pwc-archive/papers-with-abstracts huggingface dataset:
A Logical Approach to Criminal Case Investigation
Authors: Takanori Ugai, Yusuke Koyanagi, Fumihito Nishino
Date: 2024-02-13 00:00:00

XAI (eXplanable AI) techniques that have the property of explaining the reasons for their conclusions, i.e. explainability or interpretability, are attracting attention. XAI is expected to be used in the development of forensic science and the justice system. In today's forensic and criminal investigation environment, experts face many challenges due to large amounts of data, small pieces of evidence in a chaotic and complex environment, traditional laboratory structures and sometimes inadequate knowledge. All these can lead to failed investigations and miscarriages of justice. In this paper, we describe the application of one logical approach to crime scene investigation. The subject of the application is ``The Adventure of the Speckled Band'' from the Sherlock Holmes short stories. The applied data is the knowledge graph created for the Knowledge Graph Reasoning Challenge. We tried to find the murderer by inferring each person with the motive, opportunity, and method. We created an ontology of motives and methods of murder from dictionaries and dictionaries, added it to the knowledge graph of ``The Adventure of the Speckled Band'', and applied scripts to determine motives, opportunities, and methods.
Ai4Fapar: How artificial intelligence can help to forecast the seasonal earth observation signal
Authors: Filip Sabo, Martin Claverie, Michele Meroni, Arthur Hrast Essenfelder
Date: 2024-02-08 00:00:00
Tasks: Earth Observation, valid

This paper investigated the potential of a multivariate Transformer model to forecast the temporal trajectory of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) for short (1 month) and long horizon (more than 1 month) periods at the regional level in Europe and North Africa. The input data covers the period from 2002 to 2022 and includes remote sensing and weather data for modelling FAPAR predictions. The model was evaluated using a leave one year out cross-validation and compared with the climatological benchmark. Results show that the transformer model outperforms the benchmark model for one month forecasting horizon, after which the climatological benchmark is better. The RMSE values of the transformer model ranged from 0.02 to 0.04 FAPAR units for the first 2 months of predictions. Overall, the tested Transformer model is a valid method for FAPAR forecasting, especially when combined with weather data and used for short-term predictions.
Weather Prediction with Diffusion Guided by Realistic Forecast Processes
Authors: Zhanxiang Hua, Yutong He, Chengqian Ma, Alexandra Anderson-Frey
Date: 2024-02-06 00:00:00
Tasks: Prediction, Weather Forecasting

Weather forecasting remains a crucial yet challenging domain, where recently developed models based on deep learning (DL) have approached the performance of traditional numerical weather prediction (NWP) models. However, these DL models, often complex and resource-intensive, face limitations in flexibility post-training and in incorporating NWP predictions, leading to reliability concerns due to potential unphysical predictions. In response, we introduce a novel method that applies diffusion models (DM) for weather forecasting. In particular, our method can achieve both direct and iterative forecasting with the same modeling framework. Our model is not only capable of generating forecasts independently but also uniquely allows for the integration of NWP predictions, even with varying lead times, during its sampling process. The flexibility and controllability of our model empowers a more trustworthy DL system for the general weather community. Additionally, incorporating persistence and climatology data further enhances our model's long-term forecasting stability. Our empirical findings demonstrate the feasibility and generalizability of this approach, suggesting a promising direction for future, more sophisticated diffusion models without the need for retraining.
Using remotely sensed data for air pollution assessment
Authors: Teresa Bernardino, Maria Alexandra Oliveira, João Nuno Silva
Date: 2024-02-04 00:00:00

Air pollution constitutes a global problem of paramount importance that affects not only human health, but also the environment. The existence of spatial and temporal data regarding the concentrations of pollutants is crucial for performing air pollution studies and monitor emissions. However, although observation data presents great temporal coverage, the number of stations is very limited and they are usually built in more populated areas. The main objective of this work is to create models capable of inferring pollutant concentrations in locations where no observation data exists. A machine learning model, more specifically the random forest model, was developed for predicting concentrations in the Iberian Peninsula in 2019 for five selected pollutants: $NO_2$, $O_3$ $SO_2$, $PM10$, and $PM2.5$. Model features include satellite measurements, meteorological variables, land use classification, temporal variables (month, day of year), and spatial variables (latitude, longitude, altitude). The models were evaluated using various methods, including station 10-fold cross-validation, in which in each fold observations from 10\% of the stations are used as testing data and the rest as training data. The $R^2$, RMSE and mean bias were determined for each model. The $NO_2$ and $O_3$ models presented good values of $R^2$, 0.5524 and 0.7462, respectively. However, the $SO_2$, $PM10$, and $PM2.5$ models performed very poorly in this regard, with $R^2$ values of -0.0231, 0.3722, and 0.3303, respectively. All models slightly overestimated the ground concentrations, except the $O_3$ model. All models presented acceptable cross-validation RMSE, except the $O_3$ and $PM10$ models where the mean value was a little higher (12.5934 $\mu g/m^3$ and 10.4737 $\mu g/m^3$, respectively).
Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction
Authors: Fenghua Ling, Zeyu Lu, Jing-Jia Luo, Lei Bai, Swadhin K. Behera, Dachao Jin, Baoxiang Pan, Huidong Jiang, Toshio Yamagata
Date: 2024-02-02 00:00:00

