pwc-archive/papers-with-abstracts huggingface dataset:
Consumers' Perceived Privacy Violations in Online Advertising
Authors: Kinshuk Jerath, Klaus M. Miller
Date: 2024-03-06 00:00:00

In response to privacy concerns about collecting and using personal data, the online advertising industry has been developing privacy-enhancing technologies (PETs), e.g., under Google's Privacy Sandbox initiative. In this research, we use the dual-privacy framework, which postulates that consumers have intrinsic and instrumental preferences for privacy, to understand consumers' perceived privacy violations (PPVs) for current and proposed online advertising practices. The key idea is that different practices differ in whether individual data leaves the consumer's machine or not and in how they track and target consumers; these affect, respectively, the intrinsic and instrumental components of privacy preferences differently, leading to different PPVs for different practices. We conducted online studies focused on consumers in the United States to elicit PPVs for various advertising practices. Our findings confirm the intuition that tracking and targeting consumers under the industry status quo of behavioral targeting leads to high PPV. New technologies or proposals that ensure that data are kept on the consumer's machine lower PPV relative to behavioral targeting but, importantly, this decrease is small. Furthermore, group-level targeting does not differ significantly from individual-level targeting in reducing PPV. Under contextual targeting, where there is no tracking, PPV is significantly reduced. Interestingly, with respect to PPV, consumers are indifferent between seeing untargeted ads and no ads when they are not being tracked. We find that consumer perceptions of privacy violations under different tracking and targeting practices may differ from what technical definitions suggest. Therefore, rather than relying solely on technical perspectives, a consumer-centric approach to privacy is needed, based on, for instance, the dual-privacy framework.
Paying for Privacy: Pay-or-Tracking Walls
Authors: Timo Mueller-Tribbensee, Klaus M. Miller, Bernd Skiera
Date: 2024-03-06 00:00:00

Prestigious news publishers, and more recently, Meta, have begun to request that users pay for privacy. Specifically, users receive a notification banner, referred to as a pay-or-tracking wall, that requires them to (i) pay money to avoid being tracked or (ii) consent to being tracked. These walls have invited concerns that privacy might become a luxury. However, little is known about pay-or-tracking walls, which prevents a meaningful discussion about their appropriateness. This paper conducts several empirical studies and finds that top EU publishers use pay-or-tracking walls. Their implementations involve various approaches, including bundling the pay option with advertising-free access or additional content. The price for not being tracked exceeds the advertising revenue that publishers generate from a user who consents to being tracked. Notably, publishers' traffic does not decline when implementing a pay-or-tracking wall and most users consent to being tracked; only a few users pay. In short, pay-or-tracking walls seem to provide the means for expanding the practice of tracking. Publishers profit from pay-or-tracking walls and may observe a revenue increase of 16.4% due to tracking more users than under a cookie consent banner.
Impact of COVID-19 on Exchange rate volatility of Bangladesh: Evidence through GARCH model
Authors: Rizwanul Karim
Date: 2024-03-05 00:00:00

This study uses the GARCH (1,1) model to examine the impact of COVID-19 cases (log value) on the volatility of the Exchange rate return of Bangladeshi taka (BDT) over the US dollar (USD), Japanese Yen (JPY), and Swedish Krona (SEK). The result shows that an increase in the number of COVID-19-affected cases in Bangladesh has a significant and positive impact on the volatility of exchange rates BDT/USD, BDT/JPY, and BDT/SEK.
Experimenting with Generative AI: Does ChatGPT Really Increase Everyone's Productivity?
Authors: Voraprapa Nakavachara, Tanapong Potipiti, Thanee Chaiwat
Date: 2024-03-04 00:00:00
Tasks: Econometrics, Math

