pwc-archive/papers-with-abstracts huggingface dataset:
Genuinely Robust Inference for Clustered Data
Authors: Harold D. Chiang, Yuya Sasaki, Yulong Wang
Date: 2023-08-20 00:00:00
Tasks: valid

Conventional cluster-robust inference methods are inconsistent when clusters are unignorably large. We derive a necessary and sufficient condition for consistency, which is violated in 77% of empirical studies published in American Economic Review and Econometrica (2020-2021). To address this, we propose two methods: (i) score subsampling, which retains the original estimator, and (ii) size-adjusted reweighting, which is easy to implement in software like Stata and remains valid if the cluster size follows Zipf's law. Simulations confirm the reliability and uniform size control of these approaches, offering robust alternatives where conventional methods fail.
Exploring the Role of Perceived Range Anxiety in Adoption Behavior of Plug-in Electric Vehicles
Authors: Fatemeh Nazari, Abolfazl Mohammadian
Date: 2023-08-20 00:00:00

A sustainable solution to negative externalities imposed by road transportation is replacing internal combustion vehicles with electric vehicles (EVs), especially plug-in EV (PEV) encompassing plug-in hybrid EV (PHEV) and battery EV (BEV). However, EV market share is still low and is forecast to remain low and uncertain. This shows a research need for an in-depth understanding of EV adoption behavior with a focus on one of the main barriers to the mass EV adoption, which is the limited electric driving range. The present study extends the existing literature in two directions; First, the influence of the psychological aspect of driving range, which is referred to as range anxiety, is explored on EV adoption behavior by presenting a nested logit (NL) model with a latent construct. Second, the two-level NL model captures individuals' decision on EV adoption behavior distinguished by vehicle transaction type and EV type, where the upper level yields the vehicle transaction type selected from the set of alternatives including no-transaction, sell, trade, and add. The fuel type of the vehicles decided to be acquired, either as traded-for or added vehicles, is simultaneously determined at the lower level from a set including conventional vehicle, hybrid EV, PHEV, and BEV. The model is empirically estimated using a stated preferences dataset collected in the State of California. A notable finding is that anxiety about driving range influences the preference for BEV, especially as an added than traded-for vehicle, but not the decision on PHEV adoption.
Agree to Disagree: Measuring Hidden Dissent in FOMC Meetings
Authors: Kwok Ping Tsang, Zichao Yang
Date: 2023-08-20 00:00:00

Using FOMC votes and meeting transcripts from 1976-2018, we develop a deep learning model based on self-attention mechanism to quantify hidden dissent among members. Although explicit dissent is rare, we find that members often have reservations with the policy decision, and hidden dissent is mostly driven by current or predicted macroeconomic data. Additionally, hidden dissent strongly correlates with data from the Summary of Economic Projections and a measure of monetary policy sub-optimality, suggesting it reflects both divergent preferences and differing economic outlooks among members. Finally, financial markets show an immediate response to the hidden dissent disclosed through meeting minutes.
Green or greedy: the relationship between perceived benefits and homeowners' intention to adopt residential low-carbon technologies
Authors: Fabian Scheller, Karyn Morrissey, Karsten Neuhoff, Dogan Keles
Date: 2023-08-19 00:00:00
Tasks: Decision Making

Transitioning to a net-zero economy requires a nuanced understanding of homeowners decision-making pathways when considering the adoption of Low Carbon Technologies (LCTs). These LCTs present both personal and collective benefits, with positive perceptions critically influencing attitudes and intentions. Our study analyses the relationship between two primary benefits: the household-level financial gain and the broader environmental advantage. Focusing on the intention to adopt Rooftop Photovoltaic Systems, Energy Efficient Appliances, and Green Electricity Tariffs, we employ Partial Least Squares Structural Equation Modeling to demonstrate that the adoption intention of the LCTs is underpinned by the Theory of Planned Behaviour. Attitudes toward the LCTs are more strongly related to product-specific benefits than affective constructs. In terms of evaluative benefits, environmental benefits exhibit a higher positive association with attitude formation compared to financial benefits. However, this relationship switches as homeowners move through the decision process with the financial benefits of selected LCTs having a consistently higher association with adoption intention. At the same time, financial benefits also positively affect attitudes. Observing this trend across both low- and high-cost LCTs, we recommend that policymakers amplify homeowners' recognition of the individual benefits intrinsic to LCTs and enact measures that ensure these financial benefits.
Startup Acquisitions: Acquihires and Talent Hoarding
Authors: Jean-Michel Benkert, Igor Letina, Shuo Liu
Date: 2023-08-19 00:00:00

