pwc-archive/papers-with-abstracts huggingface dataset:
Impact of phylogeny on the inference of functional sectors from protein sequence data
Authors: Nicola Dietler, Alia Abbara, Subham Choudhury, Anne-Florence Bitbol
Date: 2024-05-08 00:00:00

Statistical analysis of multiple sequence alignments of homologous proteins has revealed groups of coevolving amino acids called sectors. These groups of amino-acid sites feature collective correlations in their amino-acid usage, and they are associated to functional properties. Modeling showed that nonlinear selection on an additive functional trait of a protein is generically expected to give rise to a functional sector. These modeling results motivated a principled method, called ICOD, which is designed to identify functional sectors, as well as mutational effects, from sequence data. However, a challenge for all methods aiming to identify sectors from multiple sequence alignments is that correlations in amino-acid usage can also arise from the mere fact that homologous sequences share common ancestry, i.e. from phylogeny. Here, we generate controlled synthetic data from a minimal model comprising both phylogeny and functional sectors. We use this data to dissect the impact of phylogeny on sector identification and on mutational effect inference by different methods. We find that ICOD is most robust to phylogeny, but that conservation is also quite robust. Next, we consider natural multiple sequence alignments of protein families for which deep mutational scan experimental data is available. We show that in this natural data, conservation and ICOD best identify sites with strong functional roles, in agreement with our results on synthetic data. Importantly, these two methods have different premises, since they respectively focus on conservation and on correlations. Thus, their joint use can reveal complementary information.
Lipid-mediated hydrophobic gating in the BK potassium channel
Authors: Lucia Coronel, Giovanni Di Muccio, Brad Rothberg, Alberto Giacomello, Vincenzo Carnevale
Date: 2024-05-07 00:00:00

The large-conductance, calcium-activated potassium (BK) channel lacks the typical intracellular bundle-crossing gate present in most ion channels of the 6TM family. This observation, initially inferred from Ca$^{2+}$-free-pore accessibility experiments and recently corroborated by a CryoEM structure of the non-conductive state, raises a puzzling question: how can gating occur in absence of steric hindrance? To answer this question, we carried out molecular simulations and accurate free energy calculations to obtain a microscopic picture of the sequence of events that, starting from a Ca$^{2+}$-free state leads to ion conduction upon Ca$^{2+}$ binding. Our results highlight an unexpected role for annular lipids, which turn out to be an integral part of the gating machinery. Due to the presence of fenestrations, the "closed" Ca$^{2+}$-free pore can be occupied by the methyl groups from the lipid alkyl chains. This dynamic occupancy triggers and stabilizes the nucleation of a vapor bubble into the inner pore cavity, thus hindering ion conduction. By contrast, Ca$^{2+}$ binding results into a displacement of these lipids outside the inner cavity, lowering the hydrophobicity of this region and thus allowing for pore hydration and conduction. This lipid-mediated hydrophobic gating rationalizes several seemingly problematic experimental observations, including the state-dependent pore accessibility of blockers.
Homeostasis in Input-Output Networks: Structure, Classification and Applications
Authors: Fernando Antoneli, Martin Golubitsky, Jiaxin Jin, Ian Stewart
Date: 2024-05-06 00:00:00

