pwc-archive/papers-with-abstracts huggingface dataset:
Comparative Analysis of Remittance Inflows- International Reserves-External Debt Dyad: Exploring Bangladesh's Economic Resilience in Avoiding Sovereign Default Compared to Sri Lanka
Authors: Nusrat Nawshin, Asif Imtiaz, Md. Shamsuddin Sarker
Date: 2024-10-12 00:00:00

External debt has been identified as the most liable to cause financial crises in developing countries in Asia and Latin America. One recent example of near bankruptcy in Sri Lanka has raised serious concerns among economists about how to anticipate and tackle external debt-related problems. Bangladesh also faced a decline in export income and a sharp rise in import prices amid the aforementioned global shocks. Nevertheless, the international reserves of Bangladesh have never fallen to the level they did in Sri Lanka. This paper examines the relationship between remittance inflows, international reserves, and external debt in Bangladesh and Sri Lanka. Econometric estimations reveal that remittance affects external debt both directly and through international reserves in Bangladesh. The existence of a Dutch Disease effect in the remittance inflows-international reserves relationship has also been confirmed in Bangladesh. We also show that Bangladesh uses international reserves as collateral to obtain more external borrowing, while Sri Lanka, like many other developing countries, accumulates international reserves to deplete in "bad times." Remittances can be seen as one of the significant factors preventing Bangladesh from becoming a sovereign defaulter, whereas Sri Lanka faced that fate.
Parallel Execution Fee Mechanisms
Authors: Abdoulaye Ndiaye
Date: 2024-10-12 00:00:00

This paper investigates how pricing schemes can achieve efficient allocations in blockchain systems featuring multiple transaction queues under a global capacity constraint. I model a capacity-constrained blockchain where users submit transactions to different queues -- each representing a submarket with unique demand characteristics -- and decide to participate based on posted prices and expected delays. I find that revenue maximization tends to allocate capacity to the highest-paying queue, whereas welfare maximization generally serves all queues. Optimal relative pricing of different queues depends on factors such as market size, demand elasticity, and the balance between local and global congestion. My results have implications for the implementation of local congestion pricing for evolving blockchain architectures, including parallel transaction execution, directed acyclic graph (DAG)-based systems, and multiple concurrent proposers.
Underutilized land and sustainable development: effects on employment, economic output, and mitigation of CO2 emissions
Authors: Seymur Garibov, Wadim Strielkowski
Date: 2024-10-11 00:00:00
Tasks: Retrieval, Retrieval-augmented Generation, Time Series

Climate change, deforestation, and biodiversity loss are calling for innovative approaches to effective reforestation and afforestation. This paper explores the integration of artificial intelligence and remote sensing technologies for optimizing tree planting strategies, estimating labor requirements, and determining space needs for various tree species in Gabala District of Azerbaijan. The study employs YOLOv8 for precise identification of potential planting sites and a Retrieval-Augmented Generation approach, combined with the Gemini API, to provide tailored species recommendations. The methodology incorporates time-series modeling to forecast the impact of reforestation on CO2 emissions reduction, utilizing Holt-Winters for predictions. Our results indicate that the AI model can effectively identify suitable locations and species, offering valuable insights into the potential economic and environmental benefits of large-scale tree planting thus fostering sustainable economic development and helping to mitigate the adverse effects of global warming and climate change.
Econometrics of Insurance with Multidimensional Types
Authors: Gaurab Aryal, Isabelle Perrigne, Quang Vuong, Haiqing Xu
Date: 2024-10-10 00:00:00
Tasks: Econometrics

In this paper, we address the identification and estimation of insurance models where insurees have private information about their risk and risk aversion. The model includes random damages and allows for several claims, while insurers choose from a finite number of coverages. We show that the joint distribution of risk and risk aversion is nonparametrically identified despite bunching due to multidimensional types and a finite number of coverages. Our identification strategy exploits the observed number of claims as well as an exclusion restriction, and a full support assumption. Furthermore, our results apply to any form of competition. We propose a novel estimation procedure combining nonparametric estimators and GMM estimation that we illustrate in a Monte Carlo study.
Structural Change, Employment, and Inequality in Europe: an Economic Complexity Approach
Authors: Bernardo Caldarola, Dario Mazzilli, Aurelio Patelli, Angelica Sbardella
Date: 2024-10-10 00:00:00