We study how competitive forces may drive firms to inefficiently acquire startup talent. In our model, two rival firms have the capacity to acquire and integrate a startup operating in an orthogonal market. We show that firms may pursue such acquihires primarily as a preemptive strategy, even when they appear unprofitable in isolation. Thus, acquihires, even absent traditional competition-reducing effects, need not be benign, as they can lead to inefficient talent allocation. Additionally, our analysis underscores that such talent hoarding can diminish consumer surplus and exacerbate job volatility for acquihired employees.
Do We Price Happiness? Evidence from Korean Stock Market
Authors: HyeonJun Kim
Date: 2023-08-19 00:00:00

This study explores the potential of internet search volume data, specifically Google Trends, as an indicator for cross-sectional stock returns. Unlike previous studies, our research specifically investigates the search volume of the topic 'happiness' and its impact on stock returns in the aspect of risk pricing rather than as sentiment measurement. Empirical results indicate that this 'happiness' search exposure (HSE) can explain future returns, particularly for big and value firms. This suggests that HSE might be a reflection of a firm's ability to produce goods or services that meet societal utility needs. Our findings have significant implications for institutional investors seeking to leverage HSE-based strategies for outperformance. Additionally, our research suggests that, when selected judiciously, some search topics on Google Trends can be related to risks that impact stock prices.
On the parametric description of log-growth rates of cities' sizes of four European countries and the USA
Authors: Till Massing, Miguel Puente-Ajovín, Arturo Ramos
Date: 2023-08-19 00:00:00

We have studied the parametric description of the distribution of the log-growth rates of the sizes of cities of France, Germany, Italy, Spain and the USA. We have considered several parametric distributions well known in the literature as well as some others recently introduced. There are some models that provide similar excellent performance, for all studied samples. The normal distribution is not the one observed empirically.
The Distribution of Strike Size:Empirical Evidence from Europe and North America in the 19th and 20th Centuries
Authors: Michele Campolieti, Arturo Ramos
Date: 2023-08-19 00:00:00

We study the distribution of strike size, which we measure as lost person days, for a long period in several countries of Europe and America. When we consider the full samples, the mixtures of two or three lognormals arise as very convenient models. When restricting to the upper tails, the Pareto power law becomes almost indistinguishable of the truncated lognormal.
Student't mixture models for stock indices. A comparative study
Authors: Till Massing, Arturo Ramos
Date: 2023-08-19 00:00:00

We perform a comparative study for multiple equity indices of different countries using different models to determine the best fit using the Kolmogorov-Smirnov statistic, the Anderson-Darling statistic, the Akaike information criterion and the Bayesian information criteria as goodness-of-fit measures. We fit models both to daily and to hourly log-returns. The main result is the excellent performance of a mixture of three Student's $t$ distributions with the numbers of degrees of freedom fixed a priori (3St). In addition, we find that the different components of the 3St mixture with small/moderate/high degree of freedom parameter describe the extreme/moderate/small log-returns of the studied equity indices.
Is there a universal parametric city size distribution? Empirical evidence for 70 countries
Authors: Miguel Puente-Ajovín, Arturo Ramos, Fernando Sanz-Gracia
Date: 2023-08-19 00:00:00

