pwc-archive/papers-with-abstracts huggingface dataset:
Effects of pristine and photoaged tire wear particles and their leachable additives on key nitrogen removal processes and nitrous oxide accumulation in estuarine sediments
Authors: Jinyu Ye, Yuan Gao, Huan Gao, Qingqing Zhao, Minjie Zhou, Xiangdong Xue, Meng Shi
Date: 2024-09-13 00:00:00

Global estuaries and coastal regions, acting as critical interfaces for mitigating nitrogen flux to marine, concurrently contend with contamination from tire wear particles (TWPs). However, the effects of pristine and photoaged TWP (P-TWP and A-TWP) and their leachates (P-TWPL and A-TWPL) on key nitrogen removal processes in estuarine sediments remain unclear. This study explored the responses of denitrification rate, anammox rate, and nitrous oxide (N2O) accumulation to P-TWP, A-TWP, P-TWPL, and A-TWPL exposures in estuarine sediments, and assessed the potential biotoxic substances in TWPL. Results indicate that P-TWP inhibited the denitrification rate and increased N2O accumulation without significantly impacting the anammox rate. A-TWP intensified the denitrification rate inhibition by further reducing narG gene abundance and NAR activity, and also decreased the hzo gene abundance, HZO activity, and Candidatus Kuenenia abundance, thereby slowing the anammox rate. N2O accumulation was lower after A-TWP exposure than P-TWP, with the NIR/NOS and NOR/NOS activity ratios closely associated with N2O accumulation. Batch experiments indicated that photoaging promoted Zn release from TWPL, significantly contributing to the inhibited denitrification rate and increased N2O accumulation by TWP. In addition, TWP drives changes in microbial community structure through released additives, with the abundance of DNB and AnAOB closely linked to the Zn, Mn, and As concentrations in TWPL. This study offers insights into assessing the environmental risks of TWPs in estuarine ecosystems.
Higher order definition of causality by optimally conditioned transfer entropy
Authors: Jakub Kořenek, Pavel Sanda, Jaroslav Hlinka
Date: 2024-08-30 00:00:00
Tasks: Causal Inference

The description of the dynamics of complex systems, in particular the capture of the interaction structure and causal relationships between elements of the system, is one of the central questions of interdisciplinary research. While the characterization of pairwise causal interactions is a relatively ripe field with established theoretical concepts and the current focus is on technical issues of their efficient estimation, it turns out that the standard concepts such as Granger causality or transfer entropy may not faithfully reflect possible synergies or interactions of higher orders, phenomena highly relevant for many real-world complex systems. In this paper, we propose a generalization and refinement of the information-theoretic approach to causal inference, enabling the description of truly multivariate, rather than multiple pairwise, causal interactions, and moving thus from causal networks to causal hypernetworks. In particular, while keeping the ability to control for mediating variables or common causes, in case of purely synergetic interactions such as the exclusive disjunction, it ascribes the causal role to the multivariate causal set but \emph{not} to individual inputs, distinguishing it thus from the case of e.g. two additive univariate causes. We demonstrate this concept by application to illustrative theoretical examples as well as a biophysically realistic simulation of biological neuronal dynamics recently reported to employ synergetic computations.
SciDVS: A Scientific Event Camera with 1.7% Temporal Contrast Sensitivity at 0.7 lux
Authors: Rui Graca, Sheng Zhou, Brian Mcreynolds, Tobi Delbruck
Date: 2024-09-15 00:00:00
Tasks: Sensitivity