Generative AI technologies such as ChatGPT, Gemini, and MidJourney have made remarkable progress in recent years. Recent literature has documented ChatGPT's positive impact on productivity in areas where it has strong expertise, attributable to extensive training datasets, such as the English language and Python/SQL programming. However, there is still limited literature regarding ChatGPT's performance in areas where its capabilities could still be further enhanced. This paper aims to fill this gap. We conducted an experiment in which economics students were asked to perform writing analysis tasks in a non-English language (specifically, Thai) and math & data analysis tasks using a less frequently used programming package (specifically, Stata). The findings suggest that, on average, participants performed better using ChatGPT in terms of scores and time taken to complete the tasks. However, a detailed examination reveals that 34% of participants saw no improvement in writing analysis tasks, and 42% did not improve in math & data analysis tasks when employing ChatGPT. Further investigation indicated that higher-ability students, as proxied by their econometrics grades, were the ones who performed worse in writing analysis tasks when using ChatGPT. We also found evidence that students with better digital skills performed better with ChatGPT. This research provides insights on the impact of generative AI. Thus, stakeholders can make informed decisions to implement appropriate policy frameworks or redesign educational systems. It also highlights the critical role of human skills in addressing and complementing the limitations of technology.
Uncovering the Sino-US dynamic risk spillovers effects: Evidence from agricultural futures markets
Authors: Han-Yu Zhu, Peng-Fei Dai, Wei-Xing Zhou
Date: 2024-03-04 00:00:00

Agricultural products play a critical role in human development. With economic globalization and the financialization of agricultural products continuing to advance, the interconnections between different agricultural futures have become closer. We utilize a TVP-VAR-DY model combined with the quantile method to measure the risk spillover between 11 agricultural futures on the futures exchanges of US and China from July 9,2014, to December 31,2022. This study yielded several significant findings. Firstly, CBOT corn, soybean, and wheat were identified as the primary risk transmitters, with DCE corn and soybean as the main risk receivers. Secondly, sudden events or increased economic uncertainty can increase the overall risk spillovers. Thirdly, there is an aggregation of risk spillovers amongst agricultural futures based on the dynamic directional spillover results. Lastly, the central agricultural futures under the conditional mean are CBOT corn and soybean, while CZCE hard wheat and long-grained rice are the two risk spillover centers in extreme cases, as per the results of the spillover network and minimum spanning tree. Based on these results, decision-makers are advised to safeguard against the price risk of agricultural futures under sudden economic events, and investors can utilize the results to construct a superior investment portfolio by taking different agricultural product futures as risk-leading indicators according to various situations.
"Digitwashing": The Gap between Words and Deeds in Digital Transformation and Stock Price Crash Risk
Authors: Shutter Zor
Date: 2024-03-03 00:00:00
Tasks: valid

The contrast between companies' "fleshy" promises and the "skeletal" performance in digital transformation may lead to a higher risk of stock price crash. This paper selects a sample of Shanghai and Shenzhen A-share listed companies from 2010 to 2021, empirically analyses the specific impact of the gap between words and deeds in digital transformation (GDT) on the stock price crash risk, and explores the possible causes of GDT. We found that GDT significantly increases the stock price crash risk, and this finding is still valid after a series of robustness tests. In a further study, a deeper examination of the causes of GDT reveals that firms' perceptions of economic policy uncertainty significantly increase GDT, and the effect is more pronounced in the sample of loss-making firms. At the same time, the results of the heterogeneity test suggest that investors are more tolerant of state-owned enterprises when they are in the GDT situation. Taken together, we provide a concrete bridge between the two measures of digital transformation - digital text frequency and digital technology share - and offer new insights to enhance capital market stability.
Modelling Global Fossil CO2 Emissions with a Lognormal Distribution: A Climate Policy Tool
Authors: Faustino Prieto, Catalina B. García-García, Román Salmerón Gómez
Date: 2024-03-01 00:00:00