Homeostasis is concerned with regulatory mechanisms, present in biological systems, where some specific variable is kept close to a set value as some external disturbance affects the system. Mathematically, the notion of homeostasis can be formalized in terms of an input-output function that maps the parameter representing the external disturbance to the output variable that must be kept within a fairly narrow range. This observation inspired the introduction of the notion of infinitesimal homeostasis, namely, the derivative of the input-output function is zero at an isolated point. This point of view allows for the application of methods from singularity theory to characterize infinitesimal homeostasis points (i.e. critical points of the input-output function). In this paper we review the infinitesimal approach to the study of homeostasis in input-output networks. An input-output network is a network with two distinguished nodes `input' and `output', and the dynamics of the network determines the corresponding input-output function of the system. This class of dynamical systems provides an appropriate framework to study homeostasis and several important biological systems can be formulated in this context. Moreover, this approach, coupled to graph-theoretic ideas from combinatorial matrix theory, provides a systematic way for classifying different types of homeostasis (homeostatic mechanisms) in input-output networks, in terms of the network topology. In turn, this leads to new mathematical concepts, such as, homeostasis subnetworks, homeostasis patterns, homeostasis mode interaction. We illustrate the usefulness of this theory with several biological examples: biochemical networks, chemical reaction networks (CRN), gene regulatory networks (GRN), Intracellular metal ion regulation and so on.
Computational ghost imaging with hybrid transforms by integrating Hadamard, discrete cosine, and Haar matrices
Authors: Yi-Ning Zhao, Lin-Shan Chen, Liu-Ya Chen, Lingxin Kong, Chong Wang, Cheng Ren, Su-Heng Zhang, De-Zhong Cao
Date: 2024-05-06 00:00:00
Tasks: Image Compression

A scenario of ghost imaging with hybrid transform approach is proposed by integrating Hadamard, discrete cosine, and Haar matrices. The measurement matrix is formed by the Kronecker product of the two different transform matrices. The image information can be conveniently reconstructed by the corresponding inverse matrices. In experiment, six hybridization sets are performed in computational ghost imaging. For an object of staggered stripes, only one bucket signal survives in the Hadamard-cosine, Haar-Hadamard, and Haar-cosine hybridization sets, demonstrating flexible image compression. For a handmade windmill object, the quality factors of the reconstructed images vary with the hybridization sets. Sub-Nyquist sampling can be applied to either or both of the different transform matrices in each hybridization set in experiment. The hybridization method can be extended to apply more transforms at once. Ghost imaging with hybrid transforms may find flexible applications in image processing, such as image compression and image encryption.
Advancing Distribution Decomposition Methods Beyond Common Supports: Applications to Racial Wealth Disparities
Authors: Bernardo Modenesi
Date: 2024-05-09 00:00:00

I generalize state-of-the-art approaches that decompose differences in the distribution of a variable of interest between two groups into a portion explained by covariates and a residual portion. The method that I propose relaxes the overlapping supports assumption, allowing the groups being compared to not necessarily share exactly the same covariate support. I illustrate my method revisiting the black-white wealth gap in the U.S. as a function of labor income and other variables. Traditionally used decomposition methods would trim (or assign zero weight to) observations that lie outside the common covariate support region. On the other hand, by allowing all observations to contribute to the existing wealth gap, I find that otherwise trimmed observations contribute from 3% to 19% to the overall wealth gap, at different portions of the wealth distribution.
Sequential Validation of Treatment Heterogeneity
Authors: Stefan Wager
Date: 2024-05-09 00:00:00

We use the martingale construction of Luedtke and van der Laan (2016) to develop tests for the presence of treatment heterogeneity. The resulting sequential validation approach can be instantiated using various validation metrics, such as BLPs, GATES, QINI curves, etc., and provides an alternative to cross-validation-like cross-fold application of these metrics.
Causal Duration Analysis with Diff-in-Diff
Authors: Ben Deaner, Hyejin Ku
Date: 2024-05-08 00:00:00

In economic program evaluation, it is common to obtain panel data in which outcomes are indicators that an individual has reached an absorbing state. For example, they may indicate whether an individual has exited a period of unemployment, passed an exam, left a marriage, or had their parole revoked. The parallel trends assumption that underpins difference-in-differences generally fails in such settings. We suggest identifying conditions that are analogous to those of difference-in-differences but apply to hazard rates rather than mean outcomes. These alternative assumptions motivate estimators that retain the simplicity and transparency of standard diff-in-diff, and we suggest analogous specification tests. Our approach can be adapted to general linear restrictions between the hazard rates of different groups, motivating duration analogues of the triple differences and synthetic control methods. We apply our procedures to examine the impact of a policy that increased the generosity of unemployment benefits, using a cross-cohort comparison.
SVARs with breaks: Identification and inference
Authors: Emanuele Bacchiocchi, Toru Kitagawa
Date: 2024-05-08 00:00:00