Structural change consists of industrial diversification towards more productive, knowledge intensive activities. However, changes in the productive structure bear inherent links with job creation and income distribution. In this paper, we investigate the consequences of structural change, defined in terms of labour shifts towards more complex industries, on employment growth, wage inequality, and functional distribution of income. The analysis is conducted for European countries using data on disaggregated industrial employment shares over the period 2010-2018. First, we identify patterns of industrial specialisation by validating a country-industry industrial employment matrix using a bipartite weighted configuration model (BiWCM). Secondly, we introduce a country-level measure of labour-weighted Fitness, which can be decomposed in such a way as to isolate a component that identifies the movement of labour towards more complex industries, which we define as structural change. Thirdly, we link structural change to i) employment growth, ii) wage inequality, and iii) labour share of the economy. The results indicate that our structural change measure is associated negatively with employment growth. However, it is also associated with lower income inequality. As countries move to more complex industries, they drop the least complex ones, so the (low-paid) jobs in the least complex sectors disappear. Finally, structural change predicts a higher labour ratio of the economy; however, this is likely to be due to the increase in salaries rather than by job creation.
Blockchain-Based Ad Auctions and Bayesian Persuasion: An Analysis of Advertiser Behavior
Authors: Xinyu Li
Date: 2024-10-09 00:00:00

This paper explores how ad platforms can utilize Bayesian persuasion within blockchain-based auction systems to strategically influence advertiser behavior despite increased transparency. By integrating game-theoretic models with machine learning techniques and the principles of blockchain technology, we analyze the role of strategic information disclosure in ad auctions. Our findings demonstrate that even in environments with inherent transparency, ad platforms can design signals to affect advertisers' beliefs and bidding strategies. A detailed case study illustrates how machine learning can predict advertiser responses to different signals, leading to optimized signaling strategies that increase expected revenue. The study contributes to the literature by extending Bayesian persuasion models to transparent systems and providing practical insights for auction design in the digital advertising industry.
Post-Covid learning assessment of school children: A Project by CRY & RILM across four states
Authors: Anushka De
Date: 2024-10-03 00:00:00

The COVID-19 pandemic struck education system around the globe and initiated an immediate and complete lockdown of all the educational institutions, to maintain social distancing. CRY (Child Rights and You) in collaboration with RILM (Rotary India Literacy Mission) initiated a project to assess the learning abilities of 4000 children across four states: Jammu & Kashmir, Jharkhand, Manipur and West Bengal. Every child was provided with the books of appropriate class according to their age in order to test their competency in reading and basic calculation as well and thereafter the compatible class was determined. The assessments were carried over 3 quarters in 4 subjects: oral assessments in 1st Language, 2nd Language and Mathematics and a writing assessment and a binary variable for improvement or no improvement (1/0) was provided. This paper suggests a measure which gives a unique score for improvement level of students with varied class lag since it will not be a desirable idea to grade the students with varied class lags on the same basis. This paper also investigates and evaluates the progression of student performance over the 3 quarters, suggests the use of a comprehensive score measure for summarising the inter-quarter performance. The analysis of progression has been carried out by gender and state level for male-female and inter state comparison respectively.
On the Oscillations in Cournot Games with Best Response Strategies
Authors: Zhengyang Liu, Haolin Lu, Liang Shan, Zihe Wang
Date: 2024-10-12 00:00:00

