pwc-archive/papers-with-abstracts huggingface dataset:
Selection of Time Headway in Connected and Autonomous Vehicle Platoons under Noisy V2V Communication
Authors: Guoqi Ma, Prabhakar R. Pagilla, Swaroop Darbha
Date: 2024-04-13 00:00:00

In this paper, we investigate the selection of time headway to ensure robust string stability in connected and autonomous vehicle platoons in the presence of signal noise in Vehicle-to-Vehicle (V2V) communication. In particular, we consider the effect of noise in communicated vehicle acceleration from the predecessor vehicle to the follower vehicle on the selection of the time headway in predecessor-follower type vehicle platooning with a Constant Time Headway Policy (CTHP). Employing a CTHP based control law for each vehicle that utilizes on-board sensors for measurement of position and velocity of the predecessor vehicle and wireless communication network for obtaining the acceleration of the predecessor vehicle, we investigate how time headway is affected by communicated signal noise. We derive constraints on the CTHP controller gains for predecessor acceleration, velocity error and spacing error and a lower bound on the time headway which will ensure robust string stability of the platoon against signal noise. We provide comparative numerical simulations on an example to illustrate the main result.
Benefits of V2V communication in connected and autonomous vehicles in the presence of delays in communicated signals
Authors: Guoqi Ma, Prabhakar R. Pagilla, Swaroop Darbha
Date: 2024-04-13 00:00:00
Tasks: Autonomous Vehicles

In this paper, we investigate the effect of signal delay in communicated information in connected and autonomous vehicles. In particular, we relate this delay's effect on the selection of the time headway in predecessor-follower type vehicle platooning with a constant time headway policy (CTHP). We employ a CTHP control law for each vehicle in the platoon by considering two cases: cooperative adaptive cruise control (CACC) strategy where information from only one predecessor vehicle is employed and CACC+ where information from multiple predecessor vehicles is employed. We investigate how the lower bound on the time headway is affected by signal transmission delay due to wireless communication. We provide a systematic approach to the derivation of the lower bound of the time headway and selection of the appropriate CTHP controller gains for predecessor acceleration, velocity error and spacing error which will ensure robust string stability of the platoon under the presence of signal delay. We corroborate the main result with numerical simulations.
Interferometer measurements in interstellar communications: methods and observations
Authors: William J. Crilly Jr
Date: 2024-04-13 00:00:00

Extraterrestrial communication signals are hypothesized to be present in an extensive search space. Using principles of communication theory and system design, methods are studied and implemented to reduce the signal search space, while considering intentional transmitter detectability. The design and observational work reported in this paper adds material to previous related reports. (ref. arXiv:2105.03727, arXiv:2106.10168, arXiv:2202.12791, arXiv:2203.10065). In the current work, a two-element radio interferometer telescope and receiver algorithms are utilized to perform differential angle-of-arrival and multi-bandwidth measurements of delta-t delta-f polarized pulse pairs. The system enhances extraterrestrial signal detectability, while reducing signal false positives caused by noise and radio frequency interference. Statistical analysis utilizes a Right Ascension filter spanning celestial coordinate ranges that include the previously determined anomalous celestial direction: 5.25 +- 0.15 hr Right Ascension, -7.6 degrees +- 1 degree Declination. Observations were conducted during a duration of 61 days, comprising 244 hours of interferometer measurements.
Prevalence estimation methods for time-dependent antibody kinetics of infected and vaccinated individuals: a graph-theoretic approach
Authors: Prajakta Bedekar, Rayanne A. Luke, Anthony J. Kearsley
Date: 2024-04-13 00:00:00

