pwc-archive/papers-with-abstracts huggingface dataset:
Advancements in Electric Vehicle Charging Optimization: A Survey of Reinforcement Learning Approaches
Authors: Mehrshad Shokati, Parisa Mohammadi, Atoosa Amirinian
Date: 2024-10-21 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

In response to global warming and energy shortages, there has been a significant shift towards integrating renewable energy sources, energy storage systems, and electric vehicles. Deploying electric vehicles within smart grids offers a promising solution to reduce carbon emissions. However, managing the charging and discharging processes of them as distributed power supplies present significant challenges. Additionally, the intermittent nature of renewable energy, uncertainties in electric vehicle-related parameters, fluctuating energy prices, and varying loads make maintaining stable power system operations more complex. Effective management systems for electric vehicle battery charging are crucial to coordinating these processes and ensuring a secure, efficient, and reliable power system. Reinforcement learning, enhanced by deep learning, has gained substantial interest for its model-free approach and real-time optimization, effectively managing electric vehicle charging by maximizing cumulative rewards. This review synthesizes existing literature on reinforcement learning-based frameworks, objectives, and architectures for electric vehicle charging coordination strategies in power systems, classifying methods into centralized and decentralized categories. Additionally, the article offers suggestions for future research directions to further enhance reinforcement learning-based electric vehicle charging optimization.
Advancing Gasoline Consumption Forecasting: A Novel Hybrid Model Integrating Transformers, LSTM, and CNN
Authors: Mahmoud Ranjbar, Mohammad Rahimzadeh
Date: 2024-10-20 00:00:00
Tasks: Management

Iran, endowed with abundant hydrocarbon resources, plays a crucial role in the global energy landscape. Gasoline, as a critical fuel, significantly supports the nation's transportation sector. Accurate forecasting of gasoline consumption is essential for strategic resource management and environmental planning. This research introduces a novel approach to predicting monthly gasoline consumption using a hybrid Transformer-LSTM-CNN model, which integrates the strengths of Transformer networks, Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNN). This advanced architecture offers a superior alternative to conventional methods such as artificial neural networks and regression models by capturing both short- and long-term dependencies in time series data. By leveraging the self-attention mechanism of Transformers, the temporal memory of LSTMs, and the local pattern detection of CNNs, our hybrid model delivers improved prediction accuracy. Implemented using Python, the model provides precise future gasoline consumption forecasts and evaluates the environmental impact through the analysis of greenhouse gas emissions. This study examines gasoline consumption trends from 2007 to 2021, which rose from 64.5 million liters per day in 2007 to 99.80 million liters per day in 2021. Our proposed model forecasts consumption levels up to 2031, offering a valuable tool for policymakers and energy analysts. The results highlight the superiority of this hybrid model in improving the accuracy of gasoline consumption forecasts, reinforcing the need for advanced machine learning techniques to optimize resource management and mitigate environmental risks in the energy sector.
Kendall Correlation Coefficients for Portfolio Optimization
Authors: Tomas Espana, Victor Le Coz, Matteo Smerlak
Date: 2024-10-22 00:00:00
Tasks: Portfolio Optimization

Markowitz's optimal portfolio relies on the accurate estimation of correlations between asset returns, a difficult problem when the number of observations is not much larger than the number of assets. Using powerful results from random matrix theory, several schemes have been developed to "clean" the eigenvalues of empirical correlation matrices. By contrast, the (in practice equally important) problem of correctly estimating the eigenvectors of the correlation matrix has received comparatively little attention. Here we discuss a class of correlation estimators generalizing Kendall's rank correlation coefficient which improve the estimation of both eigenvalues and eigenvectors in data-poor regimes. Using both synthetic and real financial data, we show that these generalized correlation coefficients yield Markowitz portfolios with lower out-of-sample risk than those obtained with rotationally invariant estimators. Central to these results is a property shared by all Kendall-like estimators but not with classical correlation coefficients: zero eigenvalues only appear when the number of assets becomes proportional to the square of the number of data points.
Tipping points in fitness landscape of heterogeneous populations
Authors: Sumana Bhattacharyya, Uttam Singh, Anupam Sengupta
Date: 2024-10-23 00:00:00

