pwc-archive/papers-with-abstracts huggingface dataset:
Separating Advertising and Marketplace Functions of E-commerce Platforms: Is it Social Welfare Enhancing?
Authors: Zhe Zhang, Young Kwark, Srinivasan Raghunathan
Date: 2025-02-12 00:00:00

The use of sponsored product listings in prominent positions of consumer search results has made e-commerce platforms, which traditionally serve as marketplaces for third-party sellers to reach consumers, a major medium for those sellers to advertise their products. On the other hand, regulators have expressed anti-trust concerns about an e-commerce platform's integration of marketplace and advertising functions; they argue that such integration benefits the platform and sellers at the expense of consumers and society and have proposed separating the advertising function from those platforms. We show, contrary to regulators' concerns, that separating the advertising function from the e-commerce platform benefits the sellers, hurts the consumers, and does not necessarily benefit the social welfare. A key driver of our findings is that an independent advertising firm, which relies solely on advertising revenue, has same or lesser economic incentive to improve targeting precision than an e-commerce platform that also serves as the advertising medium, even if both have the same ability to target consumers. This is because an improvement in targeting precision enhances the marketplace commission by softening the price competition between sellers, but hurts the advertising revenue by softening the competition for prominent ad positions.
Better Together? A Field Experiment on Human-Algorithm Interaction in Child Protection
Authors: Marie-Pascale Grimon, Christopher Mills
Date: 2025-02-12 00:00:00

Algorithm tools have the potential to improve public service efficiency, but our understanding of how experts use algorithms is limited, and concerns about resulting bias are widespread. We randomize access to algorithm support for workers allocating Child Protective Services (CPS) investigations. Access to the algorithm reduced maltreatment-related hospitalizations, especially for disadvantaged groups, while reducing CPS surveillance of Black children. Child injuries fell by 29 percent. Workers improved their scrutiny of complementary information emphasized by the algorithm, and targeted investigations to children at greater risk of harm irrespective of algorithm-predicted risk. Algorithm-only counterfactuals confirm human-algorithm complementarity for both efficiency and equity.
Predicting Insurance Penetration Rate in Ghana Using the Autoregressive Integrated Moving Average (ARIMA) Model
Authors: Thomas Gyima-Adu, Godwin Gidisu
Date: 2025-02-11 00:00:00

Ghana records a low penetration of 1.05% compared to some of its African counterparts. For example South Africa, which has an insurance penetration rate of 17%, followed by Namibia which records 6.3%. This means, there is more room for improvement. More upsetting, with Ghana hovering around the 1% as at 2018, the rate works out to the small amount of Gross Domestic Product. This research seeks to model and forecast insurance penetration rate in Ghana using the Autoregressive Integrated Moving Average technique. The result indicates that ARIMA (3,1,0) is the appropriate model for insurance penetration in Ghana. Also, results from the forecast could serve as an advisory or the need to re-strategize as a country. Therefore, determining the future pattern of insurance penetration will lead to the remedies that will increase the number of insured in the future.
Analysis of energy, CO2 emissions and economy of the technological migration for clean cooking in Ecuador
Authors: J. Martinez, Jaime Marti-Herrero, S. Villacis, A. J. Riofrio, D. Vaca
Date: 2025-01-20 00:00:00

The objective of this study is to analyze the CO2 emissions and economic impacts of the implementation of the National Efficient Cooking Program (NECP) in Ecuador, which aims to migrate the population from Liquefied Petroleum Gas (LPG)-based stoves to electric induction stoves. This program is rooted in the current effort to change Ecuador's energy balance, with hydroelectric power expected to generate 83.61% of national electricity by 2022, ending the need for subsidized LPG. For this analysis, the 2014 baseline situation has been compared with two future scenarios for 2022: a business-as-usual scenario and an NECP-success scenario. This study demonstrates the viability of migration from imported fossil fuels to locally-produced renewable energy as the basis for an efficient cooking facility. The new policies scenario would save US$ 1.162 billion in annual government expenditure on cooking subsidies, and reducing CO2 emissions associated to energy for cooking in 1.8 tCO2/y.
Equitable Auction Design: With and Without Distributions
Authors: Ruiqin Wang, Cagil Kocyigit, Napat Rujeerapaiboon
Date: 2025-02-12 00:00:00

