pwc-archive/papers-with-abstracts huggingface dataset:
Floods do not sink prices, historical memory does: How flood risk impacts the Italian housing market
Authors: Anna Bellaver, Lorenzo Costantini, Ariadna Fosch, Anna Monticelli, David Scala, Marco Pangallo
Date: 2025-02-17 00:00:00

Do home prices incorporate flood risk in the immediate aftermath of specific flood events, or is it the repeated exposure over the years that plays a more significant role? We address this question through the first systematic study of the Italian housing market, which is an ideal case study because it is highly exposed to floods, though unevenly distributed across the national territory. Using a novel dataset containing about 550,000 mortgage-financed transactions between 2016 and 2024, as well as hedonic regressions and a difference-in-difference design, we find that: (i) specific floods do not decrease home prices in areas at risk; (ii) the repeated exposure to floods in flood-prone areas leads to a price decline, up to 4\% in the most frequently flooded regions; (iii) responses are heterogeneous by buyers' income and age. Young buyers (with limited exposure to prior floods) do not obtain any price reduction for settling in risky areas, while experienced buyers do. At the same time, buyers who settle in risky areas have lower incomes than buyers in safe areas in the most affected regions. Our results emphasize the importance of cultural and institutional factors in understanding how flood risk affects the housing market and socioeconomic outcomes.
Bribery, Secrecy, and Communication: Theory and Evidence from Firms
Authors: Jafar M. Olimov
Date: 2025-02-15 00:00:00

This paper studies if firms pay different types of bribes, and if corrupt bureaucrats have perfect information about resources of bribe-paying firms. We construct a model of corruption that allows for multiple informational scenarios in a single market for bribes and empirically test these scenarios on the original dataset of 429 firms operating in Tajikistan. The results indicate that firms simultaneously make voluntary and involuntary bribe payments, firms hide resources from corrupt bureaucrats to reduce involuntary bribe payments, and bureaucrats who receive voluntary bribe payments do not share bribery-relevant information with other bureaucrats.
From Policy to Practice. Upper Bound Cost Estimates of Europes Green Hydrogen Ambitions
Authors: E. Hordvei, S. Hummelen, M. Petersen, S. Backe, P. Granado
Date: 2025-02-15 00:00:00

As the European countries strive to meet their ambitious climate goals, renewable hydrogen has emerged to aid in decarbonizing energy-intensive sectors and support the overall energy transition. To ensure that hydrogen production aligns with these goals, the European Commission has introduced criteria for additionality, temporal correlation, and geographical correlation. These criteria are designed to ensure that hydrogen production from renewable sources supports the growth of renewable energy. This study assesses the impact of these criteria on green hydrogen production, focusing on production costs and technology impacts. The European energy market is simulated up to 2048 using stochastic programming, applying these requirements exclusively to green hydrogen production without the phased-in compliance period outlined in the EU regulations. The findings show that meeting the criteria will increase expected system costs by 82 EUR billion from 2024 to 2048, largely due to the rapid shift from fossil fuels to renewable energy. The additionality requirement, which mandates the use of new renewable energy installations for electrolysis, proves to be the most expensive, but also the most effective in accelerating renewable energy adoption.
What are Asset Price Bubbles? A Survey on Definitions of Financial Bubbles
Authors: Michael Heinrich Baumann, Anja Janischewski
Date: 2025-02-14 00:00:00

Financial bubbles and crashes have repeatedly caused economic turmoil notably but not only during the 2008 financial crisis. However, both in the popular press as well as scientific publications, the meaning of bubble is sometimes unspecified. Due to the multitude of bubble definitions, we conduct a systematic review with the following questions: What definitions of asset price bubbles exist in the literature? Which definitions are used in which disciplines and how frequently? We develop a system of definition categories and categorize a total of 122 papers from eleven research areas. Our results show that although one definition is indeed prevalent in the literature, the overall definition landscape is not uniform. Next to the mostly used definition as deviation from a present value of expected future cash flows, we identify several other definitions, which rely on price properties or other specifications of a fundamental value. This research contributes by shedding light on the possible variations in which bubbles are defined and operationalized.
ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy?
Authors: Jian Chen, Guohao Tang, Guofu Zhou, Wu Zhu
Date: 2025-02-14 00:00:00