This paper reports a Dynamic Vision Sensor (DVS) event camera that is 6x more sensitive at 14x lower illumination than existing commercial and prototype cameras. Event cameras output a sparse stream of brightness change events. Their high dynamic range (HDR), quick response, and high temporal resolution provide key advantages for scientific applications that involve low lighting conditions and sparse visual events. However, current DVS are hindered by low sensitivity, resulting from shot noise and pixel-to-pixel mismatch. Commercial DVS have a minimum brightness change threshold of >10%. Sensitive prototypes achieved as low as 1%, but required kilo-lux illumination. Our SciDVS prototype fabricated in a 180nm CMOS image sensor process achieves 1.7% sensitivity at chip illumination of 0.7 lx and 18 Hz bandwidth. Novel features of SciDVS are (1) an auto-centering in-pixel preamplifier providing intrascene HDR and increased sensitivity, (2) improved control of bandwidth to limit shot noise, and (3) optional pixel binning, allowing the user to trade spatial resolution for sensitivity.
A Statistical Equilibrium Approach to Adam Smith's Labor Theory of Value
Authors: Ellis Scharfenaker, Bruno Theodosio, Duncan K. Foley
Date: 2024-09-16 00:00:00

Adam Smith's inquiry into the emergence and stability of the self-organization of the division of labor in commodity production and exchange is considered using statistical equilibrium methods from statistical physics. We develop a statistical equilibrium model of the distribution of independent direct producers in a hub-and-spoke framework that predicts both the center of gravity of producers across lines of production as well as the endogenous fluctuations between lines of production that arise from Smith's concept of "perfect liberty". The ergodic distribution of producers implies a long-run balancing of "advantages to disadvantages" across lines of employment and gravitation of market prices around Smith's natural prices.
Turing Video-based Cognitive Tests to Handle Entangled Concepts
Authors: Diederik Aerts, Roberto Leporini, Sandro Sozzo
Date: 2024-09-13 00:00:00

We have proved in both human-based and computer-based tests that natural concepts generally 'entangle' when they combine to form complex sentences, violating the rules of classical compositional semantics. In this article, we present the results of an innovative video-based cognitive test on a specific conceptual combination, which significantly violates the Clauser-Horne-Shimony-Holt version of Bell's inequalities ('CHSH inequality'). We also show that collected data can be faithfully modeled within a quantum-theoretic framework elaborated by ourselves and a 'strong form of entanglement' occurs between the component concepts. While the video-based test confirms previous empirical results on entanglement in human cognition, our empirical approach surpasses language barriers and eliminates the need for prior knowledge, enabling universal accessibility. Finally, this transformative methodology allows one to unravel the underlying connections that drive our perception of reality. As a matter of fact, we provide a novel explanation for the appearance of entanglement in both physics and cognitive realms.
A Simple and Adaptive Confidence Interval when Nuisance Parameters Satisfy an Inequality
Authors: Gregory Fletcher Cox
Date: 2024-09-16 00:00:00
Tasks: regression, valid

Inequalities may appear in many models. They can be as simple as assuming a parameter is nonnegative, possibly a regression coefficient or a treatment effect. This paper focuses on the case that there is only one inequality and proposes a confidence interval that is particularly attractive, called the inequality-imposed confidence interval (IICI). The IICI is simple. It does not require simulations or tuning parameters. The IICI is adaptive. It reduces to the usual confidence interval (calculated by adding and subtracting the standard error times the $1 - \alpha/2$ standard normal quantile) when the inequality is sufficiently slack. When the inequality is sufficiently violated, the IICI reduces to an equality-imposed confidence interval (the usual confidence interval for the submodel where the inequality holds with equality). Also, the IICI is uniformly valid and has (weakly) shorter length than the usual confidence interval; it is never longer. The first empirical application considers a linear regression when a coefficient is known to be nonpositive. A second empirical application considers an instrumental variables regression when the endogeneity of a regressor is known to be nonnegative.
Unconditional Randomization Tests for Interference
Authors: Liang Zhong
Date: 2024-09-14 00:00:00
Tasks: Causal Inference, valid

