pwc-archive/papers-with-abstracts huggingface dataset:
Revisiting Global Income Convergence in the 21st Century
Authors: Bipul Verma
Date: 2024-12-20 00:00:00

Recent research has documented a reversal from divergence to convergence in income levels between rich and poor countries after 2000. This paper employs a growth accounting framework to investigate the proximate sources of convergence over the period 1980-2019. I find that while output levels began to converge only after 2000, capital (physical and human) had already been converging during 1980-2000. The divergence in total factor productivity (TFP) during this earlier period offset the gains from capital convergence, resulting in little overall income convergence. After 2000, capital maintained its pattern of convergence, and, unlike in the earlier period, TFP also began to converge. Quantitatively, more than half of the convergence between 2000 and 2019-and nearly all the convergence between 1980 and 2019 outside Sub-Saharan Africa-can be attributed to the convergence in capital rather than to improvements in TFP. These findings highlight that factor accumulation has played a much larger role in driving long-run convergence dynamics than previously documented in the literature.
A Scenario-Based Assessment of the Long-Term Funding Adequacy of the German Nuclear Waste Fund KENFO
Authors: Mahdi Awawda, Alexander Wimmers
Date: 2024-12-20 00:00:00
Tasks: Management

The German site selection process for high-level nuclear waste was initially planned to conclude in 2031, with the deep geological repository sealed around the year 2080. However, in 2022, substantial delays were announced by the responsible federal agency, pushing the site selection to 2046 or even 2068. With this delay come uncertainties regarding the duration, consequential knowledge management, and funding. German nuclear waste management activities are funded by the external segregated fund KENFO, which is designed to ensure sufficient funding via generating returns on investments (ROI) in the coming decades. Given recent developments, we assess the adequacy of the fund volume based on seven scenarios depicting potential process delays. We find that the target ROI of 3.7% will not suffice in any case, even if the site selection concludes in 2031, and that cash injections of up to EUR31.07 billion are necessary today to ensure that the fund volume will suffice. We conclude that cost estimations must be updated, KENFO must increase its target ROIs, potential capital injections must be openly discussed by policymakers, and a procedural acceleration should be implemented to ensure that financial liabilities for nuclear waste management are minimized for future taxpayers.
(Mis)information diffusion and the financial market
Authors: Tommaso Di Francesco, Daniel Torren Peraire
Date: 2024-12-20 00:00:00
Tasks: Misinformation

This paper investigates the interplay between information diffusion in social networks and its impact on financial markets with an Agent-Based Model (ABM). Agents receive and exchange information about an observable stochastic component of the dividend process of a risky asset \`a la Grossman and Stiglitz. A small proportion of the network has access to a private signal about the component, which can be clean (information) or distorted (misinformation). Other agents are uninformed and can receive information only from their peers. All agents are Bayesian, adjusting their beliefs according to the confidence they have in the source of information. We examine, by means of simulations, how information diffuses in the network and provide a framework to account for delayed absorption of shocks, that are not immediately priced as predicted by classical financial models. We investigate the effect of the network topology on the resulting asset price and evaluate under which condition misinformation diffusion can make the market more inefficient.
Countries across the world use more land for golf courses than wind or solar energy
Authors: Jann Weinand, Tristan Pelser, Max Kleinebrahm, Detlef Stolten
Date: 2024-12-19 00:00:00

Land use is a critical factor in the siting of renewable energy facilities and is often scrutinized due to perceived conflicts with other land demands. Meanwhile, substantial areas are devoted to activities such as golf, which are accessible to only a select few and have a significant land and environmental footprint. Our study shows that in countries such as the United States and the United Kingdom, far more land is allocated to golf courses than to renewable energy facilities. Areas equivalent to those currently used for golf could support the installation of up to 842 GW of solar and 659 GW of wind capacity in the top ten countries with the most golf courses. In many of these countries, this potential exceeds both current installed capacity and medium-term projections. These findings underscore the untapped potential of rethinking land use priorities to accelerate the transition to renewable energy.
Modeling Story Expectations to Understand Engagement: A Generative Framework Using LLMs
Authors: Hortense Fong, George Gui
Date: 2024-12-13 00:00:00
Tasks: Feature Engineering, Marketing