Carbon dioxide (CO2) emissions have emerged as a critical issue with profound impacts on the environment, human health, and the global economy. The steady increase in atmospheric CO2 levels, largely due to human activities such as burning fossil fuels and deforestation, has become a major contributor to climate change and its associated catastrophic effects. To tackle this pressing challenge, a coordinated global effort is needed, which necessitates a deep understanding of emissions patterns and trends. In this paper, we explore the use of statistical modelling, specifically the lognormal distribution, as a framework for comprehending and predicting CO2 emissions. We build on prior research that suggests a complex distribution of emissions and seek to test the hypothesis that a simpler distribution can still offer meaningful insights for policy-makers. We utilize data from three comprehensive databases and analyse six candidate distributions (exponential, Fisk, gamma, lognormal, Lomax, Weibull) to identify a suitable model for global fossil CO2 emissions. Our findings highlight the adequacy of the lognormal distribution in characterizing emissions across all countries and years studied. Furthermore, to provide additional support for this distribution, we provide statistical evidence supporting the applicability of Gibrat's law to those CO2 emissions. Finally, we employ the lognormal model to predict emission parameters for the coming years and propose two policies for reducing total fossil CO2 emissions. Our research aims to provide policy-makers with accurate and detailed information to support effective climate change mitigation strategies.
Idiosyncratic Risk, Government Debt and Inflation
Authors: Matthias Hänsel
Date: 2024-03-01 00:00:00

How does public debt matter for price stability? If it is useful for the private sector to insure idiosyncratic risk, even transitory government debt expansions can exert upward pressure on interest rates and create inflation. As I demonstrate using an analytically tractable model, this holds in the presence of an active Taylor rule and does not require the absence of future fiscal consolidation. Further analysis using a quantitative 2-asset HANK model reveals the magnitude of the mechanism to crucially depend on the structure of the asset market: under common assumptions, the interest rate effects of public debt are either overly strong or overly weak. After disciplining this aspect based on evidence regarding its long-term relationship with treasury returns, my framework indicates relevant short-run effects of public debt on inflation under active monetary policy: In particular, in the HANK model the mechanism can account for US inflation remaining elevated in 2023 and afterwards.
Competing Mechanisms in Games Played Through Agents: Theory and Experiment
Authors: Seungjin Han, Andrew Leal
Date: 2024-03-05 00:00:00

This paper proposes Competing Mechanism Games Played Through Agent (CMGPTA), an extension of the GPTA (Prat and Rustichini (2003)), where a Principal can offer any arbitrary mechanism that specifies a transfer schedule for each agent conditional on all Agents' messages. We identify the set of equilibrium allocations using deviator-reporting mechanisms (DRMs) on the path and single transfer schedules off the path. We design a lab experiment implementing DRMs. We observe that implemented outcomes are efficient more often than random. A majority of the time, Agents do tell the truth on the identity of a deviating Principal, despite potential gains from (tacit) collusion on false reports. As play progresses, Agents learn to play with their counterparty Agent with the average predicted probability of collusion on false reports across groups increasing from about 9% at the beginning of the experiment to just under 20% by the end. However, group heterogeneity is significant.
A Continuous-Time Stochastic Model of the Fiscal Theory of the Price Level and Consistency of Its Critique
Authors: Andrey Kofnov
Date: 2024-03-03 00:00:00

The paper tests the validity of the critique of the fiscal theory of the price level. A stochastic general equilibrium model with continuous time is constructed. An active fiscal policy and a passive monetary policy have been set. Monetary policy manages the interest rate through the Taylor rule. The stochastic default factor in the special form is introduced. A complete definite system of equations is obtained for the detection of equilibrium. It is asserted that the peculiarities of the approach to modeling are of critical importance for verifying the presence of certain hypotheses and formulating conclusions. The results of this work are in support of the fiscal theory of the price level.
Predicting the Unpredictable under Subjective Expected Utility
Authors: Burkhard C. Schipper
Date: 2024-03-03 00:00:00
Tasks: Decision Making, Form