In this paper, we consider the dynamic oscillation in the Cournot oligopoly model, which involves multiple firms producing homogeneous products. To explore the oscillation under the updates of best response strategies, we focus on the linear price functions. In this setting, we establish the existence of oscillations. In particular, we show that for the scenario of different costs among firms, the best response converges to either a unique equilibrium or a two-period oscillation. We further characterize the oscillations and propose linear-time algorithms for finding all types of two-period oscillations. To the best of our knowledge, our work is the first step toward fully analyzing the periodic oscillation in the Cournot oligopoly model.
Term structure shapes and their consistent dynamics in the Svensson family
Authors: Martin Keller-Ressel, Felix Sachse
Date: 2024-10-11 00:00:00

We examine the shapes attainable by the forward- and yield-curve in the widely-used Svensson family, including the Nelson-Siegel and Bliss subfamilies. We provide a complete classification of all attainable shapes and partition the parameter space of each family according to these shapes. Building upon these results, we then examine the consistent dynamic evolution of the Svensson family under absence of arbitrage. Our analysis shows that consistent dynamics further restrict the set of attainable shapes, and we demonstrate that certain complex shapes can no longer appear after a deterministic time horizon. Moreover a single shape (either inverse of normal curves) must dominate in the long-run.
AI-Enhanced Acoustic Analysis for Comprehensive Biodiversity Monitoring and Assessment
Authors: Kumar Srinivas Bobba, Kartheeban K, Vamsi Krishna Sai, Dinesh Bugga, Vijaya Mani Surendra Bolla
Date: 2024-10-16 00:00:00

This project proposes the development of a comprehensive real-time biodiversity monitoring system that harnesses sound data through a network of acoustic sensors and advanced artificial intelligence algorithms. The system analyzes sound recordings from various ecosystems to identify and classify different species, providing valuable insights into ecosystem health and biodiversity patterns while facilitating the detection of subtle changes in species presence and behavior over time. By addressing critical challenges such as noise pollution and species overlap, the system employs sophisticated filtering and classification techniques to ensure accurate and reliable monitoring, distinguishing between natural sounds and anthropogenic noise. Ultimately, this initiative aims to enhance our understanding of biodiversity dynamics and provide essential information to support effective conservation strategies and inform policy decisions, empowering stakeholders with actionable insights to protect and preserve vital ecosystems.
Beyond Speech and More: Investigating the Emergent Ability of Speech Foundation Models for Classifying Physiological Time-Series Signals
Authors: Orchid Chetia Phukan, Swarup Ranjan Behera, Girish, Mohd Mujtaba Akhtar, Arun Balaji Buduru, Rajesh Sharma
Date: 2024-10-16 00:00:00
Tasks: Audio Classification, Time Series

Despite being trained exclusively on speech data, speech foundation models (SFMs) like Whisper have shown impressive performance in non-speech tasks such as audio classification. This is partly because speech shares some common traits with audio, enabling SFMs to transfer effectively. In this study, we push the boundaries by evaluating SFMs on a more challenging out-of-domain (OOD) task: classifying physiological time-series signals. We test two key hypotheses: first, that SFMs can generalize to physiological signals by capturing shared temporal patterns; second, that multilingual SFMs will outperform others due to their exposure to greater variability during pre-training, leading to more robust, generalized representations. Our experiments, conducted for stress recognition using ECG (Electrocardiogram), EMG (Electromyography), and EDA (Electrodermal Activity) signals, reveal that models trained on SFM-derived representations outperform those trained on raw physiological signals. Among all models, multilingual SFMs achieve the highest accuracy, supporting our hypothesis and demonstrating their OOD capabilities. This work positions SFMs as promising tools for new uncharted domains beyond speech.
ERVQ: Enhanced Residual Vector Quantization with Intra-and-Inter-Codebook Optimization for Neural Audio Codecs
Authors: Rui-Chen Zheng, Hui-Peng Du, Xiao-Hang Jiang, Yang Ai, Zhen-Hua Ling
Date: 2024-10-16 00:00:00
Tasks: Diversity, Online Clustering, Quantization, text-to-speech, Text to Speech

