pwc-archive/papers-with-abstracts huggingface dataset:
Applications and Challenges of AI and Microscopy in Life Science Research: A Review
Authors: Himanshu Buckchash, Gyanendra Kumar Verma, Dilip K. Prasad
Date: 2025-01-22 00:00:00
Tasks: scientific discovery

The complexity of human biology and its intricate systems holds immense potential for advancing human health, disease treatment, and scientific discovery. However, traditional manual methods for studying biological interactions are often constrained by the sheer volume and complexity of biological data. Artificial Intelligence (AI), with its proven ability to analyze vast datasets, offers a transformative approach to addressing these challenges. This paper explores the intersection of AI and microscopy in life sciences, emphasizing their potential applications and associated challenges. We provide a detailed review of how various biological systems can benefit from AI, highlighting the types of data and labeling requirements unique to this domain. Particular attention is given to microscopy data, exploring the specific AI techniques required to process and interpret this information. By addressing challenges such as data heterogeneity and annotation scarcity, we outline potential solutions and emerging trends in the field. Written primarily from an AI perspective, this paper aims to serve as a valuable resource for researchers working at the intersection of AI, microscopy, and biology. It summarizes current advancements, key insights, and open problems, fostering an understanding that encourages interdisciplinary collaborations. By offering a comprehensive yet concise synthesis of the field, this paper aspires to catalyze innovation, promote cross-disciplinary engagement, and accelerate the adoption of AI in life science research.
Scalable dataset acquisition for data-driven lensless imaging
Authors: Clara S. Hung, Leyla A. Kabuli, Vasilisa Ponomarenko, Laura Waller
Date: 2025-01-23 00:00:00

Data-driven developments in lensless imaging, such as machine learning-based reconstruction algorithms, require large datasets. In this work, we introduce a data acquisition pipeline that can capture from multiple lensless imaging systems in parallel, under the same imaging conditions, and paired with computational ground truth registration. We provide an open-access 25,000 image dataset with two lensless imagers, a reproducible hardware setup, and open-source camera synchronization code. Experimental datasets from our system can enable data-driven developments in lensless imaging, such as machine learning-based reconstruction algorithms and end-to-end system design.
A Non-Parametric Approach to Heterogeneity Analysis
Authors: Avner Seror
Date: 2025-01-23 00:00:00

This paper introduces a network-based method to capture heterogeneity in consumer microdata. We develop a permutation-based approach that repeatedly combines random samples of all agents' decisions, and partitions agents into jointly rational types. Aggregating these partitions yields a network that captures unobserved heterogeneity, where edges measure how often two agents share the same type across partitions. To evaluate how observable characteristics align with the heterogeneity, we implement permutation tests that shuffle covariate labels across network nodes, thereby generating a null distribution of alignment. We show that this test is exact, with asymptotic power of one. We further propose network-based measures that quantify whether nodes with the same observable attributes are disproportionately linked or clustered, along with standardized effect sizes that gauge each covariate's global influence. This yields a flexible, nonparametric measure of the heterogeneity structure. Finally, we apply our method to grocery expenditure data from the Stanford Basket Dataset.
Generalizability with ignorance in mind: learning what we do (not) know for archetypes discovery
Authors: Emily Breza, Arun G. Chandrasekhar, Davide Viviano
Date: 2025-01-23 00:00:00