As our planet is entering into the "global boiling" era, understanding regional climate change becomes imperative. Effective downscaling methods that provide localized insights are crucial for this target. Traditional approaches, including computationally-demanding regional dynamical models or statistical downscaling frameworks, are often susceptible to the influence of downscaling uncertainty. Here, we address these limitations by introducing a diffusion probabilistic downscaling model (DPDM) into the meteorological field. This model can efficiently transform data from 1{\deg} to 0.1{\deg} resolution. Compared with deterministic downscaling schemes, it not only has more accurate local details, but also can generate a large number of ensemble members based on probability distribution sampling to evaluate the uncertainty of downscaling. Additionally, we apply the model to generate a 180-year dataset of monthly surface variables in East Asia, offering a more detailed perspective for understanding local scale climate change over the past centuries.
Source-sink dynamics in a two-patch SI epidemic model with life stages and no recovery from infection
Authors: Jimmy Calvo-Monge, Jorge Arroyo-Esquivel, Alyssa Gehman, Fabio Sanchez
Date: 2024-02-09 00:00:00

This study presents a comprehensive analysis of a two-patch, two-life stage SI model without recovery from infection, focusing on the dynamics of disease spread and host population viability in natural populations. The model, inspired by real-world ecological crises like the decline of amphibian populations due to chytridiomycosis and sea star populations due to Sea Star Wasting Disease, aims to understand the conditions under which a sink host population can present ecological rescue from a healthier, source population. Mathematical and numerical analyses reveal the critical roles of the basic reproductive numbers of the source and sink populations, the maturation rate, and the dispersal rate of juveniles in determining population outcomes. The study identifies conditions for disease-free, endemic, and extinction equilibria in sink populations, emphasizing the potential for ecological and evolutionary mechanisms to facilitate coexistence or recovery. These findings provide insights into managing natural populations affected by disease, with implications for conservation strategies, such as the importance of maintaining reproductively viable refuge populations and considering the effects of dispersal and maturation rates on population recovery. The research underscores the complexity of host-pathogen dynamics in spatially structured environments and highlights the need for multi-faceted approaches to biodiversity conservation in the face of emerging diseases.
Can machine learning predict citizen-reported angler behavior?
Authors: Julia S. Schmid, Sean Simmons, Mark A. Lewis, Mark S. Poesch, Pouria Ramazi
Date: 2024-02-07 00:00:00
Tasks: Management, Prediction

Prediction of angler behaviors, such as catch rates and angler pressure, is essential to maintaining fish populations and ensuring angler satisfaction. Angler behavior can partly be tracked by online platforms and mobile phone applications that provide fishing activities reported by recreational anglers. Moreover, angler behavior is known to be driven by local site attributes. Here, the prediction of citizen-reported angler behavior was investigated by machine-learning methods using auxiliary data on the environment, socioeconomics, fisheries management objectives, and events at a freshwater body. The goal was to determine whether auxiliary data alone could predict the reported behavior. Different spatial and temporal extents and temporal resolutions were considered. Accuracy scores averaged 88% for monthly predictions at single water bodies and 86% for spatial predictions on a day in a specific region across Canada. At other resolutions and scales, the models only achieved low prediction accuracy of around 60%. The study represents a first attempt at predicting angler behavior in time and space at a large scale and establishes a foundation for potential future expansions in various directions.
Local Projections Inference with High-Dimensional Covariates without Sparsity
Authors: Jooyoung Cha
Date: 2024-02-12 00:00:00
Tasks: Causal Inference, Model Selection

This paper presents a comprehensive local projections (LP) framework for estimating future responses to current shocks, robust to high-dimensional controls without relying on sparsity assumptions. The approach is applicable to various settings, including impulse response analysis and difference-in-differences (DiD) estimation. While methods like LASSO exist, they often assume most parameters are exactly zero, limiting their effectiveness in dense data generation processes. I propose a novel technique incorporating high-dimensional covariates in local projections using the Orthogonal Greedy Algorithm with a high-dimensional AIC (OGA+HDAIC) model selection method. This approach offers robustness in both sparse and dense scenarios, improved interpretability, and more reliable causal inference in local projections. Simulation studies show superior performance in dense and persistent scenarios compared to conventional LP and LASSO-based approaches. In an empirical application to Acemoglu, Naidu, Restrepo, and Robinson (2019), I demonstrate efficiency gains and robustness to a large set of controls. Additionally, I examine the effect of subjective beliefs on economic aggregates, demonstrating robustness to various model specifications. A novel state-dependent analysis reveals that inflation behaves more in line with rational expectations in good states, but exhibits more subjective, pessimistic dynamics in bad states.
The Benefits from Bundling Demand in K-12 Broadband Procurement
Authors: Gaurab Aryal, Charles Murry, Pallavi Pal, Arnab Palit
Date: 2024-02-11 00:00:00