Understanding when and why consumers engage with stories is crucial for content creators and platforms. While existing theories suggest that audience beliefs of what is going to happen should play an important role in engagement decisions, empirical work has mostly focused on developing techniques to directly extract features from actual content, rather than capturing forward-looking beliefs, due to the lack of a principled way to model such beliefs in unstructured narrative data. To complement existing feature extraction techniques, this paper introduces a novel framework that leverages large language models to model audience forward-looking beliefs about how stories might unfold. Our method generates multiple potential continuations for each story and extracts features related to expectations, uncertainty, and surprise using established content analysis techniques. Applying our method to over 30,000 book chapters, we demonstrate that our framework complements existing feature engineering techniques by amplifying their marginal explanatory power on average by 31%. The results reveal that different types of engagement-continuing to read, commenting, and voting-are driven by distinct combinations of current and anticipated content features. Our framework provides a novel way to study and explore how audience forward-looking beliefs shape their engagement with narrative media, with implications for marketing strategy in content-focused industries.
Experimenting with Multi-modal Information to Predict Success of Indian IPOs
Authors: Sohom Ghosh, Arnab Maji, N Harsha Vardhan, Sudip Kumar Naskar
Date: 2024-12-08 00:00:00

With consistent growth in Indian Economy, Initial Public Offerings (IPOs) have become a popular avenue for investment. With the modern technology simplifying investments, more investors are interested in making data driven decisions while subscribing for IPOs. In this paper, we describe a machine learning and natural language processing based approach for estimating if an IPO will be successful. We have extensively studied the impact of various facts mentioned in IPO filing prospectus, macroeconomic factors, market conditions, Grey Market Price, etc. on the success of an IPO. We created two new datasets relating to the IPOs of Indian companies. Finally, we investigated how information from multiple modalities (texts, images, numbers, and categorical features) can be used for estimating the direction and underpricing with respect to opening, high and closing prices of stocks on the IPO listing day.
Unveiling the Role of Artificial Intelligence and Stock Market Growth in Achieving Carbon Neutrality in the United States: An ARDL Model Analysis
Authors: Azizul Hakim Rafi, Abdullah Al Abrar Chowdhury, Adita Sultana, Abdulla All Noman
Date: 2024-12-04 00:00:00
Tasks: Diagnostic

Given the fact that climate change has become one of the most pressing problems in many countries in recent years, specialized research on how to mitigate climate change has been adopted by many countries. Within this discussion, the influence of advanced technologies in achieving carbon neutrality has been discussed. While several studies investigated how AI and Digital innovations could be used to reduce the environmental footprint, the actual influence of AI in reducing CO2 emissions (a proxy measuring carbon footprint) has yet to be investigated. This paper studies the role of advanced technologies in general, and Artificial Intelligence (AI) and ICT use in particular, in advancing carbon neutrality in the United States, between 2021. Secondly, this paper examines how Stock Market Growth, ICT use, Gross Domestic Product (GDP), and Population affect CO2 emissions using the STIRPAT model. After examining stationarity among the variables using a variety of unit root tests, this study concluded that there are no unit root problems across all the variables, with a mixed order of integration. The ARDL bounds test for cointegration revealed that variables in this study have a long-run relationship. Moreover, the estimates revealed from the ARDL model in the short- and long-run indicated that economic growth, stock market capitalization, and population significantly contributed to the carbon emissions in both the short-run and long-run. Conversely, AI and ICT use significantly reduced carbon emissions over both periods. Furthermore, findings were confirmed to be robust using FMOLS, DOLS, and CCR estimations. Furthermore, diagnostic tests indicated the absence of serial correlation, heteroscedasticity, and specification errors and, thus, the model was robust.
Prudence and higher-order risk attitudes in the rank-dependent utility model
Authors: Ruodu Wang, Qinyu Wu
Date: 2024-12-19 00:00:00