Immune events such as infection, vaccination, and a combination of the two result in distinct time-dependent antibody responses in affected individuals. These responses and event prevalences combine non-trivially to govern antibody levels sampled from a population. Time-dependence and disease prevalence pose considerable modeling challenges that need to be addressed to provide a rigorous mathematical underpinning of the underlying biology. We propose a time-inhomogeneous Markov chain model for event-to-event transitions coupled with a probabilistic framework for anti-body kinetics and demonstrate its use in a setting in which individuals can be infected or vaccinated but not both. We prove the equivalency of this approach to the framework developed in our previous work. Synthetic data are used to demonstrate the modeling process and conduct prevalence estimation via transition probability matrices. This approach is ideal to model sequences of infections and vaccinations, or personal trajectories in a population, making it an important first step towards a mathematical characterization of reinfection, vaccination boosting, and cross-events of infection after vaccination or vice versa.
The Role of Carbon Pricing in Food Inflation: Evidence from Canadian Provinces
Authors: Jiansong Xu
Date: 2024-04-15 00:00:00
Tasks: Attribute

In the search for political-economic tools for greenhouse gas mitigation, carbon pricing, which includes carbon tax and cap-and-trade, is implemented by many governments. However, the inflating food prices in carbon-pricing countries, such as Canada, have led many to believe such policies harm food affordability. This study aims to identify changes in food prices induced by carbon pricing using the case of Canadian provinces. Using the staggered difference-in-difference (DiD) approach, we find an overall deflationary effect of carbon pricing on food prices (measured by monthly provincial food CPI). The average reductions in food CPI compared to before carbon pricing are $2\%$ and $4\%$ within and beyond two years of implementation. We further find that the deflationary effects are partially driven by lower consumption with no significant change via farm input costs. Evidence in this paper suggests no inflationary effect of carbon pricing in Canadian provinces, thus giving no support to the growing voices against carbon pricing policies.
Julia as a universal platform for statistical software development
Authors: David Roodman
Date: 2024-04-14 00:00:00

The julia package integrates the Julia programming language into Stata. Users can transfer data between Stata and Julia, issue Julia commands to analyze and plot, and pass results back to Stata. Julia's econometric ecosystem is not as mature as Stata's or R's or Python's. But Julia is an excellent environment for developing high-performance numerical applications, which can then be called from many platforms. For example, the boottest program for wild bootstrap-based inference (Roodman et al. 2019) and fwildclusterboot for R (Fischer and Roodman 2021) can use the same Julia back end. And the program reghdfejl mimics reghdfe (Correia 2016) in fitting linear models with high-dimensional fixed effects while calling a Julia package for tenfold acceleration on hard problems. reghdfejl also supports nonlinear fixed-effect models that cannot otherwise be fit in Stata--though preliminarily, as the Julia package for that purpose is immature.
Identifying Causal Effects under Kink Setting: Theory and Evidence
Authors: Yi Lu, Jianguo Wang, Huihua Xie
Date: 2024-04-14 00:00:00

This paper develops a generalized framework for identifying causal impacts in a reduced-form manner under kinked settings when agents can manipulate their choices around the threshold. The causal estimation using a bunching framework was initially developed by Diamond and Persson (2017) under notched settings. Many empirical applications of bunching designs involve kinked settings. We propose a model-free causal estimator in kinked settings with sharp bunching and then extend to the scenarios with diffuse bunching, misreporting, optimization frictions, and heterogeneity. The estimation method is mostly non-parametric and accounts for the interior response under kinked settings. Applying the proposed approach, we estimate how medical subsidies affect outpatient behaviors in China.
Quantifying fair income distribution in Thailand
Authors: Thitithep Sitthiyot, Kanyarat Holasut
Date: 2024-04-15 00:00:00
Tasks: Fairness