When studying policy interventions, researchers are often interested in two related goals: i) learning for which types of individuals the program has the largest effects (heterogeneity) and ii) understanding whether those patterns of treatment effects have predictive power across environments (generalizability). To that end, we develop a framework to learn from the data how to partition observations into groups of individual and environmental characteristics whose effects are generalizable for others - a set of generalizable archetypes. Our view is that implicit in the task of archetypal discovery is detecting those contexts where effects do not generalize and where researchers should collect more evidence before drawing inference on treatment effects. We introduce a method that jointly estimates when and how a prediction can be formed and when, instead, researchers should admit ignorance and elicit further evidence before making predictions. We provide both a decision-theoretic and Bayesian foundation of our procedure. We derive finite-sample (frequentist) regret guarantees, asymptotic theory for inference, and discuss computational properties. We illustrate the benefits of our procedure over existing alternatives that would fail to admit ignorance and force pooling across all units by re-analyzing a multifaceted program targeted towards the poor across six different countries.
Continuity of the Distribution Function of the argmax of a Gaussian Process
Authors: Matias D. Cattaneo, Gregory Fletcher Cox, Michael Jansson, Kenichi Nagasawa
Date: 2025-01-22 00:00:00

An increasingly important class of estimators has members whose asymptotic distribution is non-Gaussian, yet characterizable as the argmax of a Gaussian process. This paper presents high-level sufficient conditions under which such asymptotic distributions admit a continuous distribution function. The plausibility of the sufficient conditions is demonstrated by verifying them in three prominent examples, namely maximum score estimation, empirical risk minimization, and threshold regression estimation. In turn, the continuity result buttresses several recently proposed inference procedures whose validity seems to require a result of the kind established herein. A notable feature of the high-level assumptions is that one of them is designed to enable us to employ the celebrated Cameron-Martin theorem. In a leading special case, the assumption in question is demonstrably weak and appears to be close to minimal.
An Adaptive Moving Average for Macroeconomic Monitoring
Authors: Philippe Goulet Coulombe, Karin Klieber
Date: 2025-01-22 00:00:00

The use of moving averages is pervasive in macroeconomic monitoring, particularly for tracking noisy series such as inflation. The choice of the look-back window is crucial. Too long of a moving average is not timely enough when faced with rapidly evolving economic conditions. Too narrow averages are noisy, limiting signal extraction capabilities. As is well known, this is a bias-variance trade-off. However, it is a time-varying one: the optimal size of the look-back window depends on current macroeconomic conditions. In this paper, we introduce a simple adaptive moving average estimator based on a Random Forest using as sole predictor a time trend. Then, we compare the narratives inferred from the new estimator to those derived from common alternatives across series such as headline inflation, core inflation, and real activity indicators. Notably, we find that this simple tool provides a different account of the post-pandemic inflation acceleration and subsequent deceleration.
Estimation of Linear models from Coarsened Observations Estimation of Linear models Estimation from Coarsened Observations A Method of Moments Approach
Authors: Bernard M. S. van Praag, J. Peter Hop, William H. Greene
Date: 2025-01-18 00:00:00
Tasks: Econometrics, Marketing

In the last few decades, the study of ordinal data in which the variable of interest is not exactly observed but only known to be in a specific ordinal category has become important. In Psychometrics such variables are analysed under the heading of item response models (IRM). In Econometrics, subjective well-being (SWB) and self-assessed health (SAH) studies, and in marketing research, Ordered Probit, Ordered Logit, and Interval Regression models are common research platforms. To emphasize that the problem is not specific to a specific discipline we will use the neutral term coarsened observation. For single-equation models estimation of the latent linear model by Maximum Likelihood (ML) is routine. But, for higher -dimensional multivariate models it is computationally cumbersome as estimation requires the evaluation of multivariate normal distribution functions on a large scale. Our proposed alternative estimation method, based on the Generalized Method of Moments (GMM), circumvents this multivariate integration problem. The method is based on the assumed zero correlations between explanatory variables and generalized residuals. This is more general than ML but coincides with ML if the error distribution is multivariate normal. It can be implemented by repeated application of standard techniques. GMM provides a simpler and faster approach than the usual ML approach. It is applicable to multiple -equation models with -dimensional error correlation matrices and response categories for the equation. It also yields a simple method to estimate polyserial and polychoric correlations. Comparison of our method with the outcomes of the Stata ML procedure cmp yields estimates that are not statistically different, while estimation by our method requires only a fraction of the computing time.
Recovering Unobserved Network Links from Aggregated Relational Data: Discussions on Bayesian Latent Surface Modeling and Penalized Regression
Authors: Yen-hsuan Tseng
Date: 2025-01-18 00:00:00
Tasks: Computational Efficiency, Epidemiology, Sociology