Current neural audio codecs typically use residual vector quantization (RVQ) to discretize speech signals. However, they often experience codebook collapse, which reduces the effective codebook size and leads to suboptimal performance. To address this problem, we introduce ERVQ, Enhanced Residual Vector Quantization, a novel enhancement strategy for the RVQ framework in neural audio codecs. ERVQ mitigates codebook collapse and boosts codec performance through both intra- and inter-codebook optimization. Intra-codebook optimization incorporates an online clustering strategy and a code balancing loss to ensure balanced and efficient codebook utilization. Inter-codebook optimization improves the diversity of quantized features by minimizing the similarity between successive quantizations. Our experiments show that ERVQ significantly enhances audio codec performance across different models, sampling rates, and bitrates, achieving superior quality and generalization capabilities. It also achieves 100% codebook utilization on one of the most advanced neural audio codecs. Further experiments indicate that audio codecs improved by the ERVQ strategy can improve unified speech-and-text large language models (LLMs). Specifically, there is a notable improvement in the naturalness of generated speech in downstream zero-shot text-to-speech tasks. Audio samples are available here.
Low Bitrate High-Quality RVQGAN-based Discrete Speech Tokenizer
Authors: Slava Shechtman, Avihu Dekel
Date: 2024-10-10 00:00:00

Discrete Audio codecs (or audio tokenizers) have recently regained interest due to the ability of Large Language Models (LLMs) to learn their compressed acoustic representations. Various publicly available trainable discrete tokenizers recently demonstrated impressive results for audio tokenization, yet they mostly require high token rates to gain high-quality reconstruction. In this study, we fine-tuned an open-source general audio RVQGAN model using diverse open-source speech data, considering various recording conditions and quality levels. The resulting wideband (24kHz) speech-only model achieves speech reconstruction, which is nearly indistinguishable from PCM (pulse-code modulation) with a rate of 150-300 tokens per second (1500-3000 bps). The evaluation used comprehensive English speech data encompassing different recording conditions, including studio settings. Speech samples are made publicly available in http://ibm.biz/IS24SpeechRVQ . The model is officially released in https://huggingface.co/ibm/DAC.speech.v1.0
Robust Fixed-Filter Sound Zone Control with Audio-Based Position Tracking
Authors: Sankha Subhra Bhattacharjee, Andreas Jonas Fuglsig, Flemming Christensen, Jesper Rindom Jensen, Mads Græsbøll Christensen
Date: 2024-10-10 00:00:00
Tasks: Position

Performance of sound zone control (SZC) systems deployed in practical scenarios are highly sensitive to the location of the listener(s) and can degrade significantly when listener(s) are moving. This paper presents a robust SZC system that adapts to dynamic changes such as moving listeners and varying zone locations using a dictionary-based approach. The proposed system continuously monitors the environment and updates the fixed control filters by tracking the listener position using audio signals only. To test the effectiveness of the proposed SZC method, simulation studies are carried out using practically measured impulse responses. These studies show that SZC, when incorporated with the proposed audio-only position tracking scheme, achieves optimal performance when all listener positions are available in the dictionary. Moreover, even when not all listener positions are included in the dictionary, the method still provides good performance improvement compared to a traditional fixed filter SZC scheme.
Automatic Screening for Children with Speech Disorder using Automatic Speech Recognition: Opportunities and Challenges
Authors: Dancheng Liu, Jason Yang, Ishan Albrecht-Buehler, Helen Qin, Sophie Li, Yuting Hu, Amir Nassereldine, JinJun Xiong
Date: 2024-10-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Position, speech-recognition, Speech Recognition

Speech is a fundamental aspect of human life, crucial not only for communication but also for cognitive, social, and academic development. Children with speech disorders (SD) face significant challenges that, if unaddressed, can result in lasting negative impacts. Traditionally, speech and language assessments (SLA) have been conducted by skilled speech-language pathologists (SLPs), but there is a growing need for efficient and scalable SLA methods powered by artificial intelligence. This position paper presents a survey of existing techniques suitable for automating SLA pipelines, with an emphasis on adapting automatic speech recognition (ASR) models for children's speech, an overview of current SLAs and their automated counterparts to demonstrate the feasibility of AI-enhanced SLA pipelines, and a discussion of practical considerations, including accessibility and privacy concerns, associated with the deployment of AI-powered SLAs.
Conditioning 3D Diffusion Models with 2D Images: Towards Standardized OCT Volumes through En Face-Informed Super-Resolution
Authors: Coen de Vente, Mohammad Mohaiminul Islam, Philippe Valmaggia, Carel Hoyng, Adnan Tufail, Clara I. Sánchez
Date: 2024-10-13 00:00:00
Tasks: Super-Resolution