Given a vast concern about high income inequality in Thailand as opposed to empirical findings around the world showing people's preference for fair income inequality over unfair income equality, it is therefore important to examine whether inequality in income distribution in Thailand over the past three decades is fair, and what fair inequality in income distribution in Thailand should be. To quantitatively measure fair income distribution, this study employs the fairness benchmarks that are derived from the distributions of athletes' salaries in professional sports which satisfy the concepts of distributive justice and procedural justice, the no-envy principle of fair allocation, and the general consensus or the international norm criterion of a meaningful benchmark. By using the data on quintile income shares and the income Gini index of Thailand from the National Social and Economic Development Council, this study finds that, throughout the period from 1988 to 2021, the Thai income earners in the bottom 20%, the second 20%, and the top 20% receive income shares more than the fair shares whereas those in the third 20% and the fourth 20% receive income shares less than the fair shares. Provided that there are infinite combinations of quintile income shares that can have the same value of income Gini index but only one of them is regarded as fair, this study demonstrates the use of fairness benchmarks as a practical guideline for designing policies with an aim to achieve fair income distribution in Thailand. Moreover, a comparative analysis is conducted by employing the method for estimating optimal (fair) income distribution representing feasible income equality in order to provide an alternative recommendation on what optimal (fair) income distribution characterizing feasible income equality in Thailand should be.
Has Anti-corruption Efforts lowered Enterprises Innovation Efficiency? -An Empirical Analysis from China
Authors: lunwu Liu, Shi Liu
Date: 2024-04-15 00:00:00

This study adopts the fixed effects panel model and provincial panel data on anticorruption and the innovation efficiency of high-level technology and new technology enterprises in China from 2005 to 2014, to estimate the effects of the anticorruption movement on the innovation efficiency of enterprises at different corruption levels. The empirical results show that anticorruption is positively correlated with the innovation efficiency of enterprises; however, the correlation is differentiated according to different corruption levels and business natures. At a high level of corruption, anticorruption has positive impacts on enterprises' innovation; at a low level of corruption, it negatively affects innovation efficiency. However, anticorruption has negative effects on the innovation efficiency of state-owned enterprises at both high and low corruption levels; for nonstate-owned enterprises, its effects are positive at a high corruption level and negative at a low corruption level. The effects remain the same across different regions.
Belief Bias Identification
Authors: Pedro Gonzalez-Fernandez
Date: 2024-04-14 00:00:00

This paper proposes a unified theoretical model to identify and test a comprehensive set of probabilistic updating biases within a single framework. The model achieves separate identification by focusing on the updating of belief distributions, rather than classic point-belief measurements. Testing the model in a laboratory experiment reveals significant heterogeneity at the individual level: All tested biases are present, and each participant exhibits at least one identifiable bias. Notably, motivated-belief biases (optimism and pessimism) and sequence-related biases (gambler's fallacy and hot hand fallacy) are identified as key drivers of biased inference. Moreover, at the population level, base rate neglect emerges as a persistent influence. This study contributes to the belief-updating literature by providing a methodological toolkit for researchers examining links between different conflicting biases, or exploring connections between updating biases and other behavioral phenomena.
ge_gravity2: a command for solving universal gravity models
Authors: Rodolfo G. Campos, Iliana Reggio, Jacopo Timini
Date: 2024-04-14 00:00:00
Tasks: counterfactual

We describe an algorithm for computing counterfactual trade flows, prices, output, and welfare in a large class of general equilibrium trade models. We introduce a command called ge_gravity2 that allows users to perform these computations in Stata. This command extends the existing ge_gravity command by allowing users to compute the general equilibrium effects of changes in trade policy in positive supply elasticity models. It can be used to solve any model that falls into the class of universal gravity models as defined by Allen, Arkolakis, and Takahashi [Universal Gravity, Journal of Political Economy, 128(2), 2020, 393-433].
Minimizing Errors or Surprises?
Authors: Jiaoying Pei
Date: 2024-04-13 00:00:00

Traditional finance and macroeconomic models usually assume people can form rational expectations or reach them via a learning path by minimizing prediction errors. The recent Reference Model Based Learning (RMBL) model provides a new perspective: It hypothesizes that people minimize surprises instead of errors. Following the spirit of Simon's "satisficing" criteria, RMBL predicts that they will minimize errors only when the prediction error exceeds a threshold. We conduct meta-analyses based on 18 Learning-to-Forecast Experiments (LtFEs; N=41,490). Our results from the horse race test consistently show that student participants minimize surprises instead of errors in the LtFEs. In contrast, the results based on the data from the Survey of Professional Forecasters (SPF) show no evidence that they minimize surprises. Together, our results suggest that minimizing surprises by implementing RMBL may be the simple procedure people employ when navigating complexity in forecasting.
Strategic Informed Trading and the Value of Private Information
Authors: Michail Anthropelos, Scott Robertson
Date: 2024-04-12 00:00:00