We study a mechanism design problem where a seller aims to allocate a good to multiple bidders, each with a private value. The seller supports or favors a specific group, referred to as the minority group. Specifically, the seller requires that allocations to the minority group are at least a predetermined fraction (equity level) of those made to the rest of the bidders. Such constraints arise in various settings, including government procurement and corporate supply chain policies that prioritize small businesses, environmentally responsible suppliers, or enterprises owned by historically disadvantaged individuals. We analyze two variants of this problem: stochastic mechanism design, which assumes bidders' values follow a known distribution and seeks to maximize expected revenue, and regret-based mechanism design, which makes no distributional assumptions and aims to minimize the worst-case regret. We characterize a closed-form optimal stochastic mechanism and propose a closed-form regret-based mechanism, and establish that the ex-post regret under the latter is at most a constant multiple (dependent on the equity level) of the optimal worst-case regret. We further quantify that this approximation constant is at most 1.31 across different equity levels. Both mechanisms can be interpreted as set-asides, a common policy tool that reserves a fraction of goods for minority groups. Furthermore, numerical results demonstrate that the stochastic mechanism performs well when the bidders' value distribution is accurately estimated, while the regret-based mechanism exhibits greater robustness under estimation errors.
Renegotiation-Proof Cheap Talk
Authors: Steven Kivinen, Christoph Kuzmics
Date: 2025-02-12 00:00:00

An informed Advisor and an uninformed Decision-Maker engage in repeated cheap talk communication in always new (stochastically independent) decision problems. They have a conflict of interest over which action should be implemented at least in some cases. Our main result is that, while the Decision-Maker's optimal payoff is attainable in some subgame perfect equilibrium (by force of the usual folk theorem), no payoff profile close to the Decision-Maker's optimal one is immune to renegotiation. Pareto efficient renegotiation-proof equilibria are typically attainable, and they entail a compromise between the Advisor and the Decision-Maker. This could take the form of the Advisor being truthful and the Decision-Maker not utilizing this information to their own full advantage, or the Advisor being somewhat liberal with the truth and the Decision-Maker, while fully aware of this, pretending to believe the Advisor.
Sabotage and Free Riding in Contests with a Group-Specific Public-Good/Bad Prize
Authors: Kyung Hwan Baik, Dongwoo Lee
Date: 2025-02-12 00:00:00

We study contests in which two groups compete to win (or not to win) a group-specific public-good/bad prize. Each player in the groups can exert two types of effort: one to help her own group win the prize, and one to sabotage her own group's chances of winning it. The players in the groups choose their effort levels simultaneously and independently. We introduce a specific form of contest success function that determines each group's probability of winning the prize, taking into account players' sabotage activities. We show that two types of purestrategy Nash equilibrium occur, depending on parameter values: one without sabotage activities and one with sabotage activities. In the first type, only the highest-valuation player in each group expends positive effort, whereas, in the second type, only the lowest-valuation player in each group expends positive effort.
A shared-revenue Bertrand game
Authors: Raj Pabari, Udaya Ghai, Dominique Perrault-Joncas, Kari Torkkola, Orit Ronen, Dhruv Madeka, Dean Foster, Omer Gottesman
Date: 2025-02-11 00:00:00