High anisotropy in volumetric medical images can lead to the inconsistent quantification of anatomical and pathological structures. Particularly in optical coherence tomography (OCT), slice spacing can substantially vary across and within datasets, studies, and clinical practices. We propose to standardize OCT volumes to less anisotropic volumes by conditioning 3D diffusion models with en face scanning laser ophthalmoscopy (SLO) imaging data, a 2D modality already commonly available in clinical practice. We trained and evaluated on data from the multicenter and multimodal MACUSTAR study. While upsampling the number of slices by a factor of 8, our method outperforms tricubic interpolation and diffusion models without en face conditioning in terms of perceptual similarity metrics. Qualitative results demonstrate improved coherence and structural similarity. Our approach allows for better informed generative decisions, potentially reducing hallucinations. We hope this work will provide the next step towards standardized high-quality volumetric imaging, enabling more consistent quantifications.
ECVC: Exploiting Non-Local Correlations in Multiple Frames for Contextual Video Compression
Authors: Wei Jiang, Junru Li, Kai Zhang, Li Zhang
Date: 2024-10-13 00:00:00
Proceeding: CVPR 2025 1
Tasks: Video Compression

In Learned Video Compression (LVC), improving inter prediction, such as enhancing temporal context mining and mitigating accumulated errors, is crucial for boosting rate-distortion performance. Existing LVCs mainly focus on mining the temporal movements while neglecting non-local correlations among frames. Additionally, current contextual video compression models use a single reference frame, which is insufficient for handling complex movements. To address these issues, we propose leveraging non-local correlations across multiple frames to enhance temporal priors, significantly boosting rate-distortion performance. To mitigate error accumulation, we introduce a partial cascaded fine-tuning strategy that supports fine-tuning on full-length sequences with constrained computational resources. This method reduces the train-test mismatch in sequence lengths and significantly decreases accumulated errors. Based on the proposed techniques, we present a video compression scheme ECVC. Experiments demonstrate that our ECVC achieves state-of-the-art performance, reducing 10.5% and 11.5% more bit-rates than previous SOTA method DCVC-FM over VTM-13.2 low delay B (LDB) under the intra period (IP) of 32 and -1, respectively. Our code is available at https://github.com/JiangWeibeta/ECVC.
Analysing Osteoporosis Detection: A Comparative Study of CNN and FNN
Authors: R. Geetha, S. Arulselvi, R. Tamilselvi, M. Parisa Beham, Alavikunhu Panthakkan, Wathiq Mansoor, Hussain Al Ahmad
Date: 2024-10-11 00:00:00
Tasks: Diagnostic

Osteoporosis causes progressive loss of bone density and strength, causing a more elevated risk of fracture than in normal healthy bones. It is estimated that some 1 in 3 women and 1 in 5 men over the age of 50 will experience osteoporotic fractures, which poses osteoporosis as an important public health problem worldwide. The basis of diagnosis is based on Bone Mineral Density (BMD) tests, with Dual-energy X-ray Absorptiometry (DEXA) being the most common. A T-score of -2.5 or lower defines osteoporosis. This paper focuses on the application of medical imaging analytics towards the detection of osteoporosis by conducting a comparative study of the efficiency of CNN and FNN in DEXA image analytics. Both models are very promising, although, at 95%, the FNN marginally outperformed the CNN at 93%. Hence, this research underlines the probable capability of deep learning techniques in improving the detection of osteoporosis and optimizing diagnostic tools in order to achieve better patient outcomes.