Predicting fitness of biologically-active populations, communities or systems in fluctuating environments is a long-standing challenge. Phenotypic plasticity and bet-hedging strategy, two key evolutionary traits living systems harness to optimize fitness in dynamic environments, have been widely reported yet how interplays therein could mediate fitness landscapes of heterogeneous populations remain unknown. Leveraging the financial asset pricing model, here we provide a dynamical framework for fitness of heterogeneous populations, underpinned by the interrelations between sub-populations exhibiting phenotypic plasticity and bet-hedgeding. Our framework, independent of the definition of fitness, employs a nonlinear difference equation to present fitness dynamics, and capture the emergence of tipping points, marking the onset of critical state transitions which lead to catastrophic shifts. This study identifies limits on the selective advantage conferred by bet-hedging through reduction in the temporal variance of fitness, with far-reaching ramifications on our current understanding of hedging-mediated fitness enhancement of a population. The lower bound of the effective fitness variance is set by a maximum number of bet-hedgers, beyond which the fitness landscape approaches critical transition, as confirmed by critical slowing down in the vicinity of tipping points. We estimate the scaling law for the critical slowing down numerically and derive the characteristic recovery time for heterogeneous populations. Taken together, our work provides a generic theoretical framework to quantify fitness dynamics and predict critical transitions in heterogeneous populations. The results can be extended further to model fitness landscapes of natural and synthetic multi-species consortia exposed to environmental fluctuations mimicking climatic shifts and immunopathological settings.
Holistic structure of neural pathways underlies brain perceptual rivalry: Physical mechanism of auditory stream segregation
Authors: Yuxuan Wu, Jinling Gao, Xiaona Fang, Jin Wang
Date: 2024-10-23 00:00:00

Brain perceptual rivalry, exemplified by auditory stream segregation of competing tones (A_, B__, ABA_), serves as a core mechanism of brain perception formation. While increasingly recognized as determining by neural connections rather than specific neural groups, the mechanism of brain perception remains uncertain. We demonstrate that auditory stream segregation arises from the topological structure of holistic neural pathways. By constructing a holistic pathway model using existing neurophysiological data, combining nonlinear neural dynamics and nonequilibrium physics, we uncover the biophysical mechanism of perceptual phase transitions from integrated (ABA_) to segregated streams (A_ or B_), as well as the mechanism of temporal dynamics, perceptual switching path, and attention regulation underlying these transitions. Further, we demonstrate how our framework reveals energy consumption of the auditory system and combines it with neuroelectrophysiology. Two psycho-acoustic experiments validate our predictions of perception alternation and attention modulation. Our framework provides a transformative perspective on how brain networks generate complex perceptual experiences, emphasizing the significance of neural pathway structure in the process of brain function realization.
Exploring how deep learning decodes anomalous diffusion via Grad-CAM
Authors: Jaeyong Bae, Yongjoo Baek, Hawoong Jeong
Date: 2024-10-21 00:00:00
Tasks: Deep Learning

While deep learning has been successfully applied to the data-driven classification of anomalous diffusion mechanisms, how the algorithm achieves the feat still remains a mystery. In this study, we use a well-known technique aimed at achieving explainable AI, namely the Gradient-weighted Class Activation Map (Grad-CAM), to investigate how deep learning (implemented by ResNets) recognizes the distinctive features of a particular anomalous diffusion model from the raw trajectory data. Our results show that Grad-CAM reveals the portions of the trajectory that hold crucial information about the underlying mechanism of anomalous diffusion, which can be utilized to enhance the robustness of the trained classifier against the measurement noise. Moreover, we observe that deep learning distills unique statistical characteristics of different diffusion mechanisms at various spatiotemporal scales, with larger-scale (smaller-scale) features identified at higher (lower) layers.
Disease Outbreak Detection and Forecasting: A Review of Methods and Data Sources
Authors: Ghazaleh Babanejaddehaki, Aijun An, Manos Papagelis
Date: 2024-10-21 00:00:00
Tasks: Marketing

Infectious diseases occur when pathogens from other individuals or animals infect a person, resulting in harm to both individuals and society as a whole. The outbreak of such diseases can pose a significant threat to human health. However, early detection and tracking of these outbreaks have the potential to reduce the mortality impact. To address these threats, public health authorities have endeavored to establish comprehensive mechanisms for collecting disease data. Many countries have implemented infectious disease surveillance systems, with the detection of epidemics being a primary objective. The clinical healthcare system, local/state health agencies, federal agencies, academic/professional groups, and collaborating governmental entities all play pivotal roles within this system. Moreover, nowadays, search engines and social media platforms can serve as valuable tools for monitoring disease trends. The Internet and social media have become significant platforms where users share information about their preferences and relationships. This real-time information can be harnessed to gauge the influence of ideas and societal opinions, making it highly useful across various domains and research areas, such as marketing campaigns, financial predictions, and public health, among others. This article provides a review of the existing standard methods developed by researchers for detecting outbreaks using time series data. These methods leverage various data sources, including conventional data sources and social media data or Internet data sources. The review particularly concentrates on works published within the timeframe of 2015 to 2022.
A Bayesian Perspective on the Maximum Score Problem
Authors: Christopher D. Walker
Date: 2024-10-22 00:00:00
Tasks: Bayesian Inference