We introduce and analyze a variation of the Bertrand game in which the revenue is shared between two players. This game models situations in which one economic agent can provide goods/services to consumers either directly or through an independent seller/contractor in return for a share of the revenue. We analyze the equilibria of this game, and show how they can predict different business outcomes as a function of the players' costs and the transferred revenue shares. Importantly, we identify game parameters for which independent sellers can simultaneously increase the original player's payoff while increasing consumer surplus. We then extend the shared-revenue Bertrand game by considering the shared revenue proportion as an action and giving the independent seller an outside option to sell elsewhere. This work constitutes a first step towards a general theory for how partnership and sharing of resources between economic agents can lead to more efficient markets and improve the outcomes of both agents as well as consumers.
NDAI Agreements
Authors: Matthew Stephenson, Andrew Miller, Xyn Sun, Bhargav Annem, Rohan Parikh
Date: 2025-02-11 00:00:00

We study a fundamental challenge in the economics of innovation: an inventor must reveal details of a new idea to secure compensation or funding, yet such disclosure risks expropriation. We present a model in which a seller (inventor) and buyer (investor) bargain over an information good under the threat of hold-up. In the classical setting, the seller withholds disclosure to avoid misappropriation, leading to inefficiency. We show that trusted execution environments (TEEs) combined with AI agents can mitigate and even fully eliminate this hold-up problem. By delegating the disclosure and payment decisions to tamper-proof programs, the seller can safely reveal the invention without risking expropriation, achieving full disclosure and an efficient ex post transfer. Moreover, even if the invention's value exceeds a threshold that TEEs can fully secure, partial disclosure still improves outcomes compared to no disclosure. Recognizing that real AI agents are imperfect, we model "agent errors" in payments or disclosures and demonstrate that budget caps and acceptance thresholds suffice to preserve most of the efficiency gains. Our results imply that cryptographic or hardware-based solutions can function as an "ironclad NDA," substantially mitigating the fundamental disclosure-appropriation paradox first identified by Arrow (1962) and Nelson (1959). This has far-reaching policy implications for fostering R&D, technology transfer, and collaboration.
Optimal Bundling and Dominance
Authors: Zhiming Feng
Date: 2025-02-11 00:00:00

The optimal bundling problem is the design of bundles (and prices) that maximize the expected virtual surplus, constrained by individual rationality and incentive compatibility. I focus on a relaxed, constraint-free problem that maximizes the expected virtual surplus with deterministic allocations. I show that when the difference of two virtual value functions for any pair of stochastic bundles is single-crossing, the minimal optimal menu for the relaxed problem is the set of all undominated bundles, where dominance refers to the natural comparison of virtual values across the type space. Under single-crossing differences, this comparison simplifies to comparisons on boundary types, enabling an algorithm to generate the minimal optimal menu. I further show that when the difference of two virtual value functions for any pair of stochastic bundles is monotonic, there exists an endogenous order among deterministic bundles. This order justifies that the minimal optimal menu for the relaxed problem is also optimal for the nonrelaxed problem. I also characterize the conditions under which consumers' valuation functions and distributions allow the virtual value functions to exhibit monotonic differences. This characterization, combined with the dominance notion, solves related bundling problems such as distributional robustness, bundling with additive values, and the optimality of pure, nested, and relatively novel, singleton-bundle menus. As a side result, I discover new properties of single crossing and monotonic differences in convex environments.
Advances in Microphone Array Processing and Multichannel Speech Enhancement
Authors: Gongping Huang, Jesper R. Jensen, Jingdong Chen, Jacob Benesty, Mads G. Christensen, Akihiko Sugiyama, Gary Elko, Tomas Gaensler
Date: 2025-02-13 00:00:00
Tasks: Speech Enhancement

This paper reviews pioneering works in microphone array processing and multichannel speech enhancement, highlighting historical achievements, technological evolution, commercialization aspects, and key challenges. It provides valuable insights into the progression and future direction of these areas. The paper examines foundational developments in microphone array design and optimization, showcasing innovations that improved sound acquisition and enhanced speech intelligibility in noisy and reverberant environments. It then introduces recent advancements and cutting-edge research in the field, particularly the integration of deep learning techniques such as all-neural beamformers. The paper also explores critical applications, discussing their evolution and current state-of-the-art technologies that significantly impact user experience. Finally, the paper outlines future research directions, identifying challenges and potential solutions that could drive further innovation in these fields. By providing a comprehensive overview and forward-looking perspective, this paper aims to inspire ongoing research and contribute to the sustained growth and development of microphone arrays and multichannel speech enhancement.
Predicting Cognitive Decline: A Multimodal AI Approach to Dementia Screening from Speech
Authors: Lei Chi, Arav Sharma, Ari Gebhardt, Joseph T. Colonel
Date: 2025-02-13 00:00:00
Tasks: Classification, regression