We obtain a full characterization of consistency with respect to higher-order stochastic dominance within the rank-dependent utility model. Different from the results in the literature, we do not assume any conditions on the utility functions and the probability weighting function, such as differentiability or continuity. It turns out that the level of generality that we offer leads to models that do not have a continuous probability weighting function and yet they satisfy prudence. In particular, the corresponding probability weighting function can only have a jump at 1, and must be linear on [0,1).
Interleaved Speech-Text Language Models are Simple Streaming Text to Speech Synthesizers
Authors: Yifan Yang, Ziyang Ma, Shujie Liu, Jinyu Li, Hui Wang, Lingwei Meng, Haiyang Sun, Yuzhe Liang, Ruiyang Xu, Yuxuan Hu, Yan Lu, Rui Zhao, Xie Chen
Date: 2024-12-20 00:00:00
Tasks: Language Modeling, Language Modelling, text-to-speech, Text to Speech

This paper introduces Interleaved Speech-Text Language Model (IST-LM) for streaming zero-shot Text-to-Speech (TTS). Unlike many previous approaches, IST-LM is directly trained on interleaved sequences of text and speech tokens with a fixed ratio, eliminating the need for additional efforts in duration prediction and grapheme-to-phoneme alignment. The ratio of text chunk size to speech chunk size is crucial for the performance of IST-LM. To explore this, we conducted a comprehensive series of statistical analyses on the training data and performed correlation analysis with the final performance, uncovering several key factors: 1) the distance between speech tokens and their corresponding text tokens, 2) the number of future text tokens accessible to each speech token, and 3) the frequency of speech tokens precedes their corresponding text tokens. Experimental results demonstrate how to achieve an optimal streaming TTS system without complicated engineering optimization, which has a limited gap with the non-streaming system. IST-LM is conceptually simple and empirically powerful, paving the way for streaming TTS with minimal overhead while largely maintaining performance, showcasing broad prospects coupled with real-time text stream from LLMs.
UME: Upcycling Mixture-of-Experts for Scalable and Efficient Automatic Speech Recognition
Authors: Li Fu, Shanyong Yu, Siqi Li, Lu Fan, Youzheng Wu, Xiaodong He
Date: 2024-12-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Mixture-of-Experts, speech-recognition, Speech Recognition

Recent advancements in scaling up models have significantly improved performance in Automatic Speech Recognition (ASR) tasks. However, training large ASR models from scratch remains costly. To address this issue, we introduce UME, a novel method that efficiently Upcycles pretrained dense ASR checkpoints into larger Mixture-of-Experts (MoE) architectures. Initially, feed-forward networks are converted into MoE layers. By reusing the pretrained weights, we establish a robust foundation for the expanded model, significantly reducing optimization time. Then, layer freezing and expert balancing strategies are employed to continue training the model, further enhancing performance. Experiments on a mixture of 170k-hour Mandarin and English datasets show that UME: 1) surpasses the pretrained baseline by a margin of 11.9% relative error rate reduction while maintaining comparable latency; 2) reduces training time by up to 86.7% and achieves superior accuracy compared to training models of the same size from scratch.
Domain-Incremental Learning for Audio Classification
Authors: Manjunath Mulimani, Annamaria Mesaros
Date: 2024-12-23 00:00:00
Tasks: Audio Classification, Classification, Incremental Learning, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In this work, we propose a method for domain-incremental learning for audio classification from a sequence of datasets recorded in different acoustic conditions. Fine-tuning a model on a sequence of evolving domains or datasets leads to forgetting of previously learned knowledge. On the other hand, freezing all the layers of the model leads to the model not adapting to the new domain. In this work, our novel dynamic network architecture keeps the shared homogeneous acoustic characteristics of domains, and learns the domain-specific acoustic characteristics in incremental steps. Our approach achieves a good balance between retaining the knowledge of previously learned domains and acquiring the knowledge of the new domain. We demonstrate the effectiveness of the proposed method on incremental learning of single-label classification of acoustic scenes from European cities and Korea, and multi-label classification of audio recordings from Audioset and FSD50K datasets. The proposed approach learns to classify acoustic scenes incrementally with an average accuracy of 71.9% for the order: European cities -> Korea, and 83.4% for Korea -> European cities. In a multi-label audio classification setup, it achieves an average lwlrap of 47.5% for Audioset -> FSD50K and 40.7% for FSD50K -> Audioset.
Tandem spoofing-robust automatic speaker verification based on time-domain embeddings
Authors: Avishai Weizman, Yehuda Ben-Shimol, Itshak Lapidot
Date: 2024-12-22 00:00:00
Tasks: Speaker Verification, Voice Anti-spoofing

