pwc-archive/papers-with-abstracts huggingface dataset:
Efficient low-carbon development in green hydrogen and ammonia economy: a case of Ukraine
Authors: Wadim Strielkowski
Date: 2025-03-28 00:00:00

This paper focuses on assessing the potentials for the efficient low carbon development in green hydrogen and ammonia economy using an example of Ukraine as a case study. It describes the country prerequisites for the transition to renewable energy sources and outlines the ongoing green hydrogen projects. Moreover, it offers a comprehensive SWOT analysis of Ukraine engagement in the green hydrogen and ammonia economy. Furthermore, the paper employs a comprehensive bibliometric network analysis using a sample of 204 selected publications indexed in the Web of Science database. It is doing so by carrying out the network cluster analysis using the text data as well as the bibliometric data with the help of VOSViewer software. The results and outcomes might be helpful for researchers, stakeholders, and policymakers alike in devising effective strategies and policies aimed at rebuilding and recreating Ukrainian efficient low-carbon energy sector based on the renewable energy sources.
The Quantum Reserve Token: A Decentralized Digital Currency Backed by Quantum Computational Capacity as a Candidate for Global Reserve Status
Authors: Amarendra Sharma
Date: 2025-03-28 00:00:00

The U.S. dollar's status as the global reserve currency faces growing challenges from a 36 trillion dollar national debt, geopolitical shifts, and the emergence of digital currencies. This paper introduces the Quantum Reserve Token (QRT), a decentralized digital currency backed by quantum computational capacity - a scarce, productive resource projected to add over 1 trillion dollars to global GDP by 2035. Unlike Bitcoin's fixed-supply volatility, stablecoins' dependence on fiat trust, or central bank digital currencies' jurisdictional limits, QRT uses quantum computing power as a novel value anchor. This study develops a monetary theory-based framework for QRT, compares it to existing digital currency models, and evaluates its feasibility across technological, economic, geopolitical, and adoption dimensions. QRT offers a stable, neutral, and scalable reserve currency alternative, potentially reshaping the global monetary system.
Gene-environment interplay and public policies
Authors: Dilnoza Muslimova, Niels Rietveld
Date: 2025-03-27 00:00:00

We posit that gene-environment interplay (GxE) studies should be developed both theoretically and empirically to be of relevance to policy makers. On the theoretical front, this development is essential because the current literature lacks the integration of a clear framework capturing the various goals of public policies. Empirically, GxE models need to be further developed because the common way of modelling GxE effects fails to adequately capture the heterogeneous effects public policies may have along the distribution of genetic propensities (as captured by polygenic indices). We fill these gaps by proposing a policy classification for GxE research and by offering guidance on advancing the empirical modelling of policy-informative GxE interplay. While doing so, we provide a systematic review of existing GxE studies on educational outcomes exploiting policy reforms or environments that could be targeted by public policy.
The "Days of Learning" Metric for Education Evaluations
Authors: Gregory Camilli
Date: 2025-03-26 00:00:00

The third National Charter School Study aimed to test whether charter school were effective and to highlight outcomes on academic progress. The results showed that typical charter school students outperformed similar students in noncharter public schools by 6 days in mathematics and 16 days in reading. The key metric used to claim better performance in charter schools is "days of learning." In this report, the origin of the days-of-learning metric is explored with NAEP data, and the interpretation of days of learning as within-year growth is considered.
Entrepreneurial Motivations and ESG Performance Evidence from Automobile Companies Listed on the Chinese Stock Exchange
Authors: Jun Cui
Date: 2025-03-26 00:00:00

This study explores the impact of entrepreneurial motivations on ESG performance in Chinese stock exchange listed automobile companies. Using quantitative methods and empirical analysis via STATA software, the research examines baseline stability, endogeneity, heterogeneity, and mediation/moderation mechanisms. A sample of 50 firms from the Shanghai and Shenzhen Stock Exchanges 2003 and 2023 was analyzed. Results indicate that entrepreneurial motivations positively influence ESG performance. mediated by innovation capability and moderated by market competition intensity. These findings offer theoretical and practical insights, aligning with Stakeholder and institutional theories. The study provides a robust framework for understanding strategic ESG behavior in Chinas automobile sector.
Sino-US S and T Frictions and Transnational Knowledge Flows: Evidence from machine learning and cross-national patent data
Authors: Yanqing Yang, Nan Zhang, Jinfeng Ge, Yan Xu
Date: 2025-03-26 00:00:00
Tasks: Friction