We study a new market design for K-12 schools' internet procurement. In 2014, New Jersey switched from school-specific decentralized procurement to a new system that bundled participating schools into four regional groups. Using an event study approach, we estimate that participation in this new procurement system reduced K-12 schools' internet prices by approximately $10 per megabit per second per month (Mbps) 37% relative to baseline levels, while increasing bandwidth by 500% Mbps. Relying on a feature of the procurement design, we present evidence that prices fell because bundling mitigated the exposure problem in internet procurement -- the risk that suppliers win contracts insufficient to cover their fixed infrastructure costs. Our findings are robust to deviations from the parallel trend assumption that may arise in our setting because of voluntary participation. Bounding the change in school expenditures due to the program, we find that participants saved at least as much as their federal E-rate subsidy. Using the estimated price reduction and bandwidth expansion, we calculate substantial welfare gains under minimal assumptions on schools' demand for broadband.
Spillover Effects of US Monetary Policy on Emerging Markets Amidst Uncertainty
Authors: Povilas Lastauskas, Anh Dinh Minh Nguyen
Date: 2024-02-11 00:00:00

This paper examines the impact of US monetary policy tightening on emerging markets, distinguishing between direct and indirect spillover effects using the global vector autoregression with stochastic volatility covering 32 countries. The paper demonstrates that an increase in the US interest rate significantly reduces output for emerging markets, leading to larger, more prolonged, and persistent declines. Such an impact is further intensified by global trade integration, causing a sharper yet slightly quicker rebounding output drop. The spillover effects are significantly amplified when US monetary policy tightening is accompanied by an increase in monetary policy uncertainty. Finally, emerging markets exhibit considerable heterogeneity in their responses to US monetary policy shocks.
Scientific Talent Leaks Out of Funding Gaps
Authors: Wei Yang Tham, Joseph Staudt, Elisabeth Ruth Perlman, Stephanie D. Cheng
Date: 2024-02-11 00:00:00

We study how delays in NIH grant funding affect the career outcomes of research personnel. Using comprehensive earnings and tax records linked to university transaction data along with a difference-in-differences design, we find that a funding interruption of more than 30 days has a substantial effect on job placements for personnel who work in labs with a single NIH R01 research grant, including a 3 percentage point (40%) increase in the probability of not working in the US. Incorporating information from the full 2020 Decennial Census and data on publications, we find that about half of those induced into nonemployment appear to permanently leave the US and are 90% less likely to publish in a given year, with even larger impacts for trainees (postdocs and graduate students). Among personnel who continue to work in the US, we find that interrupted personnel earn 20% less than their continuously-funded peers, with the largest declines concentrated among trainees and other non-faculty personnel (such as staff and undergraduates). Overall, funding delays account for about 5% of US nonemployment in our data, indicating that they have a meaningful effect on the scientific labor force at the national level.
Transporte Aereo: Economia e Politicas Publicas
Authors: Alessandro V. M. Oliveira
Date: 2024-02-11 00:00:00

This book, written in Portuguese, presents a comprehensive analysis of the air transport industry in Brazil, highlighting its vital importance to the country's economy. It explores the sector's complexity, from economic characteristics to interaction with the national aeronautical industry, through the specialization of the workforce and market demand analysis. The book delves into the economic regulation of air transport, tracing the evolution from periods of strict regulation to phases of liberalization and deregulation, and examines market dynamics, focusing on concentration and competitiveness. It also analyzes demand and supply through case studies, investigating everything from tourists traveling within Brazil to the coverage of the national territory and the prices of air tickets. Finally, the book proposes principles for the regulation and public policies of the air sector, emphasizing the priority of the passenger, the business environment, access to air transport, and economic efficiency, culminating in the advocacy for a free market, but with protection for competition and the consumer.
Intermodal competition in the Brazilian interstate travel market
Authors: Frederico A. Turolla, Moises D. Vassallo, Alessandro V. M. Oliveira
Date: 2024-02-11 00:00:00