We study the parametric description of the city size distribution (CSD) of 70 different countries (developed and developing) using seven models, as follows: the lognormal (LN), the loglogistic (LL), the double Pareto lognormal (dPLN), the two-lognormal (2LN), the two-loglogistic (2LL), the three-lognormal (3LN) and the three-loglogistic (3LL). Our results show that 3LN and 3LL are the best densities in terms of non-rejections out of standard statistical tests. Meanwhile, according to the information criteria AIC and BIC, there is no systematically dominant distribution.
Managers' Choice of Disclosure Complexity
Authors: Jeremy Bertomeu
Date: 2023-08-18 00:00:00
Tasks: counterfactual

Aghamolla and Smith (2023) make a significant contribution to enhancing our understanding of how managers choose financial reporting complexity. I outline the key assumptions and implications of the theory, and discuss two empirical implications: (1) a U-shaped relationship between complexity and returns, and (2) a negative association between complexity and investor sophistication. However, the robust equilibrium also implies a counterfactual positive market response to complexity. I develop a simplified approach in which simple disclosures indicate positive surprises, and show that this implies greater investor skepticism toward complexity and a positive association between investor sophistication and complexity. More work is needed to understand complexity as an interaction of reporting and economic transactions, rather than solely as a reporting phenomenon.
Central Bank Digital Currency with Collateral-constrained Banks
Authors: Hanfeng Chen, Maria Elena Filippin
Date: 2023-08-20 00:00:00

We analyze the risks to bank intermediation following the introduction of a central bank digital currency (CBDC) competing with commercial bank deposits as households' source of liquidity. We revisit the result in the literature regarding the equivalence of payment systems introducing a collateral constraint on banks borrowing from the central bank. Comparing two equilibria with and without the CBDC, we find that even with this constraint, the central bank can ensure the same equilibrium allocation and price system by offering loans to banks. However, to access loans, banks must hold collateral at the expense of extending credit to firms. Thus, while the CBDC introduction has no real effects on the economy, it does not guarantee full neutrality as it affects banks' business models. In a dynamic model extension, we examine the effects of an increase in the CBDC and show that the CBDC does not cause bank disintermediation or crowd out deposits but may foster an expansion of bank credit to firms.
An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR
Authors: Chenwei Wang, Siyi Luo, Jifang Pei, Xiaoyu Liu, Yulin Huang, Yin Zhang, Jianyu Yang
Date: 2023-08-20 00:00:00
Tasks: Meta-Learning, Open Set Learning

Existing synthetic aperture radar automatic target recognition (SAR ATR) methods have been effective for the classification of seen target classes. However, it is more meaningful and challenging to distinguish the unseen target classes, i.e., open set recognition (OSR) problem, which is an urgent problem for the practical SAR ATR. The key solution of OSR is to effectively establish the exclusiveness of feature distribution of known classes. In this letter, we propose an entropy-awareness meta-learning method that improves the exclusiveness of feature distribution of known classes which means our method is effective for not only classifying the seen classes but also encountering the unseen other classes. Through meta-learning tasks, the proposed method learns to construct a feature space of the dynamic-assigned known classes. This feature space is required by the tasks to reject all other classes not belonging to the known classes. At the same time, the proposed entropy-awareness loss helps the model to enhance the feature space with effective and robust discrimination between the known and unknown classes. Therefore, our method can construct a dynamic feature space with discrimination between the known and unknown classes to simultaneously classify the dynamic-assigned known classes and reject the unknown classes. Experiments conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset have shown the effectiveness of our method for SAR OSR.
SAR Ship Target Recognition via Selective Feature Discrimination and Multifeature Center Classifier
Authors: Chenwei Wang, Siyi Luo, Jifang Pei, Yulin Huang, Yin Zhang, Jianyu Yang
Date: 2023-08-20 00:00:00

