Optimal Paternalism in a Population with Bounded Rationality
Authors: Charles F. Manski, Eytan Sheshinski
Date: 2024-10-17 00:00:00
Tasks: Decision Making

We consider a utilitarian planner with the power to design a discrete choice set for a heterogeneous population with bounded rationality. We find that optimal paternalism is subtle. The policy that most effectively constrains or influences choices depends on the preference distribution of the population and on the choice probabilities conditional on preferences that measure the suboptimality of behavior. We first consider the planning problem in abstraction. We next examine policy choice when individuals measure utility with additive random error and maximize mismeasured rather than actual utility. We then analyze a class of problems of binary treatment choice under uncertainty. Here we suppose that a planner can mandate a treatment conditional on publicly observed personal covariates or can decentralize decision making, enabling persons to choose their own treatments. Bounded rationality may take the form of deviations between subjective personal beliefs and objective probabilities of uncertain outcomes. We apply our analysis to clinical decision making in medicine. Having documented that optimization of paternalism requires the planner to possess extensive knowledge that is rarely available, we address the difficult problem of paternalistic policy choice when the planner is boundedly rational.
A Simple Interactive Fixed Effects Estimator for Short Panels
Authors: Robert F. Phillips, Benjamin D. Williams
Date: 2024-10-16 00:00:00

We study the interactive effects (IE) model as an extension of the conventional additive effects (AE) model. For the AE model, the fixed effects estimator can be obtained by applying least squares to a regression that adds a linear projection of the fixed effect on the explanatory variables (Mundlak, 1978; Chamberlain, 1984). In this paper, we develop a novel estimator -- the projection-based IE (PIE) estimator -- for the IE model that is based on a similar approach. We show that, for the IE model, fixed effects estimators that have appeared in the literature are not equivalent to our PIE estimator, though both can be expressed as a generalized within estimator. Unlike the fixed effects estimators for the IE model, the PIE estimator is consistent for a fixed number of time periods with no restrictions on serial correlation or conditional heteroskedasticity in the errors. We also derive a statistic for testing the consistency of the two-way fixed effects estimator in the possible presence of iterative effects. Moreover, although the PIE estimator is the solution to a high-dimensional nonlinear least squares problem, we show that it can be computed by iterating between two steps, both of which have simple analytical solutions. The computational simplicity is an important advantage relative to other strategies that have been proposed for estimating the IE model for short panels. Finally, we compare the finite sample performance of IE estimators through simulations.
Testing Identifying Assumptions in Parametric Separable Models: A Conditional Moment Inequality Approach
Authors: Leonard Goff, Désiré Kédagni, Huan Wu
Date: 2024-10-15 00:00:00

In this paper, we propose a simple method for testing identifying assumptions in parametric separable models, namely treatment exogeneity, instrument validity, and/or homoskedasticity. We show that the testable implications can be written in the intersection bounds framework, which is easy to implement using the inference method proposed in Chernozhukov, Lee, and Rosen (2013), and the Stata package of Chernozhukov et al. (2015). Monte Carlo simulations confirm that our test is consistent and controls size. We use our proposed method to test the validity of some commonly used instrumental variables, such as the average price in other markets in Nevo and Rosen (2012), the Bartik instrument in Card (2009), and the test rejects both instrumental variable models. When the identifying assumptions are rejected, we discuss solutions that allow researchers to identify some causal parameters of interest after relaxing functional form assumptions. We show that the IV model is nontestable if no functional form assumption is made on the outcome equation, when there exists a one-to-one mapping between the continuous treatment variable, the instrument, and the first-stage unobserved heterogeneity.
Aggregation Trees
Authors: Riccardo Di Francesco
Date: 2024-10-15 00:00:00
Tasks: valid