When conducting causal inference or designing policy, researchers are often concerned with the existence and extent of interference between units, which may be influenced by factors such as distance, proximity, and connection strength. However, complex correlations across units pose significant challenges for inference. This paper introduces partial null randomization tests (PNRTs), a novel framework for testing interference in experimental settings. PNRTs adopt a design-based approach, combining unconditional randomization testing with pairwise comparisons to enable straightforward implementation and ensure finite-sample validity under minimal assumptions about network structure. To illustrate the method's broad applicability, this paper applies it to a large-scale experiment by Blattman et al. (2021) in Bogota, Colombia, which evaluates the impact of hotspot policing on crime using street segments as units of analysis. The findings indicate that increasing police patrolling time in hotspots has a significant displacement effect on violent crime but not on property crime. A simulation study calibrated to this dataset further demonstrates the strong power properties of PNRTs and their suitability for general interference scenarios.
The Clustered Dose-Response Function Estimator for continuous treatment with heterogeneous treatment effects
Authors: Cerqua Augusto, Di Stefano Roberta, Mattera Raffaele
Date: 2024-09-13 00:00:00

Many treatments are non-randomly assigned, continuous in nature, and exhibit heterogeneous effects even at identical treatment intensities. Taken together, these characteristics pose significant challenges for identifying causal effects, as no existing estimator can provide an unbiased estimate of the average causal dose-response function. To address this gap, we introduce the Clustered Dose-Response Function (Cl-DRF), a novel estimator designed to discern the continuous causal relationships between treatment intensity and the dependent variable across different subgroups. This approach leverages both theoretical and data-driven sources of heterogeneity and operates under relaxed versions of the conditional independence and positivity assumptions, which are required to be met only within each identified subgroup. To demonstrate the capabilities of the Cl-DRF estimator, we present both simulation evidence and an empirical application examining the impact of European Cohesion funds on economic growth.
Bayesian Dynamic Factor Models for High-dimensional Matrix-valued Time Series
Authors: Wei zhang
Date: 2024-09-12 00:00:00
Tasks: Time Series

High-dimensional matrix-valued time series are of significant interest in economics and finance, with prominent examples including cross region macroeconomic panels and firms' financial data panels. We introduce a class of Bayesian matrix dynamic factor models that utilize matrix structures to identify more interpretable factor patterns and factor impacts. Our model accommodates time-varying volatility, adjusts for outliers, and allows cross-sectional correlations in the idiosyncratic components. To determine the dimension of the factor matrix, we employ an importance-sampling estimator based on the cross-entropy method to estimate marginal likelihoods. Through a series of Monte Carlo experiments, we show the properties of the factor estimators and the performance of the marginal likelihood estimator in correctly identifying the true dimensions of the factor matrices. Applying our model to a macroeconomic dataset and a financial dataset, we demonstrate its ability in unveiling interesting features within matrix-valued time series.
Sensitivity analysis of the perturbed utility stochastic traffic equilibrium
Authors: Mogens Fosgerau, Nikolaj Nielsen, Mads Paulsen, Thomas Kjær Rasmussen, Rui Yao
Date: 2024-09-12 00:00:00
Tasks: model, Sensitivity

This paper develops a novel sensitivity analysis framework for the perturbed utility route choice (PURC) model and the accompanying stochastic traffic equilibrium model. We provide general results that determine the marginal change in link flows following a marginal change in link costs across the network in the cases of flow-independent and flow-dependent link costs. We derive analytical sensitivity expressions for the Jacobian of the individual optimal PURC flow and equilibrium link flows with respect to link cost parameters under mild differentiability assumptions. Numerical examples illustrate the robustness of our method, demonstrating its use for estimating equilibrium link flows after link cost shifts, identifying critical design parameters, and quantifying uncertainty in performance predictions. The findings have implications for network design, pricing strategies, and policy analysis in transportation planning and economics, providing a bridge between theoretical models and real-world applications.
Automatic Pricing and Replenishment Strategies for Vegetable Products Based on Data Analysis and Nonlinear Programming
Authors: Mingpu Ma
Date: 2024-09-05 00:00:00