Maritime surveillance is not only necessary for every country, such as in maritime safeguarding and fishing controls, but also plays an essential role in international fields, such as in rescue support and illegal immigration control. Most of the existing automatic target recognition (ATR) methods directly send the extracted whole features of SAR ships into one classifier. The classifiers of most methods only assign one feature center to each class. However, the characteristics of SAR ship images, large inner-class variance, and small interclass difference lead to the whole features containing useless partial features and a single feature center for each class in the classifier failing with large inner-class variance. We proposes a SAR ship target recognition method via selective feature discrimination and multifeature center classifier. The selective feature discrimination automatically finds the similar partial features from the most similar interclass image pairs and the dissimilar partial features from the most dissimilar inner-class image pairs. It then provides a loss to enhance these partial features with more interclass separability. Motivated by divide and conquer, the multifeature center classifier assigns multiple learnable feature centers for each ship class. In this way, the multifeature centers divide the large inner-class variance into several smaller variances and conquered by combining all feature centers of one ship class. Finally, the probability distribution over all feature centers is considered comprehensively to achieve an accurate recognition of SAR ship images. The ablation experiments and experimental results on OpenSARShip and FUSAR-Ship datasets show that our method has achieved superior recognition performance under decreasing training SAR ship samples.
SAR Ship Target Recognition Via Multi-Scale Feature Attention and Adaptive-Weighed Classifier
Authors: Chenwei Wang, Jifang Pei, Siyi Luo, Weibo Huo, Yulin Huang, Yin Zhang, Jianyu Yang
Date: 2023-08-20 00:00:00

Maritime surveillance is indispensable for civilian fields, including national maritime safeguarding, channel monitoring, and so on, in which synthetic aperture radar (SAR) ship target recognition is a crucial research field. The core problem to realizing accurate SAR ship target recognition is the large inner-class variance and inter-class overlap of SAR ship features, which limits the recognition performance. Most existing methods plainly extract multi-scale features of the network and utilize equally each feature scale in the classification stage. However, the shallow multi-scale features are not discriminative enough, and each scale feature is not equally effective for recognition. These factors lead to the limitation of recognition performance. Therefore, we proposed a SAR ship recognition method via multi-scale feature attention and adaptive-weighted classifier to enhance features in each scale, and adaptively choose the effective feature scale for accurate recognition. We first construct an in-network feature pyramid to extract multi-scale features from SAR ship images. Then, the multi-scale feature attention can extract and enhance the principal components from the multi-scale features with more inner-class compactness and inter-class separability. Finally, the adaptive weighted classifier chooses the effective feature scales in the feature pyramid to achieve the final precise recognition. Through experiments and comparisons under OpenSARship data set, the proposed method is validated to achieve state-of-the-art performance for SAR ship recognition.
SAR ATR Method with Limited Training Data via an Embedded Feature Augmenter and Dynamic Hierarchical-Feature Refiner
Authors: Chenwei Wang, Siyi Luo, Yulin Huang, Jifang Pei, Yin Zhang, Jianyu Yang
Date: 2023-08-20 00:00:00

Without sufficient data, the quantity of information available for supervised training is constrained, as obtaining sufficient synthetic aperture radar (SAR) training data in practice is frequently challenging. Therefore, current SAR automatic target recognition (ATR) algorithms perform poorly with limited training data availability, resulting in a critical need to increase SAR ATR performance. In this study, a new method to improve SAR ATR when training data are limited is proposed. First, an embedded feature augmenter is designed to enhance the extracted virtual features located far away from the class center. Based on the relative distribution of the features, the algorithm pulls the corresponding virtual features with different strengths toward the corresponding class center. The designed augmenter increases the amount of information available for supervised training and improves the separability of the extracted features. Second, a dynamic hierarchical-feature refiner is proposed to capture the discriminative local features of the samples. Through dynamically generated kernels, the proposed refiner integrates the discriminative local features of different dimensions into the global features, further enhancing the inner-class compactness and inter-class separability of the extracted features. The proposed method not only increases the amount of information available for supervised training but also extracts the discriminative features from the samples, resulting in superior ATR performance in problems with limited SAR training data. Experimental results on the moving and stationary target acquisition and recognition (MSTAR), OpenSARShip, and FUSAR-Ship benchmark datasets demonstrate the robustness and outstanding ATR performance of the proposed method in response to limited SAR training data.
Deformable-Detection Transformer for Microbubble Localization in Ultrasound Localization Microscopy
Authors: Sepideh K. Gharamaleki, Brandon Helfield, Hassan Rivaz
Date: 2023-08-18 00:00:00
Tasks: Super-Resolution, Transfer Learning