Uncovering the heterogeneous effects of particular policies or "treatments" is a key concern for researchers and policymakers. A common approach is to report average treatment effects across subgroups based on observable covariates. However, the choice of subgroups is crucial as it poses the risk of $p$-hacking and requires balancing interpretability with granularity. This paper proposes a nonparametric approach to construct heterogeneous subgroups. The approach enables a flexible exploration of the trade-off between interpretability and the discovery of more granular heterogeneity by constructing a sequence of nested groupings, each with an optimality property. By integrating our approach with "honesty" and debiased machine learning, we provide valid inference about the average treatment effect of each group. We validate the proposed methodology through an empirical Monte-Carlo study and apply it to revisit the impact of maternal smoking on birth weight, revealing systematic heterogeneity driven by parental and birth-related characteristics.
Closed-form estimation and inference for panels with attrition and refreshment samples
Authors: Grigory Franguridi, Lidia Kosenkova
Date: 2024-10-15 00:00:00
Tasks: Form

It has long been established that, if a panel dataset suffers from attrition, auxiliary (refreshment) sampling restores full identification under additional assumptions that still allow for nontrivial attrition mechanisms. Such identification results rely on implausible assumptions about the attrition process or lead to theoretically and computationally challenging estimation procedures. We propose an alternative identifying assumption that, despite its nonparametric nature, suggests a simple estimation algorithm based on a transformation of the empirical cumulative distribution function of the data. This estimation procedure requires neither tuning parameters nor optimization in the first step, i.e. has a closed form. We prove that our estimator is consistent and asymptotically normal and demonstrate its good performance in simulations. We provide an empirical illustration with income data from the Understanding America Study.
Large Scale Longitudinal Experiments: Estimation and Inference
Authors: Apoorva Lal, Alexander Fischer, Matthew Wardrop
Date: 2024-10-13 00:00:00
Tasks: Computational Efficiency

Large-scale randomized experiments are seldom analyzed using panel regression methods because of computational challenges arising from the presence of millions of nuisance parameters. We leverage Mundlak's insight that unit intercepts can be eliminated by using carefully chosen averages of the regressors to rewrite several common estimators in a form that is amenable to weighted-least squares estimation with frequency weights. This renders regressions involving arbitrary strata intercepts tractable with very large datasets, optionally with the key compression step computed out-of-memory in SQL. We demonstrate that these methods yield more precise estimates than other commonly used estimators, and also find that the compression strategy greatly increases computational efficiency. We provide in-memory (pyfixest) and out-of-memory (duckreg) python libraries to implement these estimators.
Nickell Meets Stambaugh: A Tale of Two Biases in Panel Predictive Regressions
Authors: Chengwang Liao, Ziwei Mei, Zhentao Shi
Date: 2024-10-13 00:00:00

In panel predictive regressions with persistent covariates, coexistence of the Nickell bias and the Stambaugh bias imposes challenges for hypothesis testing. This paper introduces a new estimator, the IVX-X-Jackknife (IVXJ), which effectively removes this composite bias and reinstates standard inferential procedures. The IVXJ estimator is inspired by the IVX technique in time series. In panel data where the cross section is of the same order as the time dimension, the bias of the baseline panel IVX estimator can be corrected via an analytical formula by leveraging an innovative X-Jackknife scheme that divides the time dimension into the odd and even indices. IVXJ is the first procedure that achieves unified inference across a wide range of modes of persistence in panel predictive regressions, whereas such unified inference is unattainable for the popular within-group estimator. Extended to accommodate long-horizon predictions with multiple regressions, IVXJ is used to examine the impact of debt levels on financial crises by panel local projection. Our empirics provide comparable results across different categories of debt.
On the Lower Confidence Band for the Optimal Welfare
Authors: Kirill Ponomarev, Vira Semenova
Date: 2024-10-09 00:00:00
Tasks: valid

This article addresses the question of reporting a lower confidence band (LCB) for optimal welfare in a policy learning problem. A straightforward procedure inverts a one-sided t-test based on an efficient estimator of the optimal welfare. We show that under empirically relevant data-generating processes, this procedure can be dominated by an LCB corresponding to suboptimal welfare, with the average difference of the order N-1/2. We relate the first-order dominance result to a lack of uniformity in the margin assumption, a standard sufficient condition for debiased inference on the optimal welfare ensuring that the first-best policy is well-separated from the suboptimal ones. Finally, we show that inverting the existing tests from the moment inequality literature produces LCBs that are robust to the non-uniqueness of the optimal policy and easy to compute. We find that this approach performs well empirically in the context of the National JTPA study.
Designing Scientific Grants
Authors: Christoph Carnehl, Marco Ottaviani, Justus Preusser
Date: 2024-10-16 00:00:00