In this paper we propose a class of structural vector autoregressions (SVARs) characterized by structural breaks (SVAR-WB). Together with standard restrictions on the parameters and on functions of them, we also consider constraints across the different regimes. Such constraints can be either (a) in the form of stability restrictions, indicating that not all the parameters or impulse responses are subject to structural changes, or (b) in terms of inequalities regarding particular characteristics of the SVAR-WB across the regimes. We show that all these kinds of restrictions provide benefits in terms of identification. We derive conditions for point and set identification of the structural parameters of the SVAR-WB, mixing equality, sign, rank and stability restrictions, as well as constraints on forecast error variances (FEVs). As point identification, when achieved, holds locally but not globally, there will be a set of isolated structural parameters that are observationally equivalent in the parametric space. In this respect, both common frequentist and Bayesian approaches produce unreliable inference as the former focuses on just one of these observationally equivalent points, while for the latter on a non-vanishing sensitivity to the prior. To overcome these issues, we propose alternative approaches for estimation and inference that account for all admissible observationally equivalent structural parameters. Moreover, we develop a pure Bayesian and a robust Bayesian approach for doing inference in set-identified SVAR-WBs. Both the theory of identification and inference are illustrated through a set of examples and an empirical application on the transmission of US monetary policy over the great inflation and great moderation regimes.
Two-way Fixed Effects and Differences-in-Differences Estimators in Heterogeneous Adoption Designs
Authors: Clément de Chaisemartin, Diego Ciccia Xavier D'Haultfœuille, Felix Knau
Date: 2024-05-07 00:00:00

We consider treatment-effect estimation under a parallel trends assumption, in designs where no unit is treated at period one, all units receive a strictly positive dose at period two, and the dose varies across units. There are therefore no true control groups in such cases. First, we develop a test of the assumption that the treatment effect is mean independent of the treatment, under which the commonly-used two-way-fixed-effects estimator is consistent. When this test is rejected or lacks power, we propose alternative estimators, robust to heterogeneous effects. If there are units with a period-two treatment arbitrarily close to zero, the robust estimator is a difference-in-difference using units with a period-two treatment below a bandwidth as controls. Without such units, we propose non-parametric bounds, and an estimator relying on a parametric specification of treatment-effect heterogeneity. We use our results to revisit Pierce and Schott (2016) and Enikolopov et al. (2011).
Detailed Gender Wage Gap Decompositions: Controlling for Worker Unobserved Heterogeneity Using Network Theory
Authors: Jamie Fogel, Bernardo Modenesi
Date: 2024-05-07 00:00:00

Recent advances in the literature of decomposition methods in economics have allowed for the identification and estimation of detailed wage gap decompositions. In this context, building reliable counterfactuals requires using tighter controls to ensure that similar workers are correctly identified by making sure that important unobserved variables such as skills are controlled for, as well as comparing only workers with similar observable characteristics. This paper contributes to the wage decomposition literature in two main ways: (i) developing an economic principled network based approach to control for unobserved worker skills heterogeneity in the presence of potential discrimination; and (ii) extending existing generic decomposition tools to accommodate for potential lack of overlapping supports in covariates between groups being compared, which is likely to be the norm in more detailed decompositions. We illustrate the methodology by decomposing the gender wage gap in Brazil.
A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances
Authors: Yuehao Bai, Azeem M. Shaikh, Max Tabord-Meehan
Date: 2024-05-06 00:00:00
Tasks: regression

The past two decades have witnessed a surge of new research in the analysis of randomized experiments. The emergence of this literature may seem surprising given the widespread use and long history of experiments as the "gold standard" in program evaluation, but this body of work has revealed many subtle aspects of randomized experiments that may have been previously unappreciated. This article provides an overview of some of these topics, primarily focused on stratification, regression adjustment, and cluster randomization.
A quantile-based nonadditive fixed effects model
Authors: Xin Liu
Date: 2024-05-06 00:00:00
Tasks: model, quantile regression