Accurate network data are essential in fields such as economics, sociology, and computer science. Aggregated Relational Data (ARD) provides a way to capture network structures using partial data. This article compares two main frameworks for recovering network links from ARD: Bayesian Latent Surface Modeling (BLSM) and Frequentist Penalized Regression (FPR). Using simulation studies and real-world applications, we evaluate their theoretical properties, computational efficiency, and practical utility in domains like financial risk assessment and epidemiology. Key findings emphasize the importance of trait design, privacy considerations, and hybrid modeling approaches to improve scalability and robustness.
The AI Penalization Effect: People Reduce Compensation for Workers Who Use AI
Authors: Jin Kim, Shane Schweitzer, Christoph Riedl, David De Cremer
Date: 2025-01-22 00:00:00
Tasks: Multiple-choice

We investigate whether and why people might adjust compensation for workers who use AI tools. Across 11 studies (N = 3,846), participants consistently lowered compensation for AI-assisted workers compared to those who were unassisted. This "AI Penalization" effect was robust across (1) different types of work (e.g., specific tasks or general work scenarios) and worker statuses (e.g., full-time, part-time, or freelance), (2) different forms of compensation (e.g., required payments or optional bonuses) and their timing, (3) various methods of eliciting compensation (e.g., slider scale, multiple choice, and numeric entry), and (4) conditions where workers' output quality was held constant, subject to varying inferences, or statistically controlled. Moreover, the effect emerged not only in hypothetical compensation scenarios (Studies 1-9) but also with real gig workers and real monetary compensation (Studies 10 and 11). People reduced compensation for workers using AI because they believed these workers deserved less credit than those who did not use AI (Studies 7 and 8). This mediated effect attenuated when it was less permissible to reduce worker compensation, such as when employment contracts provide stricter constraints (Study 8). Our findings suggest that adoption of AI tools in the workplace may exacerbate inequality among workers, as those protected by structured contracts are less vulnerable to compensation reductions, while those without such protections are at greater risk of financial penalties for using AI.
Audio-Based Epileptic Seizure Detection
Authors: M.N. Istiaq Ahsan, Csaba Kertesz, Annamaria Mesaros, Toni Heittola, Andrew Knight, Tuomas Virtanen
Date: 2019-09-06 00:00:00
Proceeding: IEEE 2019 9
Tasks: Event Detection, Seizure Detection, Sound Event Detection

This paper investigates automatic epileptic seizure
detection from audio recordings using convolutional neural net-
works. The labeling and analysis of seizure events are necessary
in the medical field for patient monitoring, but the manual
annotation by expert annotators is time-consuming and extremely
monotonous. The proposed method treats all seizure vocalizations
as a single target event class, and models the seizure detection
problem in terms of detecting the target vs non-target classes. For
detection, the method employs a convolutional neural network
trained to detect the seizure events in short time segments, based
on mel-energies as feature representation. Experiments carried
out with different seizure types on 900 hours of audio recordings
from 40 patients show that the proposed approach can detect
seizures with over 80% accuracy, with a 13% false positive rate
and a 22.8% false negative rate.
Index Terms—Epileptic seizure detection, convolutional neural
network (CNN), sound event detection, audio processing and
analysis.
Exploring the heterogeneous impacts of Indonesia's conditional cash transfer scheme (PKH) on maternal health care utilisation using instrumental causal forests
Authors: Vishalie Shah, Julia Hatamyar, Taufik Hidayat, Noemi Kreif
Date: 2025-01-22 00:00:00