This paper identifies the impact of China-U.S. science and technology (S&T) friction on knowledge flows in different fields, using data on invention patent applications from China, the U.S., Europe, and the World Patent Office (WPO) along with machine-learning-based econometric methods. The empirical results find that the negative impacts of China-U.S. S&T frictions on cross-border knowledge flows are confined to a limited number of technology areas during the period of our observation. This paper further explores the characteristics of the negatively impacted technology areas, and the empirical results show that technology areas that rely more on basic scientific research, where the distribution of U.S. scientific and technological strength is more concentrated, and where the gap between U.S. and Chinese science and technology is narrower, are more likely to be the victims of the Sino-U.S. S&T friction.
Roommates with Convex Preferences
Authors: Sophie Bade
Date: 2025-03-31 00:00:00

Roommate problems with convex preferences always have stable matchings. Efficiency and individual rationality are, moreover, compatible with strategyproofness in such convex roommate problems. Both of these results fail without the assumption of convexity. In the environment under study, preferences are convex if and only if they are single peaked. Any individually rational and convex roommate problem is homomorphic to a marriage market where an agent's gender corresponds to the direction of the agent's top-ranked partner. The existence of stable matchings then follows from the existence of stable matchings in marriage markets. To prove the second existence result, I define an efficient, individually rational, and strategyproof mechanism for convex roommate problems. To calculate outcomes, this mechanism starts with all agents being single and then gradually reassigns agents to better partners by performing minimal Pareto improvements. Whenever it becomes clear that some agent cannot be part of any further Pareto improvement, such an agent is matched.
Credit, Land Speculation, and Low-Interest-Rate Policy
Authors: Tomohiro Hirano, Joseph E. Stiglitz
Date: 2025-03-30 00:00:00

This paper analyses the impact of credit expansions arising from increases in collateral values or lower interest rate policies on long-run productivity and economic growth in a two-sector endogenous growth economy with credit frictions, with the driver of growth lying in one sector (manufacturing) but not in the other (real estate). We show that it is not so much aggregate credit expansion that matters for long-run productivity and economic growth but sectoral credit expansions. Credit expansions associated mainly with relaxation of real estate financing (capital investment financing) will be productivity-and growth-retarding (enhancing). Without financial regulations, low interest rates and more expansionary monetary policy may so encourage land speculation using leverage that productive capital investment and economic growth are decreased. Unlike in standard macroeconomic models, in ours, the equilibrium price of land will be finite even if the safe rate of interest is less than the rate of output growth.
A Note on the Definition of Strategies in Opportunity Hunting Games
Authors: Ran Eilat, Zvika Neeman, Eilon Solan
Date: 2025-03-30 00:00:00

In this note, we propose an alternative definition of strategies and histories in opportunity hunting games, as introduced in Eilat, Neeman and Solan (2025). The advantage of the formulation presented here is that it encompasses a broader class of strategies than the inertial strategies analyzed in Eilat, Neeman and Solan (2025), including those that allow for a countably infinite number of inspections within finite time intervals. Although the definitions of histories and strategies in this broader setting are more subtle, we show that the main results continue to hold. This demonstrates that the findings in Eilat, Neeman and Solan (2025) are robust to environments in which players have access to a richer strategy space.
Manipulation of positional social choice correspondences under incomplete information
Authors: Raffaele Berzi, Daniela Bubboloni, Michele Gori
Date: 2025-03-29 00:00:00