Recent progress has been made in detecting early stage dementia entirely through recordings of patient speech. Multimodal speech analysis methods were applied to the PROCESS challenge, which requires participants to use audio recordings of clinical interviews to predict patients as healthy control, mild cognitive impairment (MCI), or dementia and regress the patient's Mini-Mental State Exam (MMSE) scores. The approach implemented in this work combines acoustic features (eGeMAPS and Prosody) with embeddings from Whisper and RoBERTa models, achieving competitive results in both regression (RMSE: 2.7666) and classification (Macro-F1 score: 0.5774) tasks. Additionally, a novel two-tiered classification setup is utilized to better differentiate between MCI and dementia. Our approach achieved strong results on the test set, ranking seventh on regression and eleventh on classification out of thirty-seven teams, exceeding the baseline results.
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech
Authors: Xin Wang, Héctor Delgado, Hemlata Tak, Jee-weon Jung, Hye-jin Shim, Massimiliano Todisco, Ivan Kukanov, Xuechen Liu, Md Sahidullah, Tomi Kinnunen, Nicholas Evans, Kong Aik Lee, Junichi Yamagishi, Myeonghun Jeong, Ge Zhu, Yongyi Zang, You Zhang, Soumi Maiti, Florian Lux, Nicolas Müller, Wangyou Zhang, Chengzhe Sun, Shuwei Hou, Siwei Lyu, Sébastien Le Maguer, Cheng Gong, Hanjie Guo, Liping Chen, Vishwanath Singh
Date: 2025-02-13 00:00:00
Tasks: Adversarial Attack, Adversarial Attack Detection, Face Swapping, Speaker Verification, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis, Voice Conversion

ASVspoof 5 is the fifth edition in a series of challenges which promote the study of speech spoofing and deepfake attacks as well as the design of detection solutions. We introduce the ASVspoof 5 database which is generated in a crowdsourced fashion from data collected in diverse acoustic conditions (cf. studio-quality data for earlier ASVspoof databases) and from ~2,000 speakers (cf. ~100 earlier). The database contains attacks generated with 32 different algorithms, also crowdsourced, and optimised to varying degrees using new surrogate detection models. Among them are attacks generated with a mix of legacy and contemporary text-to-speech synthesis and voice conversion models, in addition to adversarial attacks which are incorporated for the first time. ASVspoof 5 protocols comprise seven speaker-disjoint partitions. They include two distinct partitions for the training of different sets of attack models, two more for the development and evaluation of surrogate detection models, and then three additional partitions which comprise the ASVspoof 5 training, development and evaluation sets. An auxiliary set of data collected from an additional 30k speakers can also be used to train speaker encoders for the implementation of attack algorithms. Also described herein is an experimental validation of the new ASVspoof 5 database using a set of automatic speaker verification and spoof/deepfake baseline detectors. With the exception of protocols and tools for the generation of spoofed/deepfake speech, the resources described in this paper, already used by participants of the ASVspoof 5 challenge in 2024, are now all freely available to the community.
Causal Analysis of ASR Errors for Children: Quantifying the Impact of Physiological, Cognitive, and Extrinsic Factors
Authors: Vishwanath Pratap Singh, Md. Sahidullah, Tomi Kinnunen
Date: 2025-02-12 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Benchmarking, Causal Inference, Sensitivity, speech-recognition, Speech Recognition