We consider a market of risky financial assets whose participants are an informed trader, a representative uninformed trader, and noisy liquidity providers. We prove the existence of a market-clearing equilibrium when the insider internalizes her power to impact prices, but the uninformed trader takes prices as given. Compared to the associated competitive economy, in equilibrium the insider strategically reveals a noisier signal, and prices are less reactive to publicly available information. Additionally, and in direct contrast to the related literature, in equilibrium the insider's indirect utility monotonically increases in the signal precision. Therefore, the insider is motivated not only to obtain, but also to refine, her signal. Lastly, we show that compared to the competitive economy, the insider's internalization of price impact is utility improving for the uninformed trader, but somewhat surprisingly may be utility decreasing for the insider herself. This utility reduction occurs provided the insider is sufficiently risk averse compared to the uninformed trader, and provided the signal is of sufficiently low quality.
More, better or different? Trade-offs between group size and competence development in jury theorems
Authors: Gustaf Arrhenius, Klas Markström
Date: 2024-04-15 00:00:00

In many circumstances there is a trade off between the number of voters and the time they can be given before having to make a decision since both aspects are costly. An example is the hiring of a committee with a fixed salary budget: more people but a shorter time for each to develop their competence about the issue at hand or less people with a longer time for competence development? In this paper we investigate the interaction between the number of voters, the development of their competence over time and the final probability for an optimal majority decision. Among other things we consider how different learning profiles, or rates of relevant competence increase, for the members of a committee affects the optimal committee size. To the best of our knowledge, our model is the first that includes the potentially positive effects of having a heterogeneous group of voters on majority decisions in a satisfactory way. We also discuss how some earlier attempts fail to capture the effect of heterogeneity correctly.
Long run consequence of p-hacking
Authors: Xuanye Wang
Date: 2024-04-13 00:00:00

We study the theoretical consequence of p-hacking on the accumulation of knowledge under the framework of mis-specified Bayesian learning. A sequence of researchers, in turn, choose projects that generate noisy information in a field. In choosing projects, researchers need to carefully balance as projects generates big information are less likely to succeed. In doing the project, a researcher p-hacks at intensity $\varepsilon$ so that the success probability of a chosen project increases (unduly) by a constant $\varepsilon$. In interpreting previous results, researcher behaves as if there is no p-hacking because the intensity $\varepsilon$ is unknown and presumably small. We show that over-incentivizing information provision leads to the failure of learning as long as $\varepsilon\neq 0$. If the incentives of information provision is properly provided, learning is correct almost surely as long as $\varepsilon$ is small.
Competition for Budget-Constrained Buyers: Exploring All-Pay Auctions
Authors: Cemil Selcuk
Date: 2024-04-12 00:00:00
Tasks: All

This note pursues two primary objectives. First, we analyze the outcomes of an all-pay auction within a store where buyers with and without financial constraints arrive at varying rates, and where buyer types are private information. Second, we investigate the selection of an auction format (comprising first-price, second-price, and all-pay formats) in a competitive search setting, where sellers try to attract customers. Our results indicate that if the budget constraint is not too restrictive, the all-pay rule emerges as the preferred selling format in the unique symmetric equilibrium. This is thanks to its ability to prompt buyers to submit lower bids, thereby generally avoiding budget constraints, while allowing the seller to collect all bids.
MarsQE: Semantic-Informed Quality Enhancement for Compressed Martian Image
Authors: Chengfeng Liu, Mai Xu, Qunliang Xing, Xin Zou
Date: 2024-04-15 00:00:00
Tasks: Image Compression