This paper presents a Bayesian inference framework for a linear index threshold-crossing binary choice model that satisfies a median independence restriction. The key idea is that the model is observationally equivalent to a probit model with nonparametric heteroskedasticity. Consequently, Gibbs sampling techniques from Albert and Chib (1993) and Chib and Greenberg (2013) lead to a computationally attractive Bayesian inference procedure in which a Gaussian process forms a conditionally conjugate prior for the natural logarithm of the skedastic function.
General Seemingly Unrelated Local Projections
Authors: Florian Huber, Christian Matthes, Michael Pfarrhofer
Date: 2024-10-22 00:00:00
Tasks: Missing Values

We provide a framework for efficiently estimating impulse response functions with Local Projections (LPs). Our approach offers a Bayesian treatment for LPs with Instrumental Variables, accommodating multiple shocks and instruments per shock, accounts for autocorrelation in multi-step forecasts by jointly modeling all LPs as a seemingly unrelated system of equations, defines a flexible yet parsimonious joint prior for impulse responses based on a Gaussian Process, allows for joint inference about the entire vector of impulse responses, and uses all available data across horizons by imputing missing values.
Identifying Conduct Parameters with Separable Demand: A Counterexample to Lau (1982)
Authors: Yuri Matsumura, Suguru Otani
Date: 2024-10-22 00:00:00

We provide a counterexample to the conduct parameter identification result established in the foundational work of Lau (1982), which generalizes the identification theorem of Bresnahan (1982) by relaxing the linearity assumptions. We identify a separable demand function that still permits identification and validate this case both theoretically and through numerical simulations.
Conformal Predictive Portfolio Selection
Authors: Masahiro Kato
Date: 2024-10-19 00:00:00
Tasks: Conformal Prediction, Prediction Intervals

This study examines portfolio selection using predictive models for portfolio returns. Portfolio selection is a fundamental task in finance, and a variety of methods have been developed to achieve this goal. For instance, the mean-variance approach constructs portfolios by balancing the trade-off between the mean and variance of asset returns, while the quantile-based approach optimizes portfolios by considering tail risk. These methods often depend on distributional information estimated from historical data using predictive models, each of which carries its own uncertainty. To address this, we propose a framework for predictive portfolio selection via conformal prediction , called \emph{Conformal Predictive Portfolio Selection} (CPPS). Our approach forecasts future portfolio returns, computes the corresponding prediction intervals, and selects the portfolio of interest based on these intervals. The framework is flexible and can accommodate a wide range of predictive models, including autoregressive (AR) models, random forests, and neural networks. We demonstrate the effectiveness of the CPPS framework by applying it to an AR model and validate its performance through empirical studies, showing that it delivers superior returns compared to simpler strategies.
Marine Microplastics and Infant Health
Authors: Xinming Du, Shan Zhang, Eric Zou
Date: 2024-10-22 00:00:00

A century of plastic usage has led to an accumulation of plastic waste in waterways and oceans. Over time, these wastes break down into particles smaller than 5 microns -- or ''microplastics'' -- which can infiltrate human biological systems. Despite decades of research into this emerging source of environmental pollution, there is a paucity of direct evidence on the health impacts of microplastics exposure at a population scale. This paper reports the first empirical link between in-utero microplastic exposure and adverse birth outcomes. Our analysis is based on a compiled dataset of 3 million births that occurred in coastal areas of 15 countries spanning four continents, which we merge with a novel remote-sensing measurements of marine microplastic concentrations. We show that in-utero exposure to microplastics, particularly during the third trimester of pregnancy, leads to a significant increase in the likelihood of low birth weight. A doubling of exposure increases low birth weight hazard by 0.37 per 1,000 births, which implies over 205,000 cases per year globally can be attributed to microplastic exposure. We further show that aerosolization -- whereby microplastic particles become airborne and inhalable due to seawater evaporation -- is an important pathway for health impact, a challenge that is likely to escalate as ocean temperatures continue to rise.
Estimating Spillovers from Sampled Connections
Authors: Kieran Marray
Date: 2024-10-22 00:00:00

Empirical researchers often estimate spillover effects by fitting linear or non-linear regression models to sampled network data. Here, we show that common sampling schemes induce dependence between observed and unobserved spillovers. Due to this dependence, spillover estimates are biased, often upwards. We then show how researchers can construct unbiased estimates of spillover effects by rescaling using aggregate network statistics. Our results can be used to bound true effect sizes, determine robustness of estimates to missingness, and construct estimates when missingness depends on treatment. We apply our results to re-estimate the propagation of idiosyncratic shocks between US public firms, and peer effects amongst USAFA cadets.
Nonparametric Estimation of Matching Efficiency and Elasticity on a Private On-the-Job Search Platform: Evidence from Japan, 2014-2024
Authors: Suguru Otani
Date: 2024-10-22 00:00:00