This paper overviews the economics of scientific grants, focusing on the interplay between the inherent uncertainty in research, researchers' incentives, and grant design. Grants differ from traditional market systems and other science and innovation policy tools, such as prizes and patents. We outline the main economic forces specific to science, noting the limited attention given to grant funding in the economics literature. Using tools from information economics, we identify key incentive problems at various stages of the grant funding process and offer guidance for effective grant design. In the allocation stage, funders aim to select the highest-merit applications while minimizing evaluation costs. The selection rule, in turn, impacts researchers' incentives to apply and invest in their proposals. In the grant management stage, funders monitor researchers to ensure efficient use of funds. We discuss the advantages and potential pitfalls of (partial) lotteries and emphasize the effectiveness of staged grant design in promoting a productive use of grants. Beyond these broadly applicable insights, our overview highlights the need for further research on grantmaking. Understudied areas include, at the micro level, the interplay of different grant funding stages, and at the macro level, the interaction of grants with other instruments in the market for science.
Labor Market Policies in High- and Low-Interest Rate Environments: Evidence from the Euro Area
Authors: Povilas Lastauskas, Julius Stakėnas
Date: 2024-10-15 00:00:00

Do labor market policies initiated in periods of loose monetary policy yield different outcomes from those introduced when monetary tightening prevails? Using data from 11 euro-area members up to 2010 -- and extending to 17 countries up to 2020 -- we analyze three labor market policies: replacement rates, spending on active labor market policies (ALMPs), and employment protection. We find that these policies deliver different macroeconomic outcomes in low- and high-interest rate environments. In particular, ALMPs reduce unemployment if implemented under a loose monetary policy but not otherwise, whereas higher employment protection delivers expansionary effects under a tight monetary policy. These findings highlight that the effectiveness of labor market policies is significantly influenced by the monetary policy environment, emphasizing the need for coordinated policy design. Methodologically, we contribute by proposing to average local projections using Mallow's $C_{p}$ criterion, allowing for inferences that are robust to mis-specification and accommodate non-linearities.
A Firm Link: Overall, Between- and Within-Firm Inequality Through the Lens of a Sorting Model
Authors: Paweł Gola, Yuejun Zhao
Date: 2024-10-15 00:00:00

This paper provides a new theory of the observed co-movement between overall wage inequality and its between-firm component. We develop and solve analytically a frictionless sorting model with two-sided heterogeneity, in which firms consist of distributions of tasks, choose how many workers to employ and reward their workers both through wages and amenities. We show that, for empirically-relevant parameter ranges, overall and between-firm inequality are firmly linked: A change in any of the models' primitives increases overall wage inequality if and only if it also increases the ratio of between-firm to overall inequality. Subsequently, we calibrate the model to match the Norwegian economy and find that the increase in wage inequality from 1995 to 2014 had a different primary cause (raising span-of-control cost) than the accompanying rise in welfare inequality (increased skill variance), and that the apparent decrease in wage inequality after 2015 masked a continued increase in welfare inequality.
Supply Chain Optimization Strategies: An Empirical Study on Fresh Product Delivery Routes
Authors: Yu Yuan, Xiaoke Xie, Yulei Xie
Date: 2024-10-14 00:00:00

In recent years, with rising consumer demand, fresh products have gained increasing attention, leading to rapid growth in the fresh food market. However, due to their perishable nature and sensitivity to storage conditions, fresh products are vulnerable to damage during transportation. Improper handling, excessive transit times, and physical impacts can result in significant losses. As a result, enhancing the efficiency of fresh product distribution while maintaining quality has become critical to the further development of the fresh food industry. Using Y chain supermarket as a case study, this paper investigates the logistics of fresh product distribution, identifying current challenges and inefficiencies. Through literature review, expert interviews, and comparative analysis, the study offers strategic recommendations for optimizing fresh product delivery routes to improve distribution efficiency and product quality.