We study whether ChatGPT and DeepSeek can extract information from the Wall Street Journal to predict the stock market and the macroeconomy. We find that ChatGPT has predictive power. DeepSeek underperforms ChatGPT, which is trained more extensively in English. Other large language models also underperform. Consistent with financial theories, the predictability is driven by investors' underreaction to positive news, especially during periods of economic downturn and high information uncertainty. Negative news correlates with returns but lacks predictive value. At present, ChatGPT appears to be the only model capable of capturing economic news that links to the market risk premium.
Quality thinning and value development of boreal trees on spruce-dominated stands
Authors: Petri P. Karenlampi
Date: 2025-02-13 00:00:00

For the first time, quality distribution of trees is introduced in a tree growth model. Consequently, the effects of quality thinning on stand development can be investigated. Quality thinning improves the financial return in all cases studied, but the effect is small. Rotation ages, timber stocks and maturity diameters are not much affected by quality thinning. Bare land valuation neither changes the contribution of the quality thinning. The reason for the small effect apparently lies in the value development of individual trees. The relative value development of small pulpwood trunks is large, since the harvesting expense per volume unit is reduced along with size increment. Such trees are not feasible objects for quality thinning, unless quality correlates with growth rate. Another enhanced stage of value development is when pulpwood trunks turn to sawlog trunks. For large pulpwood trunks, quality thinning is feasible. Existing sawlog content in trees dilutes the effect of quality thinning on the financial return. The results change if the growth rate is positively correlated with quality, quality thinning becoming feasible in all commercial diameter classes.
Analysis of the Order Flow Auction under Proposer-Builder Separation
Authors: Ruofei Ma, Wenpin Tang, David Yao
Date: 2025-02-17 00:00:00

In this paper, we consider the impact of the order flow auction (OFA) in the context of the proposer-builder separation (PBS) mechanism through a game-theoretic perspective. The OFA is designed to improve user welfare by redistributing maximal extractable value (MEV) to the users, in which two auctions take place: the order flow auction and the block-building auction. We formulate the OFA as a multiplayer game, and focus our analyses on the case of two competing players (builders). We prove the existence and uniqueness of a Nash equilibrium for the two-player game, and derive a closed-form solution by solving a quartic equation. Our result shows that the builder with a competitive advantage pays a relatively lower cost, leading to centralization in the builder space. In contrast, the proposer's shares evolve as a martingale process, which implies decentralization in the proposer (or, validator) space. Our analyses rely on various tools from stochastic processes, convex optimization, and polynomial equations. We also conduct numerical studies to corroborate our findings, and explore other features of the OFA under the PBS mechanism.
Selling Multiple Items to a Unit-Demand Buyer via Automated Mechanism Design
Authors: Kento Hashimoto, Keita Kuwahara, Reo Nonaka
Date: 2025-02-14 00:00:00

Finding the optimal (revenue-maximizing) mechanism to sell multiple items has been a prominent and notoriously difficult open problem. Existing work has mainly focused on deriving analytical results tailored to a particular class of problems (for example, Giannakopoulos (2015) and Yang (2023)). The present paper explores the possibility of a generally applicable methodology of the Automated Mechanism Design (AMD). We first employ the deep learning algorithm developed by D\"utting et al. (2023) to numerically solve small-sized problems, and the results are then generalized by educated guesswork and finally rigorously verified through duality. By focusing on a single buyer who can consume one item, our approach leads to two key contributions: establishing a much simpler way to verify the optimality of a wide range of problems and discovering a completely new result about the optimality of grand bundling. First, we show that selling each item at an identical price (or equivalently, selling the grand bundle of all items) is optimal for any number of items when the value distributions belong to a class that includes the uniform distribution as a special case. Different items are allowed to have different distributions. Second, for each number of items, we established necessary and sufficient conditions that $c$ must satisfy for grand bundling to be optimal when the value distribution is uniform over an interval $[c, c + 1]$. This latter model does not satisfy the previously known sufficient conditions for the optimality of grand bundling Haghpanah and Hartline (2021). Our results are in contrast to the only known results for $n$ items (for any $n$), Giannakopoulos (2015) and Daskalakis et al. (2017), which consider a single buyer with additive preferences, where the values of items are narrowly restricted to i.i.d. according to a uniform or exponential distribution.
Strategic Wealth Accumulation Under Transformative AI Expectations
Authors: Caleb Maresca
Date: 2025-02-16 00:00:00