We study the manipulability of social choice correspondences in situations where individuals have incomplete information about others' preferences. We propose a general concept of manipulability that depends on the extension rule used to derive preferences over sets of alternatives from preferences over alternatives, as well as on individuals' level of information. We then focus on the manipulability of social choice correspondences when the Kelly extension rule is used, and individuals are assumed to have the capability to anticipate the outcome of the collective decision. Under these assumptions, we introduce some monotonicity properties of social choice correspondences whose combined satisfaction is sufficient for manipulability, prove a result of manipulability for unanimous positional social choice correspondences, and present a detailed analysis of the manipulability properties for the Borda, the Plurality and the Negative Plurality social choice correspondences.
Aud-Sur: An Audio Analyzer Assistant for Audio Surveillance Applications
Authors: Phat Lam, Lam Pham, Dat Tran, Alexander Schindler, Silvia Poletti, Marcel Hasenbalg, David Fischinger, Martin Boyer
Date: 2025-03-31 00:00:00
Tasks: Information Retrieval, Language Modeling, Language Modelling, Large Language Model, Retrieval

In this paper, we present an audio analyzer assistant tool designed for a wide range of audio-based surveillance applications (This work is a part of our DEFAME FAKES and EUCINF projects). The proposed tool, refered to as Aud-Sur, comprises two main phases Audio Analysis and Audio Retrieval, respectively. In the first phase, multiple open-source audio models are leveraged to extract information from input audio recording uploaded by a user. In the second phase, users interact with the Aud-Sur tool via a natural question-and-answer manner, powered by a large language model (LLM), to retrieve the information extracted from the processed audio file. The Aud-Sur tool was deployed using Docker on a microservices-based architecture design. By leveraging open-source audio models for information extraction, LLM for audio information retrieval, and a microservices-based deployment approach, the proposed Aud-Sur tool offers a highly extensible and adaptable framework that can integrate more audio tasks, and be widely shared within the audio community for further development.
A first-order DirAC-based parametric Ambisonic coder for immersive communications
Authors: Guillaume Fuchs, Florin Ghido, Dominik Weckbecker, Oliver Thiergart
Date: 2025-03-30 00:00:00

Directional Audio Coding (DirAC) is a proven method for parametrically representing a 3D audio scene in B-format and is capable of reproducing it on arbitrary loudspeaker layouts. Although such a method seems well suited for low bitrate Ambisonic transmission, little work has been done on the feasibility of building a real system upon it. In this paper, we present a DirAC-based coding for Higher-Order Ambisonics (HOA), developed as part of a standardisation effort to extend the 3GPP EVS codec to immersive communications. Starting from the first-order DirAC model, we show how to reduce algorithmic delay, the bitrate required for the parameters and complexity by bringing the full synthesis in the spherical harmonic domain. The evaluation of the proposed technique for coding 3\textsuperscript{rd} order Ambisonics at bitrates from 32 to 128 kbps shows the relevance of the parametric approach compared with existing solutions.
The trajectoRIR Database: Room Acoustic Recordings Along a Trajectory of Moving Microphones
Authors: Stefano Damiano, Kathleen MacWilliam, Valerio Lorenzoni, Thomas Dietzen, Toon van Waterschoot
Date: 2025-03-29 00:00:00
Tasks: Sound Source Localization

Data availability is essential to develop acoustic signal processing algorithms, especially when it comes to data-driven approaches that demand large and diverse training datasets. For this reason, an increasing number of databases have been published in recent years, including either room impulse responses (RIRs) or audio recordings during motion. In this paper we introduce the trajectoRIR database, an extensive, multi-array collection of both dynamic and stationary acoustic recordings along a controlled trajectory in a room. Specifically, the database features recordings using moving microphones and stationary RIRs spatially sampling the room acoustics along an L-shaped trajectory. This combination makes trajectoRIR unique and applicable in various tasks ranging from sound source localization and tracking to spatially dynamic sound field reconstruction, auralization and system identification. The recording room has a reverberation time of 0.5 seconds, and the three different microphone configurations employed include a dummy head, with additional reference microphones located next to the ears, 3 first-order Ambisonics microphones, two circular arrays of 16 and 4 channels, and a 12-channel linear array. The motion of the microphones was achieved using a robotic cart traversing a 4.62 meter-long rail at three speeds: [0.2, 0.4, 0.8] m/s. Audio signals were reproduced using two stationary loudspeakers. The collected database features 8648 stationary RIRs, as well as perfect sweeps, speech, music, and stationary noise recorded during motion. Python functions are included to access the recorded audio as well as to retrieve geometrical information.
M2D2: Exploring General-purpose Audio-Language Representations Beyond CLAP
Authors: Daisuke Niizumi, Daiki Takeuchi, Masahiro Yasuda, Binh Thien Nguyen, Yasunori Ohishi, Noboru Harada
Date: 2025-03-28 00:00:00
Tasks: Audio captioning, Audio Classification, Audio Tagging, Audio to Text Retrieval, Emotion Recognition, Instrument Recognition, Music Auto-Tagging, Music Classification, Music Genre Classification, Music Tagging, Representation Learning, Self-Supervised Learning, Sentence Embeddings, Singer Identification, Text Retrieval, Text to Audio Retrieval, Vocal technique classification