In the field of fresh produce retail, vegetables generally have a relatively limited shelf life, and their quality deteriorates with time. Most vegetable varieties, if not sold on the day of delivery, become difficult to sell the following day. Therefore, retailers usually perform daily quantitative replenishment based on historical sales data and demand conditions. Vegetable pricing typically uses a "cost-plus pricing" method, with retailers often discounting products affected by transportation loss and quality decline. In this context, reliable market demand analysis is crucial as it directly impacts replenishment and pricing decisions. Given the limited retail space, a rational sales mix becomes essential. This paper first uses data analysis and visualization techniques to examine the distribution patterns and interrelationships of vegetable sales quantities by category and individual item, based on provided data on vegetable types, sales records, wholesale prices, and recent loss rates. Next, it constructs a functional relationship between total sales volume and cost-plus pricing for vegetable categories, forecasts future wholesale prices using the ARIMA model, and establishes a sales profit function and constraints. A nonlinear programming model is then developed and solved to provide daily replenishment quantities and pricing strategies for each vegetable category for the upcoming week. Further, we optimize the profit function and constraints based on the actual sales conditions and requirements, providing replenishment quantities and pricing strategies for individual items on July 1 to maximize retail profit. Finally, to better formulate replenishment and pricing decisions for vegetable products, we discuss and forecast the data that retailers need to collect and analyses how the collected data can be applied to the above issues.
Bitcoin Transaction Behavior Modeling Based on Balance Data
Authors: Yu Zhang, Claudio Tessone
Date: 2024-09-16 00:00:00

When analyzing Bitcoin users' balance distribution, we observed that it follows a log-normal pattern. Drawing parallels from the successful application of Gibrat's law of proportional growth in explaining city size and word frequency distributions, we tested whether the same principle could account for the log-normal distribution in Bitcoin balances. However, our calculations revealed that the exponent parameters in both the drift and variance terms deviate slightly from one. This suggests that Gibrat's proportional growth rule alone does not fully explain the log-normal distribution observed in Bitcoin users' balances. During our exploration, we discovered an intriguing phenomenon: Bitcoin users tend to fall into two distinct categories based on their behavior, which we refer to as ``poor" and ``wealthy" users. Poor users, who initially purchase only a small amount of Bitcoin, tend to buy more bitcoins first and then sell out all their holdings gradually over time. The certainty of selling all their coins is higher and higher with time. In contrast, wealthy users, who acquire a large amount of Bitcoin from the start, tend to sell off their holdings over time. The speed at which they sell their bitcoins is lower and lower over time and they will hold at least a small part of their initial holdings at last. Interestingly, the wealthier the user, the larger the proportion of their balance and the higher the certainty they tend to sell. This research provided an interesting perspective to explore bitcoin users' behaviors which may apply to other finance markets.
Simulation of Public Cash Transfer Programs on US Entrepreneurs' Financing Constraint
Authors: Liukun Wu
Date: 2024-09-16 00:00:00

In this paper, I conduct a policy exercise about how much the introduction of a cash transfer program as large as a Norwegian-sized lottery sector to the United States would affect startups. The key results are that public cash transfer programs (like lottery) do not increase much the number of new startups, but increase the size of startups, and only modestly increase aggregate productivity and output. The most important factor for entrepreneurs to start new businesses is their ability.
Revisiting the state-space model of unawareness
Authors: Alex A. T. Rathke
Date: 2024-09-15 00:00:00
Tasks: model

We propose a knowledge operator based on the agent's possibility correspondence which preserves her non-trivial unawareness within the standard state-space model. Our approach may provide a solution to the classical impossibility result that 'an unaware agent must be aware of everything'.
Contract Structure and Risk Aversion in Longevity Risk Transfers
Authors: David Landriault, Bin Li, Hong Li, Yuanyuan Zhang
Date: 2024-09-13 00:00:00