This paper presents a test of intermodal interaction between coaches and airlines in Brazil in order to check for the efficacy of recent liberalization measures designed to promote competition in both industries. Interstate travel service in the country is heavily provided by coaches, and the system is fully operated by the private sector under public delegation through permits and authorizations. Agency-based regulation was introduced in 2002 along with a price cap regime aimed at enhancing the flexibility to change fares in response to demand and cost conditions. By making use of a reaction function-based model of coach operators' pricing decisions in the interstate travel market, we then estimate the sensitivity of the changes in coach fares to the changes in airline fares in a simultaneous-equation framework. Intermodal interaction among coach operators and airlines is found to be highly significant and probably due to the competition for a small but increasing set of premium, quality-sensitive, coach passengers.
Econometric analysis to estimate the impact of holidays on airfares
Authors: Helena Povoa, Alessandro V. M. Oliveira
Date: 2024-02-11 00:00:00

The number of air transportation passengers during the holidays in Brazil has grown notably since the late nineties. One of the reasons is greater competition in airfares made possible by economic liberalization. This paper presents an econometric model of airline pricing aiming at estimating the impacts of holiday periods on fares, with special emphasis on three-day holiday events. It makes use of a database with daily collected data from the internet between 2008 and 2010 for the major Brazilian city, Sao Paulo. The econometric panel data model employs a two-way error components "within" estimator, controlling for airline/airport-pair fixed effect along with quotation and departure months effects. The decomposition of time effects between quotation and departure month effects is the main methodological contribution of the paper. Results allow for a comparative analysis of the performance of Sao Paulo's downtown and international airports - respectively, Congonhas (CGH), and Guarulhos (GRU) airports. As a result, the price of tickets bought 60 days in advance for flights with two stops leaving from the downtown airport fell by most.
Measuring the Dunkelflaute: How (not) to analyze variable renewable energy shortage
Authors: Martin Kittel, Wolf-Peter Schill
Date: 2024-02-09 00:00:00

As variable renewable energy sources increasingly gain importance in global energy systems, there is a growing interest in understanding periods of variable renewable energy shortage ("Dunkelflauten"). Defining, quantifying, and comparing such shortage events across different renewable generation technologies and locations presents a surprisingly intricate challenge. Various methodological approaches exist in different bodies of literature, which have been applied to single technologies in specific locations or technology portfolios across multiple regions. We provide an overview of various methods for quantifying variable renewable energy shortage, focusing either on supply from variable renewables or its mismatch with electricity demand. We explain and critically discuss the merits and challenges of different approaches for defining and identifying shortage events and propose further methodological improvements for more accurate shortage determination. Additionally, we elaborate on comparability requirements for multi-technological and multi-regional energy shortage analysis. In doing so, we aim to contribute to unifying disparate methodologies, harmonizing terminologies, and providing guidance for future research.
Comparative Statics for Optimal Stopping Problems in Nonstationary Environments
Authors: Théo Durandard, Matteo Camboni
Date: 2024-02-10 00:00:00

How do decisions change with the economic environment and with time? This paper studies general nonstationary stopping problems and provides the methodological tools to answer these questions. First, we identify conditions that ensure a monotone relation between decisions' timing and outcomes. These conditions apply to a prevalent class of economic environments. Second, we develop a theory of monotone comparative statics for stopping problems, offering general and unifying qualitative insights into the decision-maker's value and stopping behavior. We apply our results to models of information acquisition, bankruptcy, irreversible investment, and option pricing to explain documented patterns at odds with current theories.
Perfect Bayesian Persuasion
Authors: Elliot Lipnowski, Doron Ravid, Denis Shishkin
Date: 2024-02-09 00:00:00

A sender commits to an experiment to persuade a receiver. Accounting for the sender's experiment-choice incentives, and not presupposing a receiver tie-breaking rule when indifferent, we characterize when the sender's equilibrium payoff is unique and so coincides with her "Bayesian persuasion" value. A sufficient condition in finite models is that every action which is receiver-optimal at some belief is uniquely optimal at some other belief -- a generic property. We similarly show the equilibrium sender payoff is typically unique in ordered models. In an extension, we show uniqueness generates robustness to imperfect sender commitment.
Estimation of non-uniform motion blur using a patch-based regression convolutional neural network (CNN)
Authors: Luis G. Varela, Laura E. Boucheron, Steven Sandoval, David Voelz, Abu Bucker Siddik
Date: 2024-02-12 00:00:00

The non-uniform blur of atmospheric turbulence can be modeled as a superposition of linear motion blur kernels at a patch level. We propose a regression convolutional neural network (CNN) to predict angle and length of a linear motion blur kernel for varying sized patches. We analyze the robustness of the network for different patch sizes and the performance of the network in regions where the characteristics of the blur are transitioning. Alternating patch sizes per epoch in training, we find coefficient of determination scores across a range of patch sizes of $R^2>0.78$ for length and $R^2>0.94$ for angle prediction. We find that blur predictions in regions overlapping two blur characteristics transition between the two characteristics as overlap changes. These results validate the use of such a network for prediction of non-uniform blur characteristics at a patch level.