This paper uses instrumental causal forests, a novel machine learning method, to explore the treatment effect heterogeneity of Indonesia's conditional cash transfer scheme on maternal health care utilisation. Using randomised programme assignment as an instrument for enrollment in the scheme, we estimate conditional local average treatment effects for four key outcomes: good assisted delivery, delivery in a health care facility, pre-natal visits, and post-natal visits. We find significant treatment effect heterogeneity by supply-side characteristics, even though supply-side readiness was taken into account during programme development. Mothers in areas with more doctors, nurses, and delivery assistants were more likely to benefit from the programme, in terms of increased rates of good assisted delivery outcome. We also find large differences in benefits according to indicators of household poverty and survey wave, reflecting the possible impact of changes in programme design in its later years. The impact on post-natal visits in 2013 displayed the largest heterogeneity among all outcomes, with some women less likely to attend post-natal check ups after receiving the cash transfer in the long term.
Enhancing Economic Literacy through Causal Diagrams
Authors: Oleg V. Pavlov, Natalia V. Smirnova, Elena V. Smirnova
Date: 2025-01-22 00:00:00

A literacy-targeted approach to economic instruction draws on insights from cognitive science. It highlights that students process complex economic information by constructing and modifying schemas that represent economic material. Following this approach, we developed a set of instructional activities centered around causal diagrams that promote a deeper understanding of economic topics beyond the traditional lecture-based methods. Our results show that structural debriefing activities can be used effectively to introduce students to the causal diagrams that explain key economic relationships in the national income model, government-purchases multiplier and tax multiplier.
Dollarized Economies in Latin America. An Inflationary Analysis of Pre, During and Post Pandemic
Authors: Joseph Ariel Tello Carvache, Jorge Alejandro Moncayo Correa, Carlos Sempertegui Seminario
Date: 2025-01-21 00:00:00

Given the hyperinflation that most of the Latin American countries suffered in the 90 and their decision towards adopting dollarization and in most cases keeping their own currency, this paper analyzes the effectiveness of dollarization as a protective mechanism against economic disruptions in Latin American countries. It assesses the context that led Latin American dollarized countries to dollarize and analyzes CPI, GDP, and the poverty rates pre, during, and postpandemic in Latin American countries, considering those that are dollarized and those that are not, and evaluating its relation to the US. Interviews were carried out with experts in the field. It assesses the advantages and disadvantages of dollarization regarding global crises. The data was compared and analyzed to check if there were patterns that support the paper objective which is that dollarization might serve as a protective mechanism against economic disruption. It was found that dollarization protects the economy against inflation, however, it does not fully protect the economy when considering economic performance and poverty. In conclusion, this research concludes that dollarization does not completely serve as a protective mechanism against economic disruptions nonetheless, it found that a bigger role is played by domestic policies and government action.
An Empirical Approach toward the Interaction between Pension System and Demographic Dividend: Evidence of a Co-Integrated Socio-Economic Model of China
Authors: Mostafa R. Sarkandiz
Date: 2025-01-21 00:00:00

The present study attempts to investigate the demographic dividend phenomenon in China. For this goal, a socio-economic approach has been used to analyze the topic from 1995 to 2019. In contrast to common belief, the outcomes revealed that China is still benefiting from a demographic dividend. However, due to the accelerated population aging trend and the increasing share of government expenditure on the public pension system, the window opportunity has yet to close. Furthermore, concerning the absolute value of estimated coefficients, the employment rate of young people in the age bundle of [15, 24] has the highest impact on decreasing government expenditure.
Open Sourcing GPTs: Economics of Open Sourcing Advanced AI Models
Authors: Mahyar Habibi
Date: 2025-01-20 00:00:00