This paper analyzes how expectations of Transformative AI (TAI) affect current economic behavior by introducing a novel mechanism where automation redirects labor income from workers to those controlling AI systems, with the share of automated labor controlled by each household depending on their wealth at the time of invention. Using a modified neoclassical growth model calibrated to contemporary AI timeline forecasts, I find that even moderate assumptions about wealth-based allocation of AI labor generate substantial increases in pre-TAI interest rates. Under baseline scenarios with proportional wealth-based allocation, one-year interest rates rise to 10-16% compared to approximately 3% without strategic competition. The model reveals a notable divergence between interest rates and capital rental rates, as households accept lower productive returns in exchange for the strategic value of wealth accumulation. These findings suggest that evolving beliefs about TAI could create significant upward pressure on interest rates well before any technological breakthrough occurs, with important implications for monetary policy and financial stability.
Narrow Bracketing and Risk in Games
Authors: Fedor Sandomirskiy, Po Hyun Sung, Omer Tamuz, Ben Wincelberg
Date: 2025-02-16 00:00:00

We study finite normal-form games under a narrow bracketing assumption: when players play several games simultaneously, they consider each one separately. We show that under mild additional assumptions, players must play either Nash equilibria, logit quantal response equilibria, or their generalizations, which capture players with various risk attitudes.
Improving Acoustic Side-Channel Attacks on Keyboards Using Transformers and Large Language Models
Authors: Jin Hyun Park, Seyyed Ali Ayati, Yichen Cai
Date: 2025-02-13 00:00:00

The increasing prevalence of microphones in everyday devices and the growing reliance on online services have amplified the risk of acoustic side-channel attacks (ASCAs) targeting keyboards. This study explores deep learning techniques, specifically vision transformers (VTs) and large language models (LLMs), to enhance the effectiveness and applicability of such attacks. We present substantial improvements over prior research, with the CoAtNet model achieving state-of-the-art performance. Our CoAtNet shows a 5.0% improvement for keystrokes recorded via smartphone (Phone) and 5.9% for those recorded via Zoom compared to previous benchmarks. We also evaluate transformer architectures and language models, with the best VT model matching CoAtNet's performance. A key advancement is the introduction of a noise mitigation method for real-world scenarios. By using LLMs for contextual understanding, we detect and correct erroneous keystrokes in noisy environments, enhancing ASCA performance. Additionally, fine-tuned lightweight language models with Low-Rank Adaptation (LoRA) deliver comparable performance to heavyweight models with 67X more parameters. This integration of VTs and LLMs improves the practical applicability of ASCA mitigation, marking the first use of these technologies to address ASCAs and error correction in real-world scenarios.
SpeechT-RAG: Reliable Depression Detection in LLMs with Retrieval-Augmented Generation Using Speech Timing Information
Authors: Xiangyu Zhang, Hexin Liu, Qiquan Zhang, Beena Ahmed, Julien Epps
Date: 2025-02-16 00:00:00
Tasks: Depression Detection, RAG, Retrieval, Retrieval-augmented Generation, Uncertainty Quantification