We consider a decision maker who is unaware of objects to be sampled and thus cannot form beliefs about the occurrence of particular objects. Ex ante she can form beliefs about the occurrence of novelty and the frequencies of yet to be known objects. Conditional on any sampled objects, she can also form beliefs about the next object being novel or being one of the previously sampled objects. We characterize behaviorally such beliefs under subjective expected utility. In doing so, we relate "reverse" Bayesianism, a central property in the literature on decision making under growing awareness, with exchangeable random partitions, the central property in the literature on the discovery of species problem and mutations in statistics, combinatorial probability theory, and population genetics. Partition exchangeable beliefs do not necessarily satisfy "reverse" Bayesianism. Yet, the most prominent models of exchangeable random partitions, the model by De Morgan (1838), the one parameter model of Ewens (1972), and the two parameter model of Pitman (1995) and Zabell (1997), do satisfy "reverse" Bayesianism. Our characterization allows us to interpret these models as subjective beliefs of a decision maker and to derive the parameters from choice behavior.
Room Impulse Response Estimation using Optimal Transport: Simulation-Informed Inference
Authors: David Sundström, Anton Björkman, Andreas Jakobsson, Filip Elvander
Date: 2024-03-06 00:00:00

The ability to accurately estimate room impulse responses (RIRs) is integral to many applications of spatial audio processing. Regrettably, estimating the RIR using ambient signals, such as speech or music, remains a challenging problem due to, e.g., low signal-to-noise ratios, finite sample lengths, and poor spectral excitation. Commonly, in order to improve the conditioning of the estimation problem, priors are placed on the amplitudes of the RIR. Although serving as a regularizer, this type of prior is generally not useful when only approximate knowledge of the delay structure is available, which, for example, is the case when the prior is a simulated RIR from an approximation of the room geometry. In this work, we target the delay structure itself, constructing a prior based on the concept of optimal transport. As illustrated using both simulated and measured data, the resulting method is able to beneficially incorporate information even from simple simulation models, displaying considerable robustness to perturbations in the assumed room dimensions and its temperature.
PixIT: Joint Training of Speaker Diarization and Speech Separation from Real-world Multi-speaker Recordings
Authors: Joonas Kalda, Clément Pagés, Ricard Marxer, Tanel Alumäe, Hervé Bredin
Date: 2024-03-04 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition, Speech Separation

A major drawback of supervised speech separation (SSep) systems is their reliance on synthetic data, leading to poor real-world generalization. Mixture invariant training (MixIT) was proposed as an unsupervised alternative that uses real recordings, yet struggles with overseparation and adapting to long-form audio. We introduce PixIT, a joint approach that combines permutation invariant training (PIT) for speaker diarization (SD) and MixIT for SSep. With a small extra requirement of needing SD labels, it solves the problem of overseparation and allows stitching local separated sources leveraging existing work on clustering-based neural SD. We measure the quality of the separated sources via applying automatic speech recognition (ASR) systems to them. PixIT boosts the performance of various ASR systems across two meeting corpora both in terms of the speaker-attributed and utterance-based word error rates while not requiring any fine-tuning.
Advanced Signal Analysis in Detecting Replay Attacks for Automatic Speaker Verification Systems
Authors: Lee Shih Kuang
Date: 2024-03-02 00:00:00
Tasks: Speaker Verification

This study proposes novel signal analysis methods for replay speech detection in automatic speaker verification (ASV) systems. The proposed methods -- arbitrary analysis (AA), mel scale analysis (MA), and constant Q analysis (CQA) -- are inspired by the calculation of the Fourier inversion formula. These methods introduce new perspectives in signal analysis for replay speech detection by employing alternative sinusoidal sequence groups. The efficacy of the proposed methods is examined on the ASVspoof 2019 \& 2021 PA databases with experiments, and confirmed by the performance of systems that incorporated the proposed methods; the successful integration of the proposed methods and a speech feature that calculates temporal autocorrelation of speech (TAC) from complex spectra strongly confirms it. Moreover, the proposed CQA and MA methods show their superiority to the conventional methods on efficiency (approximately 2.36 times as fast compared to the conventional constant Q transform (CQT) method) and efficacy, respectively, in analyzing speech signals, making them promising to utilize in music and speech processing works.
ProxNF: Neural Field Proximal Training for High-Resolution 4D Dynamic Image Reconstruction
Authors: Luke Lozenski, Refik Mert Cam, Mark D. Pagel, Mark A. Anastasio, Umberto Villa
Date: 2024-03-06 00:00:00
Tasks: Image Reconstruction