This paper explores the economic underpinnings of open sourcing advanced large language models (LLMs) by for-profit companies. Empirical analysis reveals that: (1) LLMs are compatible with R&D portfolios of numerous technologically differentiated firms; (2) open-sourcing likelihood decreases with an LLM's performance edge over rivals, but increases for models from large tech companies; and (3) open-sourcing an advanced LLM led to an increase in research-related activities. Motivated by these findings, a theoretical framework is developed to examine factors influencing a profit-maximizing firm's open-sourcing decision. The analysis frames this decision as a trade-off between accelerating technology growth and securing immediate financial returns. A key prediction from the theoretical analysis is an inverted-U-shaped relationship between the owner's size, measured by its share of LLM-compatible applications, and its propensity to open source the LLM. This finding suggests that moderate market concentration may be beneficial to the open source ecosystems of multi-purpose software technologies.
Sovereign Debt Default and Climate Risk
Authors: Emilio Barucci, Daniele Marazzina, Aldo Nassigh
Date: 2025-01-20 00:00:00

We explore the interplay between sovereign debt default/renegotiation and environmental factors (e.g., pollution from land use, natural resource exploitation). Pollution contributes to the likelihood of natural disasters and influences economic growth rates. The country can default on its debt at any time while also deciding whether to invest in pollution abatement. The framework provides insights into the credit spreads of sovereign bonds and explains the observed relationship between bond spread and a country's climate vulnerability. Through calibration for developing and low-income countries, we demonstrate that there is limited incentive for these countries to address climate risk, and the sensitivity of bond spreads to climate vulnerability remains modest. Climate risk does not play a relevant role on the decision to default on sovereign debt. Financial support for climate abatement expenditures can effectively foster climate adaptation actions, instead renegotiation conditional upon pollution abatement does not produce any effect.
The Missing Link: Identifying Digital Intermediaries in E-Government
Authors: Sergio Toro-Maureira, Alejandro Olivares, Rocio Saez-Vergara, Sebastian Valenzuela, Macarena Valenzuela, Teresa Correa
Date: 2025-01-18 00:00:00

The digitalization of public administration has advanced significantly on a global scale. Many governments now view digital platforms as essential for improving the delivery of public services and fostering direct communication between citizens and public institutions. However, this view overlooks the role played by digital intermediaries significantly shape the provision of e-government services. Using Chile as a case study, we analyze these intermediaries through a national survey on digitalization, we find five types of intermediaries: family members, peers, political figures, bureaucrats, and community leaders. The first two classes comprise close intermediaries, while the latter three comprise hierarchical intermediaries. Our findings suggest that all these intermediaries are a critical but underexplored element in the digitalization of public administration.
Building Short Value Chains for Animal Welfare-Friendly Products Adoption: Insights from a Restaurant-Based Study in Japan
Authors: Takuya Washio, Sota Takagi, Miki Saijo, Ken Wako, Keitaro Sato, Hiroyuki Ito, Ken-ichi Takeda, Takumi Ohashi
Date: 2025-01-18 00:00:00

As global attention on sustainable and ethical food systems grows, animal welfare-friendly products (AWFP) are increasingly recognized as essential to addressing consumer and producer concerns. However, traditional research often neglects the interdependencies between production, retail, and consumption stages within the supply chain. This study examined how cross-stage interactions among producers, consumers, and retail intermediaries can promote AWFP adoption. By establishing a short value chain from production to consumption, we conducted a two-month choice experiment in the operational restaurant, employing a mixed-method approach to quantitatively and qualitatively assess stakeholder responses. The results revealed that providing information about AWFP practices significantly influenced consumer behavior, increasing both product selection and perceived value. Retailers recognized the potential for economic benefits and strengthened customer loyalty, while producers identified new revenue opportunities by re-fattening delivered cow. These coordinated changes - defined as synchronized actions and mutual reinforcement across production, retail, and consumption - generated positive feedback loops that motivated stakeholders to adopt AWFP practices. This research underscores the potential of strategically designed short value chain to foster cross-stage coordination and highlights their role as practical entry points for promoting sustainable and ethical food systems on a larger scale.