Lossy image compression is essential for Mars exploration missions, due to the limited bandwidth between Earth and Mars. However, the compression may introduce visual artifacts that complicate the geological analysis of the Martian surface. Existing quality enhancement approaches, primarily designed for Earth images, fall short for Martian images due to a lack of consideration for the unique Martian semantics. In response to this challenge, we conduct an in-depth analysis of Martian images, yielding two key insights based on semantics: the presence of texture similarities and the compact nature of texture representations in Martian images. Inspired by these findings, we introduce MarsQE, an innovative, semantic-informed, two-phase quality enhancement approach specifically designed for Martian images. The first phase involves the semantic-based matching of texture-similar reference images, and the second phase enhances image quality by transferring texture patterns from these reference images to the compressed image. We also develop a post-enhancement network to further reduce compression artifacts and achieve superior compression quality. Our extensive experiments demonstrate that MarsQE significantly outperforms existing approaches for Earth images, establishing a new benchmark for the quality enhancement on Martian images.
Synthetic Brain Images: Bridging the Gap in Brain Mapping With Generative Adversarial Model
Authors: Drici Mourad, Kazeem Oluwakemi Oseni
Date: 2024-04-11 00:00:00
Tasks: Data Augmentation, Deep Learning, Image Generation

Magnetic Resonance Imaging (MRI) is a vital modality for gaining precise anatomical information, and it plays a significant role in medical imaging for diagnosis and therapy planning. Image synthesis problems have seen a revolution in recent years due to the introduction of deep learning techniques, specifically Generative Adversarial Networks (GANs). This work investigates the use of Deep Convolutional Generative Adversarial Networks (DCGAN) for producing high-fidelity and realistic MRI image slices. The suggested approach uses a dataset with a variety of brain MRI scans to train a DCGAN architecture. While the discriminator network discerns between created and real slices, the generator network learns to synthesise realistic MRI image slices. The generator refines its capacity to generate slices that closely mimic real MRI data through an adversarial training approach. The outcomes demonstrate that the DCGAN promise for a range of uses in medical imaging research, since they show that it can effectively produce MRI image slices if we train them for a consequent number of epochs. This work adds to the expanding corpus of research on the application of deep learning techniques for medical image synthesis. The slices that are could be produced possess the capability to enhance datasets, provide data augmentation in the training of deep learning models, as well as a number of functions are made available to make MRI data cleaning easier, and a three ready to use and clean dataset on the major anatomical plans.
Building an AI Support Tool for Real-time Ulcerative Colitis Diagnosis
Authors: Bjørn Leth Møller, Bobby Zhao Sheng Lo, Johan Burisch, Flemming Bendtsen, Ida Vind, Bulat Ibragimov, Christian Igel
Date: 2024-04-10 00:00:00
Tasks: Decision Making

Ulcerative Colitis (UC) is a chronic inflammatory bowel disease decreasing life quality through symptoms such as bloody diarrhoea and abdominal pain. Endoscopy is a cornerstone of diagnosis and monitoring of UC. The Mayo endoscopic subscore (MES) index is the standard for measuring UC severity during endoscopic evaluation. However, the MES is subject to high inter-observer variability leading to misdiagnosis and suboptimal treatment. We propose using a machine-learning based MES classification system to support the endoscopic process and to mitigate the observer-variability. The system runs real-time in the clinic and augments doctors' decision-making during the endoscopy. This project report outlines the process of designing, creating and evaluating our system. We describe our initial evaluation, which is a combination of a standard non-clinical model test and a first clinical test of the system on a real patient.
Semiconductor Devices Condition Monitoring Using Harmonics in Inverter Control Variables
Authors: Shuyu Ou, Ariya Sangwongwanich, Subham Sahoo, Frede Blaabjerg
Date: 2024-04-15 00:00:00

The health status of power semiconductor devices in power converters is important but difficult to monitor. This paper analyzes the relationship between harmonics in inverter control variables and a health precursor (the on-state voltage Von of power semiconductor devices). Based on the analysis, harmonics can estimate Von without adding extra sensing circuits. The method is validated through simulations.