I propose a quantile-based nonadditive fixed effects panel model to study heterogeneous causal effects. Similar to standard fixed effects (FE) model, my model allows arbitrary dependence between regressors and unobserved heterogeneity, but it generalizes the additive separability of standard FE to allow the unobserved heterogeneity to enter nonseparably. Similar to structural quantile models, my model's random coefficient vector depends on an unobserved, scalar ''rank'' variable, in which outcomes (excluding an additive noise term) are monotonic at a particular value of the regressor vector, which is much weaker than the conventional monotonicity assumption that must hold at all possible values. This rank is assumed to be stable over time, which is often more economically plausible than the panel quantile studies that assume individual rank is iid over time. It uncovers the heterogeneous causal effects as functions of the rank variable. I provide identification and estimation results, establishing uniform consistency and uniform asymptotic normality of the heterogeneous causal effect function estimator. Simulations show reasonable finite-sample performance and show my model complements fixed effects quantile regression. Finally, I illustrate the proposed methods by examining the causal effect of a country's oil wealth on its military defense spending.
Financial knowledge and borrower discouragement
Authors: David Aristei, Manuela Gallo, Raoul Minetti
Date: 2024-05-09 00:00:00
Tasks: Sensitivity

This study provides first empirical evidence on the impact of entrepreneurs' financial knowledge on borrower discouragement. Using novel survey data on Italian micro-enterprises, we find that less financially knowledgeable entrepreneurs are more likely to be discouraged from applying for new financing, due to higher application costs and expected rejection. Our main results are robust to several sensitivity checks, including accounting for potential endogeneity. Furthermore, we show that the observed self-rationing mechanism is rather inefficient, suggesting that financial knowledge might play a key role in reducing credit market imperfections.
Shaping the Future of Urban Mobility: Insights into Autonomous Vehicle Acceptance in Shanghai Through TAM and Perceived Risk Analysis
Authors: Miaomiao Shen, Linxuan Yu, Jing Xu, Zihao Sang, Ruijia Li, Xiang Yuan
Date: 2024-05-09 00:00:00
Tasks: Autonomous Vehicles, Marketing

Autonomous vehicles (AVs) have begun experimental commercialization initiatives in places such as Shanghai, China, and it is a valuable research question whether people's willingness to use AVs has changed from the prior. This study explores Shanghai residents' attitudes towards AVs by applying the Technology Acceptance Model (TAM), the Perceived Risk (BAR) model, and introducing perceived externalities as a new psychological variable. Through a survey in Shanghai, where AVs are operational, and structural equation modeling, it was found that perceived usefulness and ease of use positively influence willingness to use AVs, with perceived usefulness being the most significant factor. Perceived externalities have a positive impact, while perceived risk negatively affects willingness to use. Interestingly, ease of use increases perceived risk, but this is mitigated by the benefits perceived in usefulness. This research, differing significantly from previous studies, aims to guide government policy and industry strategies to enhance design, marketing, and popularization.
Enhancing Organizational Performance: Harnessing AI and NLP for User Feedback Analysis in Product Development
Authors: Tian Tian, Liu Ze hui, Huang Zichen, Yubing Tang
Date: 2024-05-07 00:00:00
Tasks: Decision Making, Sentiment Analysis

This paper explores the application of AI and NLP techniques for user feedback analysis in the context of heavy machine crane products. By leveraging AI and NLP, organizations can gain insights into customer perceptions, improve product development, enhance satisfaction and loyalty, inform decision-making, and gain a competitive advantage. The paper highlights the impact of user feedback analysis on organizational performance and emphasizes the reasons for using AI and NLP, including scalability, objectivity, improved accuracy, increased insights, and time savings. The methodology involves data collection, cleaning, text and rating analysis, interpretation, and feedback implementation. Results include sentiment analysis, word cloud visualizations, and radar charts comparing product attributes. These findings provide valuable information for understanding customer sentiment, identifying improvement areas, and making data-driven decisions to enhance the customer experience. In conclusion, promising AI and NLP techniques in user feedback analysis offer organizations a powerful tool to understand customers, improve product development, increase satisfaction, and drive business success
Return to Office and the Tenure Distribution
Authors: David Van Dijcke, Florian Gunsilius, Austin Wright
Date: 2024-05-07 00:00:00
Tasks: counterfactual