Large Language Models (LLMs) have been increasingly adopted for health-related tasks, yet their performance in depression detection remains limited when relying solely on text input. While Retrieval-Augmented Generation (RAG) typically enhances LLM capabilities, our experiments indicate that traditional text-based RAG systems struggle to significantly improve depression detection accuracy. This challenge stems partly from the rich depression-relevant information encoded in acoustic speech patterns information that current text-only approaches fail to capture effectively. To address this limitation, we conduct a systematic analysis of temporal speech patterns, comparing healthy individuals with those experiencing depression. Based on our findings, we introduce Speech Timing-based Retrieval-Augmented Generation, SpeechT-RAG, a novel system that leverages speech timing features for both accurate depression detection and reliable confidence estimation. This integrated approach not only outperforms traditional text-based RAG systems in detection accuracy but also enhances uncertainty quantification through a confidence scoring mechanism that naturally extends from the same temporal features. Our unified framework achieves comparable results to fine-tuned LLMs without additional training while simultaneously addressing the fundamental requirements for both accuracy and trustworthiness in mental health assessment.
OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models
Authors: William Chen, Jinchuan Tian, Yifan Peng, Brian Yan, Chao-Han Huck Yang, Shinji Watanabe
Date: 2025-02-14 00:00:00
Tasks: speech-recognition, Speech Recognition, Translation

Neural scaling laws offer valuable insights for designing robust sequence processing architectures. While these laws have been extensively characterized in other modalities, their behavior in speech remains comparatively underexplored. In this work, we introduce OWLS, an open-access, reproducible suite of multilingual speech recognition and translation models spanning 0.25B to 18B parameters, with the 18B version being the largest speech model, to the best of our knowledge. OWLS leverages up to 360K hours of public speech data across 150 languages, enabling a systematic investigation into how data, model, and compute scaling each influence performance in multilingual speech tasks. We use OWLS to derive neural scaling laws, showing how final performance can be reliably predicted when scaling. One of our key findings is that scaling enhances performance on low-resource languages/dialects, helping to mitigate bias and improve the accessibility of speech technologies. Finally, we show how OWLS can be used to power new research directions by discovering emergent abilities in large-scale speech models. Model checkpoints will be released on https://huggingface.co/collections/espnet/owls-scaling-laws-for-speech-recognition-and-translation-67ab7f991c194065f057ce8d for future studies.
MTLM: Incorporating Bidirectional Text Information to Enhance Language Model Training in Speech Recognition Systems
Authors: Qingliang Meng, Pengju Ren, Tian Li, Changsong Dai, HuiZhi Liang
Date: 2025-02-14 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Automatic speech recognition (ASR) systems normally consist of an acoustic model (AM) and a language model (LM). The acoustic model estimates the probability distribution of text given the input speech, while the language model calibrates this distribution toward a specific knowledge domain to produce the final transcription. Traditional ASR-specific LMs are typically trained in a unidirectional (left-to-right) manner to align with autoregressive decoding. However, this restricts the model from leveraging the right-side context during training, limiting its representational capacity. In this work, we propose MTLM, a novel training paradigm that unifies unidirectional and bidirectional manners through 3 training objectives: ULM, BMLM, and UMLM. This approach enhances the LM's ability to capture richer linguistic patterns from both left and right contexts while preserving compatibility with standard ASR autoregressive decoding methods. As a result, the MTLM model not only enhances the ASR system's performance but also support multiple decoding strategies, including shallow fusion, unidirectional/bidirectional n-best rescoring. Experiments on the LibriSpeech dataset show that MTLM consistently outperforms unidirectional training across multiple decoding strategies, highlighting its effectiveness and flexibility in ASR applications.
Microphone Array Geometry Independent Multi-Talker Distant ASR: NTT System for the DASR Task of the CHiME-8 Challenge
Authors: Naoyuki Kamo, Naohiro Tawara, Atsushi Ando, Takatomo Kano, Hiroshi Sato, Rintaro Ikeshita, Takafumi Moriya, Shota Horiguchi, Kohei Matsuura, Atsunori Ogawa, Alexis Plaquet, Takanori Ashihara, Tsubasa Ochiai, Masato Mimura, Marc Delcroix, Tomohiro Nakatani, Taichi Asami, Shoko Araki
Date: 2025-02-14 00:00:00
Tasks: Action Detection, Activity Detection, Automatic Speech Recognition, speaker-diarization, Speaker Diarization, Speech Enhancement, speech-recognition, Speech Recognition