The increasing use of children's automatic speech recognition (ASR) systems has spurred research efforts to improve the accuracy of models designed for children's speech in recent years. The current approach utilizes either open-source speech foundation models (SFMs) directly or fine-tuning them with children's speech data. These SFMs, whether open-source or fine-tuned for children, often exhibit higher word error rates (WERs) compared to adult speech. However, there is a lack of systemic analysis of the cause of this degraded performance of SFMs. Understanding and addressing the reasons behind this performance disparity is crucial for improving the accuracy of SFMs for children's speech. Our study addresses this gap by investigating the causes of accuracy degradation and the primary contributors to WER in children's speech. In the first part of the study, we conduct a comprehensive benchmarking study on two self-supervised SFMs (Wav2Vec2.0 and Hubert) and two weakly supervised SFMs (Whisper and MMS) across various age groups on two children speech corpora, establishing the raw data for the causal inference analysis in the second part. In the second part of the study, we analyze the impact of physiological factors (age, gender), cognitive factors (pronunciation ability), and external factors (vocabulary difficulty, background noise, and word count) on SFM accuracy in children's speech using causal inference. The results indicate that physiology (age) and particular external factor (number of words in audio) have the highest impact on accuracy, followed by background noise and pronunciation ability. Fine-tuning SFMs on children's speech reduces sensitivity to physiological and cognitive factors, while sensitivity to the number of words in audio persists. Keywords: Children's ASR, Speech Foundational Models, Causal Inference, Physiology, Cognition, Pronunciation
Learned Correction Methods for Ultrasound Computed Tomography Imaging Using Simplified Physics Models
Authors: Luke Lozenski, Hanchen Wang, Fu Li, Mark A. Anastasio, Brendt Wohlberg, Youzuo Lin, Umberto Villa
Date: 2025-02-13 00:00:00
Tasks: Image Reconstruction, Out-of-Distribution Generalization, SSIM

Ultrasound computed tomography (USCT) is an emerging modality for breast imaging. Image reconstruction methods that incorporate accurate wave physics produce high resolution quantitative images of acoustic properties but are computationally expensive. The use of a simplified linear model in reconstruction reduces computational expense at the cost of reduced accuracy. This work aims to systematically compare different learning approaches for USCT reconstruction utilizing simplified linear models. This work considered various learning approaches to compensate for errors stemming from a linearized wave propagation model: correction in the data and image domains. The resulting image reconstruction methods are systematically assessed, alongside data-driven and model-based methods, in four virtual imaging studies utilizing anatomically realistic numerical phantoms. Image quality was assessed utilizing relative root mean square error (RRMSE), structural similarity index measure (SSIM), and a task-based assessment for tumor detection. Correction in the measurement domain resulted in images with minor visual artifacts and highly accurate task performance. Correction in the image domain demonstrated a heavy bias on training data, resulting in hallucinations, but greater robustness to measurement noise. Combining both forms of correction performed best in terms of RRMSE and SSIM, at the cost of task performance. This work systematically assessed learned reconstruction methods incorporating an approximated physical model for USCT imaging. Results demonstrated the importance of incorporating physics, compared to data-driven methods. Learning a correction in the data domain led to better task performance and robust out-of-distribution generalization compared to correction in the image domain.
SQ-GAN: Semantic Image Communications Using Masked Vector Quantization
Authors: Francesco Pezone, Sergio Barbarossa, Giuseppe Caire
Date: 2025-02-13 00:00:00
Tasks: Image Compression, Quantization, Segmentation, Semantic Segmentation

This work introduces Semantically Masked VQ-GAN (SQ-GAN), a novel approach integrating generative models to optimize image compression for semantic/task-oriented communications. SQ-GAN employs off-the-shelf semantic semantic segmentation and a new specifically developed semantic-conditioned adaptive mask module (SAMM) to selectively encode semantically significant features of the images. SQ-GAN outperforms state-of-the-art image compression schemes such as JPEG2000 and BPG across multiple metrics, including perceptual quality and semantic segmentation accuracy on the post-decoding reconstructed image, at extreme low compression rates expressed in bits per pixel.