Spoofing-robust automatic speaker verification (SASV) systems are a crucial technology for the protection against spoofed speech. In this study, we focus on logical access attacks and introduce a novel approach to SASV tasks. A novel representation of genuine and spoofed speech is employed, based on the probability mass function (PMF) of waveform amplitudes in the time domain. This methodology generates novel time embeddings derived from the PMF of selected groups within the training set. This paper highlights the role of gender segregation and its positive impact on performance. We propose a countermeasure (CM) system that employs time-domain embeddings derived from the PMF of spoofed and genuine speech, as well as gender recognition based on male and female time-based embeddings. The method exhibits notable gender recognition capabilities, with mismatch rates of 0.94% and 1.79% for males and females, respectively. The male and female CM systems achieve an equal error rate (EER) of 8.67% and 10.12%, respectively. By integrating this approach with traditional speaker verification systems, we demonstrate improved generalization ability and tandem detection cost function evaluation using the ASVspoof2019 challenge database. Furthermore, we investigate the impact of fusing the time embedding approach with traditional CM and illustrate how this fusion enhances generalization in SASV architectures.
Incremental Disentanglement for Environment-Aware Zero-Shot Text-to-Speech Synthesis
Authors: Ye-Xin Lu, Hui-Peng Du, Zheng-Yan Sheng, Yang Ai, Zhen-Hua Ling
Date: 2024-12-22 00:00:00
Tasks: Decoder, Disentanglement, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

This paper proposes an Incremental Disentanglement-based Environment-Aware zero-shot text-to-speech (TTS) method, dubbed IDEA-TTS, that can synthesize speech for unseen speakers while preserving the acoustic characteristics of a given environment reference speech. IDEA-TTS adopts VITS as the TTS backbone. To effectively disentangle the environment, speaker, and text factors, we propose an incremental disentanglement process, where an environment estimator is designed to first decompose the environmental spectrogram into an environment mask and an enhanced spectrogram. The environment mask is then processed by an environment encoder to extract environment embeddings, while the enhanced spectrogram facilitates the subsequent disentanglement of the speaker and text factors with the condition of the speaker embeddings, which are extracted from the environmental speech using a pretrained environment-robust speaker encoder. Finally, both the speaker and environment embeddings are conditioned into the decoder for environment-aware speech generation. Experimental results demonstrate that IDEA-TTS achieves superior performance in the environment-aware TTS task, excelling in speech quality, speaker similarity, and environmental similarity. Additionally, IDEA-TTS is also capable of the acoustic environment conversion task and achieves state-of-the-art performance.
Time-Graph Frequency Representation with Singular Value Decomposition for Neural Speech Enhancement
Authors: Tingting Wang, Tianrui Wang, Meng Ge, Qiquan Zhang, Zirui Ge, Zhen Yang
Date: 2024-12-22 00:00:00
Tasks: Speech Enhancement