Accurate spatiotemporal image reconstruction methods are needed for a wide range of biomedical research areas but face challenges due to data incompleteness and computational burden. Data incompleteness arises from the undersampling often required to increase frame rates, while computational burden emerges due to the memory footprint of high-resolution images with three spatial dimensions and extended time horizons. Neural fields (NFs), an emerging class of neural networks that act as continuous representations of spatiotemporal objects, have previously been introduced to solve these dynamic imaging problems by reframing image reconstruction as a problem of estimating network parameters. Neural fields can address the twin challenges of data incompleteness and computational burden by exploiting underlying redundancies in these spatiotemporal objects. This work proposes ProxNF, a novel neural field training approach for spatiotemporal image reconstruction leveraging proximal splitting methods to separate computations involving the imaging operator from updates of the network parameters. Specifically, ProxNF evaluates the (subsampled) gradient of the data-fidelity term in the image domain and uses a fully supervised learning approach to update the neural field parameters. This method is demonstrated in two numerical phantom studies and an in-vivo application to tumor perfusion imaging in small animal models using dynamic contrast-enhanced photoacoustic computed tomography (DCE PACT).
LDSF: Lightweight Dual-Stream Framework for SAR Target Recognition by Coupling Local Electromagnetic Scattering Features and Global Visual Features
Authors: Xuying Xiong, Xinyu Zhang, Weidong Jiang, Tianpeng Liu
Date: 2024-03-06 00:00:00
Tasks: Graph Neural Network

Mainstream DNN-based SAR-ATR methods still face issues such as easy overfitting of a few training data, high computational overhead, and poor interpretability of the black-box model. Integrating physical knowledge into DNNs to improve performance and achieve a higher level of physical interpretability becomes the key to solving the above problems. This paper begins by focusing on the electromagnetic (EM) backscattering mechanism. We extract the EM scattering (EMS) information from the complex SAR data and integrate the physical properties of the target into the network through a dual-stream framework to guide the network to learn physically meaningful and discriminative features. Specifically, one stream is the local EMS feature (LEMSF) extraction net. It is a heterogeneous graph neural network (GNN) guided by a multi-level multi-head attention mechanism. LEMSF uses the EMS information to obtain topological structure features and high-level physical semantic features. The other stream is a CNN-based global visual features (GVF) extraction net that captures the visual features of SAR pictures from the image domain. After obtaining the two-stream features, a feature fusion subnetwork is proposed to adaptively learn the fusion strategy. Thus, the two-stream features can maximize the performance. Furthermore, the loss function is designed based on the graph distance measure to promote intra-class aggregation. We discard overly complex design ideas and effectively control the model size while maintaining algorithm performance. Finally, to better validate the performance and generalizability of the algorithms, two more rigorous evaluation protocols, namely once-for-all (OFA) and less-for-more (LFM), are used to verify the superiority of the proposed algorithm on the MSTAR.
Ice-Tide: Implicit Cryo-ET Imaging and Deformation Estimation
Authors: Valentin Debarnot, Vinith Kishore, Ricardo D. Righetto, Ivan Dokmanić
Date: 2024-03-04 00:00:00
Tasks: Cryogenic Electron Tomography, Electron Tomography

We introduce ICE-TIDE, a method for cryogenic electron tomography (cryo-ET) that simultaneously aligns observations and reconstructs a high-resolution volume. The alignment of tilt series in cryo-ET is a major problem limiting the resolution of reconstructions. ICE-TIDE relies on an efficient coordinate-based implicit neural representation of the volume which enables it to directly parameterize deformations and align the projections. Furthermore, the implicit network acts as an effective regularizer, allowing for high-quality reconstruction at low signal-to-noise ratios as well as partially restoring the missing wedge information. We compare the performance of ICE-TIDE to existing approaches on realistic simulated volumes where the significant gains in resolution and accuracy of recovering deformations can be precisely evaluated. Finally, we demonstrate ICE-TIDE's ability to perform on experimental data sets.