To overcome the half a wavelength resolution limitations of ultrasound imaging, microbubbles (MBs) have been utilized widely in the field. Conventional MB localization methods are limited whether by exhaustive parameter tuning or considering a fixed Point Spread Function (PSF) for MBs. This questions their adaptability to different imaging settings or depths. As a result, development of methods that don't rely on manually adjusted parameters is crucial. Previously, we used a transformer-based approach i.e. DEtection TRansformer (DETR) (arXiv:2005.12872v3 and arXiv:2209.11859v1) to address the above mentioned issues. However, DETR suffers from long training times and lower precision for smaller objects. In this paper, we propose the application of DEformable DETR (DE-DETR) ( arXiv:2010.04159) for MB localization to mitigate DETR's above mentioned challenges. As opposed to DETR, where attention is casted upon all grid pixels, DE-DETR utilizes a multi-scale deformable attention to distribute attention within a limited budget. To evaluate the proposed strategy, pre-trained DE-DETR was fine-tuned on a subset of the dataset provided by the IEEE IUS Ultra-SR challenge organizers using transfer learning principles and subsequently we tested the network on the rest of the dataset, excluding the highly correlated frames. The results manifest an improvement both in precision and recall and the final super-resolution maps compared to DETR.
Advancing Intra-operative Precision: Dynamic Data-Driven Non-Rigid Registration for Enhanced Brain Tumor Resection in Image-Guided Neurosurgery
Authors: Nikos Chrisochoides, Andriy Fedorov, Fotis Drakopoulos, Andriy Kot, Yixun Liu, Panos Foteinos, Angelos Angelopoulos, Olivier Clatz, Nicholas Ayache, Peter M. Black, Alex J. Golby, Ron Kikinis
Date: 2023-08-18 00:00:00
Tasks: Distributed Computing

During neurosurgery, medical images of the brain are used to locate tumors and critical structures, but brain tissue shifts make pre-operative images unreliable for accurate removal of tumors. Intra-operative imaging can track these deformations but is not a substitute for pre-operative data. To address this, we use Dynamic Data-Driven Non-Rigid Registration (NRR), a complex and time-consuming image processing operation that adjusts the pre-operative image data to account for intra-operative brain shift. Our review explores a specific NRR method for registering brain MRI during image-guided neurosurgery and examines various strategies for improving the accuracy and speed of the NRR method. We demonstrate that our implementation enables NRR results to be delivered within clinical time constraints while leveraging Distributed Computing and Machine Learning to enhance registration accuracy by identifying optimal parameters for the NRR method. Additionally, we highlight challenges associated with its use in the operating room.
Parametric Channel Estimation with Short Pilots in RIS-Assisted Near- and Far-Field Communications
Authors: Mehdi Haghshenas, Parisa Ramezani, Maurizio Magarini, Emil Björnson
Date: 2023-08-21 00:00:00

Considering the dimensionality of a typical reconfigurable intelligent surface (RIS), channel state information acquisition in RIS-assisted systems requires lengthy pilot transmissions. Moreover, the large aperture of the RIS may cause transmitters/receivers to fall in its near-field region, where both distance and angles affect the channel structure. This paper proposes a parametric maximum likelihood estimation (MLE) framework for jointly estimating the direct channel between the user and the base station (BS) and the line-of-sight channel between the user and the RIS, in both far-field and near-field scenarios. The MLE framework is first developed for the case of single-antenna BS and later extended to the scenario where the BS is equipped with multiple antennas. A novel adaptive RIS configuration strategy is proposed to select the RIS configuration for the next pilot to actively refine the estimate. We design a minimal-sized codebook of orthogonal RIS configurations to choose from during pilot transmission with a dimension much smaller than the number of RIS elements. To further reduce the required number of pilots, we propose an initialization strategy with two wide beams. We demonstrate numerically that the proposed MLE method needs only a few pilots for achieving accurate channel estimates and further show that the presented framework performs well under Rician fading. We also showcase efficient user channel tracking in near-field and far-field scenarios.