Time-frequency (T-F) domain methods for monaural speech enhancement have benefited from the success of deep learning. Recently, focus has been put on designing two-stream network models to predict amplitude mask and phase separately, or, coupling the amplitude and phase into Cartesian coordinates and constructing real and imaginary pairs. However, most methods suffer from the alignment modeling of amplitude and phase (real and imaginary pairs) in a two-stream network framework, which inevitably incurs performance restrictions. In this paper, we introduce a graph Fourier transform defined with the singular value decomposition (GFT-SVD), resulting in real-valued time-graph representation for neural speech enhancement. This real-valued representation-based GFT-SVD provides an ability to align the modeling of amplitude and phase, leading to avoiding recovering the target speech phase information. Our findings demonstrate the effects of real-valued time-graph representation based on GFT-SVD for neutral speech enhancement. The extensive speech enhancement experiments establish that the combination of GFT-SVD and DNN outperforms the combination of GFT with the eigenvector decomposition (GFT-EVD) and magnitude estimation UNet, and outperforms the short-time Fourier transform (STFT) and DNN, regarding objective intelligibility and perceptual quality. We release our source code at: https://github.com/Wangfighting0015/GFT\_project.
SLAM-Omni: Timbre-Controllable Voice Interaction System with Single-Stage Training
Authors: Wenxi Chen, Ziyang Ma, Ruiqi Yan, Yuzhe Liang, Xiquan Li, Ruiyang Xu, Zhikang Niu, Yanqiao Zhu, Yifan Yang, Zhanxun Liu, Kai Yu, Yuxuan Hu, Jinyu Li, Yan Lu, Shujie Liu, Xie Chen
Date: 2024-12-20 00:00:00
Tasks: Spoken Dialogue Systems

Recent advancements highlight the potential of end-to-end real-time spoken dialogue systems, showcasing their low latency and high quality. In this paper, we introduce SLAM-Omni, a timbre-controllable, end-to-end voice interaction system with single-stage training. SLAM-Omni achieves zero-shot timbre control by modeling spoken language with semantic tokens and decoupling speaker information to a vocoder. By predicting grouped speech semantic tokens at each step, our method significantly reduces the sequence length of audio tokens, accelerating both training and inference. Additionally, we propose historical text prompting to compress dialogue history, facilitating efficient multi-round interactions. Comprehensive evaluations reveal that SLAM-Omni outperforms prior models of similar scale, requiring only 15 hours of training on 4 GPUs with limited data. Notably, it is the first spoken dialogue system to achieve competitive performance with a single-stage training approach, eliminating the need for pre-training on TTS or ASR tasks. Further experiments validate its multilingual and multi-turn dialogue capabilities on larger datasets.
AsymLLIC: Asymmetric Lightweight Learned Image Compression
Authors: Shen Wang, Zhengxue Cheng, Donghui Feng, Guo Lu, Li Song, Wenjun Zhang
Date: 2024-12-23 00:00:00
Tasks: Decoder, Image Compression

Learned image compression (LIC) methods often employ symmetrical encoder and decoder architectures, evitably increasing decoding time. However, practical scenarios demand an asymmetric design, where the decoder requires low complexity to cater to diverse low-end devices, while the encoder can accommodate higher complexity to improve coding performance. In this paper, we propose an asymmetric lightweight learned image compression (AsymLLIC) architecture with a novel training scheme, enabling the gradual substitution of complex decoding modules with simpler ones. Building upon this approach, we conduct a comprehensive comparison of different decoder network structures to strike a better trade-off between complexity and compression performance. Experiment results validate the efficiency of our proposed method, which not only achieves comparable performance to VVC but also offers a lightweight decoder with only 51.47 GMACs computation and 19.65M parameters. Furthermore, this design methodology can be easily applied to any LIC models, enabling the practical deployment of LIC techniques.