Contrastive language-audio pre-training (CLAP) has addressed audio-language tasks such as audio-text retrieval by aligning audio and text in a common feature space. While CLAP addresses general audio-language tasks, its audio features do not generalize well in audio tasks. In contrast, self-supervised learning (SSL) models learn general-purpose audio features that perform well in diverse audio tasks. We pursue representation learning that can be widely used in audio applications and hypothesize that a method that learns both general audio features and CLAP features should achieve our goal, which we call a general-purpose audio-language representation. To implement our hypothesis, we propose M2D2, a second-generation masked modeling duo (M2D) that combines an SSL M2D and CLAP. M2D2 learns two types of features using two modalities (audio and text) in a two-stage training process. It also utilizes advanced LLM-based sentence embeddings in CLAP training for powerful semantic supervision. In the first stage, M2D2 learns generalizable audio features from M2D and CLAP, where CLAP aligns the features with the fine LLM-based semantic embeddings. In the second stage, it learns CLAP features using the audio features learned from the LLM-based embeddings. Through these pre-training stages, M2D2 should enhance generalizability and performance in its audio and CLAP features. Experiments validated that M2D2 achieves effective general-purpose audio-language representation, highlighted with SOTA fine-tuning mAP of 49.0 for AudioSet, SOTA performance in music tasks, and top-level performance in audio-language tasks.
Enhancing nonnative speech perception and production through an AI-powered application
Authors: Georgios P. Georgiou
Date: 2025-03-18 00:00:00
Tasks: Sentence

While research on using Artificial Intelligence (AI) through various applications to enhance foreign language pronunciation is expanding, it has primarily focused on aspects such as comprehensibility and intelligibility, largely neglecting the improvement of individual speech sounds in both perception and production. This study seeks to address this gap by examining the impact of training with an AI-powered mobile application on nonnative sound perception and production. Participants completed a pretest assessing their ability to discriminate the second language English heed-hid contrast and produce these vowels in sentence contexts. The intervention involved training with the Speakometer mobile application, which incorporated recording tasks featuring the English vowels, along with pronunciation feedback and practice. The posttest mirrored the pretest to measure changes in performance. The results revealed significant improvements in both discrimination accuracy and production of the target contrast following the intervention. However, participants did not achieve native-like competence. These findings highlight the effectiveness of AI-powered applications in facilitating speech acquisition and support their potential use for personalized, interactive pronunciation training beyond the classroom.
Enhancing Aviation Communication Transcription: Fine-Tuning Distil-Whisper with LoRA
Authors: Shokoufeh Mirzaei, Jesse Arzate, Yukti Vijay
Date: 2025-03-13 00:00:00
Tasks: Automatic Speech Recognition, parameter-efficient fine-tuning, speech-recognition, Speech Recognition