With the official end of the COVID-19 pandemic, debates about the return to office have taken center stage among companies and employees. Despite their ubiquity, the economic implications of return to office policies are not fully understood. Using 260 million resumes matched to company data, we analyze the causal effects of such policies on employees' tenure and seniority levels at three of the largest US tech companies: Microsoft, SpaceX, and Apple. Our estimation procedure is nonparametric and captures the full heterogeneity of tenure and seniority of employees in a distributional synthetic controls framework. We estimate a reduction in counterfactual tenure that increases for employees with longer tenure. Similarly, we document a leftward shift in the seniority distribution towards positions below the senior level. These shifts appear to be driven by employees leaving to larger firms that are direct competitors. Our results suggest that return to office policies can lead to an outflow of senior employees, posing a potential threat to the productivity, innovation, and competitiveness of the wider firm.
Large Effects of Small Cues: Priming Selfish Economic Decisions
Authors: Avichai Snir, Dudi Levy, Dian Wang, Haipeng Allan Chen, Daniel Levy
Date: 2024-05-06 00:00:00

Many experimental studies report that economics students tend to act more selfishly than students of other disciplines, a finding that received widespread public and professional attention. Two main explanations that the existing literature offers for the differences found in the behavior between economists and noneconomists are the selection effect, and the indoctrination effect. We offer an alternative, novel explanation. We argue that these differences can be explained by differences in the interpretation of the context. We test this hypothesis by conducting two social dilemma experiments in the US and Israel with participants from both economics and non-economics majors. In the experiments, participants face a tradeoff between profit maximization, that is the market norm and workers welfare, that is the social norm. We use priming to manipulate the cues that the participants receive before they make their decision. We find that when participants receive cues signaling that the decision has an economic context, both economics and non-economics students tend to maximize profits. When the participants receive cues emphasizing social norms, on the other hand, both economics and non-economics students are less likely to maximize profits. We conclude that some of the differences found between the decisions of economics and non-economics students can be explained by contextual cues.
Credit, Land Speculation, and Long-Run Economic Growth
Authors: Tomohiro Hirano, Joseph E. Stiglitz
Date: 2024-05-09 00:00:00

This paper presents a model that studies the impact of credit expansions arising from increases in collateral values or lower interest rate policies on long-run productivity and economic growth in a two-sector endogenous growth economy, with the driver of growth lying in one sector (manufacturing) but not in the other (real estate). We show that it is not so much aggregate credit expansion that matters for long-run productivity and economic growth but sectoral credit expansions. Credit expansions associated mainly with relaxation of real estate financing (capital investment financing) will be productivity-and growth-retarding (enhancing). Without financial regulations, low interest rates and more expansionary monetary policy may so encourage land speculation using leverage that productive capital investment and economic growth are decreased. Unlike in standard macroeconomic models, in ours, the equilibrium price of land will be finite even if the safe rate of interest is less than the rate of output growth.
Designing Social Learning
Authors: Aleksei Smirnov, Egor Starkov
Date: 2024-05-09 00:00:00

This paper studies strategic communication in the context of social learning. Product reviews are used by consumers to learn product quality, but in order to write a review, a consumer must be convinced to purchase the item first. When reviewers care about welfare of future consumers, this leads to a conflict: a reviewer today wants the future consumers to purchase the item even when this comes at a loss to them, so that more information is revealed for the consumers that come after. We show that due to this conflict, communication via reviews is inevitably noisy, regardless of whether reviewers can commit to a communication strategy or have to resort to cheap talk. The optimal communication mechanism involves truthful communication of extreme experiences and pools the moderate experiences together.