This paper introduces an economic framework to assess optimal longevity risk transfers between institutions, focusing on the interactions between a buyer exposed to long-term longevity risk and a seller offering longevity protection. While most longevity risk transfers have occurred in the reinsurance sector, where global reinsurers provide long-term protections, the capital market for longevity risk transfer has struggled to gain traction, resulting in only a few short-term instruments. We investigate how differences in risk aversion between the two parties affect the equilibrium structure of longevity risk transfer contracts, contrasting `static' contracts that offer long-term protection with `dynamic' contracts that provide short-term, variable coverage. Our analysis shows that static contracts are preferred by more risk-averse buyers, while dynamic contracts are favored by more risk-averse sellers who are reluctant to commit to long-term agreements. When incorporating information asymmetry through ambiguity, we find that ambiguity can cause more risk-averse sellers to stop offering long-term contracts. With the assumption that global reinsurers, acting as sellers in the reinsurance sector and buyers in the capital market, are generally less risk-averse than other participants, our findings provide theoretical explanations for current market dynamics and suggest that short-term instruments offer valuable initial steps toward developing an efficient and active capital market for longevity risk transfer.
A Market for Lemons? Strategic Directions for a Vigilant Application of Artificial Intelligence in Entrepreneurship Research
Authors: Martin Obschonka, Moren Levesque
Date: 2024-09-13 00:00:00

The rapid expansion of AI adoption (e.g., using machine learning, deep learning, or large language models as research methods) and the increasing availability of big data have the potential to bring about the most significant transformation in entrepreneurship scholarship the field has ever witnessed. This article makes a pressing meta-contribution by highlighting a significant risk of unproductive knowledge exchanges in entrepreneurship research amid the AI revolution. It offers strategies to mitigate this risk and provides guidance for future AI-based studies to enhance their collective impact and relevance. Drawing on Akerlof's renowned market-for-lemons concept, we identify the potential for significant knowledge asymmetries emerging from the field's evolution into its current landscape (e.g., complexities around construct validity, theory building, and research relevance). Such asymmetries are particularly deeply ingrained due to what we term the double-black-box puzzle, where the widely recognized black box nature of AI methods intersects with the black box nature of the entrepreneurship phenomenon driven by inherent uncertainty. As a result, these asymmetries could lead to an increase in suboptimal research products that go undetected, collectively creating a market for lemons that undermines the field's well-being, reputation, and impact. However, importantly, if these risks can be mitigated, the AI revolution could herald a new golden era for entrepreneurship research. We discuss the necessary actions to elevate the field to a higher level of AI resilience while steadfastly maintaining its foundational principles and core values.
Dynamic Link and Flow Prediction in Bank Transfer Networks
Authors: Shu Takahashi, Kento Yamamoto, Shumpei Kobayashi, Ryoma Kondo, Ryohei Hisano
Date: 2024-09-13 00:00:00
Tasks: Dynamic Link Prediction, Link Prediction, Prediction

The prediction of both the existence and weight of network links at future time points is essential as complex networks evolve over time. Traditional methods, such as vector autoregression and factor models, have been applied to small, dense networks, but become computationally impractical for large-scale, sparse, and complex networks. Some machine learning models address dynamic link prediction, but few address the simultaneous prediction of both link presence and weight. Therefore, we introduce a novel model that dynamically predicts link presence and weight by dividing the task into two sub-tasks: predicting remittance ratios and forecasting the total remittance volume. We use a self-attention mechanism that combines temporal-topological neighborhood features to predict remittance ratios and use a separate model to forecast the total remittance volume. We achieve the final prediction by multiplying the outputs of these models. We validated our approach using two real-world datasets: a cryptocurrency network and bank transfer network.
The first alumni donation in 1880 in Japan: social image and the open-academic record system
Authors: Eiji Yamamura
Date: 2024-09-12 00:00:00

In 1880, Keio, a private school in Japan, was in jeopardy of being closed. To cope with the situation, the school first created a fundraising campaign during the 18801-90 period. The school was established in 1857, and since 1861, the list covering all students academic record has been distributed not only to teachers but also to all students. Individual-level historical academic record was integrated with the list of contributors. Using the data, we compared persons who had learned in Keio before and after the system was introduced. The main findings are presented as follows. first, graduates who share the academic record are more likely to contribute, and their amount of donation is larger; second, the class size is negatively correlated with the likelihood of contribution and with its amount; and third, academic performance, as shown in the list, is positively correlated with the likelihood of contribution but not with the amount of donation, using a sub-sample of those who shared the list. The introduction of the system strengthened the community network and role of social image shared by the members. This resulted in a successful fundraising for the school, an unprecedented feat in the history of Japan.