Transcription of aviation communications has several applications, from assisting air traffic controllers in identifying the accuracy of read-back errors to search and rescue operations. Recent advances in artificial intelligence have provided unprecedented opportunities for improving aviation communication transcription tasks. OpenAI's Whisper is one of the leading automatic speech recognition models. However, fine-tuning Whisper for aviation communication transcription is not computationally efficient. Thus, this paper aims to use a Parameter-Efficient Fine-tuning method called Low-Rank Adaptation to fine-tune a more computationally efficient version of Whisper, distil-Whisper. To perform the fine-tuning, we used the Air Traffic Control Corpus dataset from the Linguistic Data Consortium, which contains approximately 70 hours of controller and pilot transmissions near three major airports in the US. The objective was to reduce the word error rate to enhance accuracy in the transcription of aviation communication. First, starting with an initial set of hyperparameters for LoRA (Alpha = 64 and Rank = 32), we performed a grid search. We applied a 5-fold cross-validation to find the best combination of distil-Whisper hyperparameters. Then, we fine-tuned the model for LoRA hyperparameters, achieving an impressive average word error rate of 3.86% across five folds. This result highlights the model's potential for use in the cockpit.
Qieemo: Speech Is All You Need in the Emotion Recognition in Conversations
Authors: Jinming Chen, Jingyi Fang, Yuanzhong Zheng, Yaoxuan Wang, Haojun Fei
Date: 2025-03-05 00:00:00
Tasks: All, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Emotion Classification, Emotion Recognition, speech-recognition, Speech Recognition

Emotion recognition plays a pivotal role in intelligent human-machine interaction systems. Multimodal approaches benefit from the fusion of diverse modalities, thereby improving the recognition accuracy. However, the lack of high-quality multimodal data and the challenge of achieving optimal alignment between different modalities significantly limit the potential for improvement in multimodal approaches. In this paper, the proposed Qieemo framework effectively utilizes the pretrained automatic speech recognition (ASR) model backbone which contains naturally frame aligned textual and emotional features, to achieve precise emotion classification solely based on the audio modality. Furthermore, we design the multimodal fusion (MMF) module and cross-modal attention (CMA) module in order to fuse the phonetic posteriorgram (PPG) and emotional features extracted by the ASR encoder for improving recognition accuracy. The experimental results on the IEMOCAP dataset demonstrate that Qieemo outperforms the benchmark unimodal, multimodal, and self-supervised models with absolute improvements of 3.0%, 1.2%, and 1.9% respectively.
PupiNet: Seamless OCT-OCTA Interconversion Through Wavelet-Driven and Multi-Scale Attention Mechanisms
Authors: Renzhi Tian, Jinjie Wang, Wei Yang, Weizhen Li, Haoran Chen, Yiran Zhu, Chengchang Pan, Honggang Qi
Date: 2025-03-31 00:00:00
Tasks: Diagnostic

Optical Coherence Tomography (OCT) and Optical Coherence Tomography Angiography (OCTA) are key diagnostic tools for clinical evaluation and management of retinal diseases. Compared to traditional OCT, OCTA provides richer microvascular information, but its acquisition requires specialized sensors and high-cost equipment, creating significant challenges for the clinical deployment of hardware-dependent OCTA imaging methods. Given the technical complexity of OCTA image acquisition and potential mechanical artifacts, this study proposes a bidirectional image conversion framework called PupiNet, which accurately achieves bidirectional transformation between 3D OCT and 3D OCTA. The generator module of this framework innovatively integrates wavelet transformation and multi-scale attention mechanisms, significantly enhancing image conversion quality. Meanwhile, an Adaptive Discriminator Augmentation (ADA) module has been incorporated into the discriminator to optimize model training stability and convergence efficiency. To ensure clinical accuracy of vascular structures in the converted images, we designed a Vessel Structure Matcher (VSM) supervision module, achieving precise matching of vascular morphology between generated images and target images. Additionally, the Hierarchical Feature Calibration (HFC) module further guarantees high consistency of texture details between generated images and target images across different depth levels. To rigorously validate the clinical effectiveness of the proposed method, we conducted a comprehensive evaluation on a paired OCT-OCTA image dataset containing 300 eyes with various retinal pathologies. Experimental results demonstrate that PupiNet not only reliably achieves high-quality bidirectional transformation between the two modalities but also shows significant advantages in image fidelity, vessel structure preservation, and clinical usability.