I use proprietary data from the online job scouting platform BizReach in Japan, spanning from 2014 to 2024, to estimate the matching function for high-skill employed workers on a private on-the-job search platform, employing a novel nonparametric approach developed by Lange and Papageorgiou (2020). This analysis is compared to the public off-the-job search platform, Hello Work. The results indicate that matching efficiency on the private platform is both more volatile and higher than that of the public platform, suggesting the increasing popularity of the private platform. The matching elasticity with respect to users consistently hovers around 0.75, while the elasticity with respect to vacancies reaches approximately 1.0, indicating a higher and more balanced elasticity compared to the Hello Work platform. Additionally, the study reveals evidence of industry-level heterogeneity on the private platform.
On Regularity and Normalization in Sequential Screening
Authors: Ian Ball, Teemu Pekkarinen
Date: 2024-10-23 00:00:00

We comment on the regularity assumptions in the multi-agent sequential screening model of Eso and Szentes (2007). First, we observe that the regularity assumptions are not invariant to relabeling each agent's signal realizations. Second, we show that the regularity assumptions rule out valuation distributions with common bounded support. Third, we show that if each signal realization is labeled to equal the expected valuation, then the regularity assumptions imply that each agent's valuation is equal to his signal realization plus independent mean-zero noise.
Strategic Irreversible Investment
Authors: Jan-Henrik Steg
Date: 2024-10-23 00:00:00

This paper studies oligopolistic irreversible investment with closed-loop strategies. These permit fully dynamic interactions that result in much richer strategic behavior than previous studies with open-loop strategies allow. The tradeoff between preemption incentives and the option value of waiting becomes distinctly visible. Strategies that depend on present capital stocks enable credible reactions that deter from excessive preemption and support positive option values in equilibrium. Simpler strategies lead into a "preemption trap" with perfectly competitive outcome and zero net present values. To obtain these results, a novel concept of Markov perfect equilibrium is developed that copes with optimal investment taking the form of singular control.
Commitment and Randomization in Communication
Authors: Emir Kamenica, Xiao Lin
Date: 2024-10-23 00:00:00

When does Sender, in a Sender-Receiver game, strictly value commitment? In a setting with finitely many actions and states, we establish that, generically, commitment has no value if and only if a partitional experiment is optimal. Moreover, if Sender's preferred cheap-talk equilibrium necessarily involves randomization, then Sender values commitment. Our results imply that if a school values commitment to a grading policy, then the school necessarily prefers to grade unfairly. We also ask: for what share of preference profiles does commitment have no value? For any state space, if there are $\left|A\right|$ actions, the share is at least $\frac{1}{\left|A\right|^{\left|A\right|}}$. As the number of states grows large, the share converges precisely to $\frac{1}{\left|A\right|^{\left|A\right|}}$.
Note on Bubbles Attached to Real Assets
Authors: Tomohiro Hirano, Alexis Akira Toda
Date: 2024-10-22 00:00:00

A rational bubble is a situation in which the asset price exceeds its fundamental value defined by the present value of dividends in a rational equilibrium model. We discuss the recent development of the theory of rational bubbles attached to real assets, emphasizing the following three points. (i) There exist plausible economic models in which bubbles inevitably emerge in the sense that all equilibria are bubbly. (ii) Such models are necessarily nonstationary but their long-run behavior can be analyzed using the local stable manifold theorem. (iii) Bubbles attached to real assets can naturally and necessarily arise with economic development. Finally, we present a model with stocks and land, and show that bubbles in aggregate stock and land prices necessarily emerge.
Improving Automatic Speech Recognition with Decoder-Centric Regularisation in Encoder-Decoder Models
Authors: Alexander Polok, Santosh Kesiraju, Karel Beneš, Lukáš Burget, Jan Černocký
Date: 2024-10-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, speech-recognition, Speech Recognition

This paper proposes a simple yet effective way of regularising the encoder-decoder-based automatic speech recognition (ASR) models that enhance the robustness of the model and improve the generalisation to out-of-domain scenarios. The proposed approach is dubbed as $\textbf{De}$coder-$\textbf{C}$entric $\textbf{R}$egularisation in $\textbf{E}$ncoder-$\textbf{D}$ecoder (DeCRED) architecture for ASR, where auxiliary classifier(s) is introduced in layers of the decoder module. Leveraging these classifiers, we propose two decoding strategies that re-estimate the next token probabilities. Using the recent E-branchformer architecture, we build strong ASR systems that obtained competitive WERs as compared to Whisper-medium and outperformed OWSM v3; while relying only on a fraction of training data and model size. On top of such a strong baseline, we show that DeCRED can further improve the results and, moreover, generalise much better to out-of-domain scenarios, where we show an absolute reduction of 2.7 and 2.9 WERs on AMI and Gigaspeech datasets, respectively. We provide extensive analysis and accompanying experiments that support the benefits of the proposed regularisation scheme.