In this paper, we introduce a multi-talker distant automatic speech recognition (DASR) system we designed for the DASR task 1 of the CHiME-8 challenge. Our system performs speaker counting, diarization, and ASR. It handles various recording conditions, from diner parties to professional meetings and from two to eight speakers. We perform diarization first, followed by speech enhancement, and then ASR as the challenge baseline. However, we introduced several key refinements. First, we derived a powerful speaker diarization relying on end-to-end speaker diarization with vector clustering (EEND-VC), multi-channel speaker counting using enhanced embeddings from EEND-VC, and target-speaker voice activity detection (TS-VAD). For speech enhancement, we introduced a novel microphone selection rule to better select the most relevant microphones among the distributed microphones and investigated improvements to beamforming. Finally, for ASR, we developed several models exploiting Whisper and WavLM speech foundation models. We present the results we submitted to the challenge and updated results we obtained afterward. Our strongest system achieves a 63% relative macro tcpWER improvement over the baseline and outperforms the challenge best results on the NOTSOFAR-1 meeting evaluation data among geometry-independent systems.
MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition
Authors: Sungnyun Kim, Kangwook Jang, Sangmin Bae, Sungwoo Cho, Se-Young Yun
Date: 2025-02-11 00:00:00
Tasks: Audio-Visual Speech Recognition, Computational Efficiency, Mixture-of-Experts, Robust Speech Recognition, speech-recognition, Speech Recognition, Visual Speech Recognition

Audio-visual speech recognition (AVSR) has become critical for enhancing speech recognition in noisy environments by integrating both auditory and visual modalities. However, existing AVSR systems struggle to scale up without compromising computational efficiency. In this study, we introduce MoHAVE (Mixture of Hierarchical Audio-Visual Experts), a novel robust AVSR framework designed to address these scalability constraints. By leveraging a Mixture-of-Experts (MoE) architecture, MoHAVE activates modality-specific expert groups, ensuring dynamic adaptation to various audio-visual inputs with minimal computational overhead. Key contributions of MoHAVE include: (1) a sparse MoE framework that efficiently scales AVSR model capacity, (2) a hierarchical gating mechanism that dynamically utilizes the expert groups based on input context, enhancing adaptability and robustness, and (3) remarkable performance across robust AVSR benchmarks, including LRS3 and MuAViC transcription and translation tasks, setting a new standard for scalable speech recognition systems.
On Quantizing Neural Representation for Variable-Rate Video Coding
Authors: Junqi Shi, Zhujia Chen, Hanfei Li, Qi Zhao, Ming Lu, Tong Chen, Zhan Ma
Date: 2025-02-17 00:00:00
Tasks: Quantization

This work introduces NeuroQuant, a novel post-training quantization (PTQ) approach tailored to non-generalized Implicit Neural Representations for variable-rate Video Coding (INR-VC). Unlike existing methods that require extensive weight retraining for each target bitrate, we hypothesize that variable-rate coding can be achieved by adjusting quantization parameters (QPs) of pre-trained weights. Our study reveals that traditional quantization methods, which assume inter-layer independence, are ineffective for non-generalized INR-VC models due to significant dependencies across layers. To address this, we redefine variable-rate INR-VC as a mixed-precision quantization problem and establish a theoretical framework for sensitivity criteria aimed at simplified, fine-grained rate control. Additionally, we propose network-wise calibration and channel-wise quantization strategies to minimize quantization-induced errors, arriving at a unified formula for representation-oriented PTQ calibration. Our experimental evaluations demonstrate that NeuroQuant significantly outperforms existing techniques in varying bitwidth quantization and compression efficiency, accelerating encoding by up to eight times and enabling quantization down to INT2 with minimal reconstruction loss. This work introduces variable-rate INR-VC for the first time and lays a theoretical foundation for future research in rate-distortion optimization, advancing the field of video coding technology. The materials will be available at https://github.com/Eric-qi/NeuroQuant.