pwc-archive/papers-with-abstracts huggingface dataset:
Understanding the Excess Bond Premium
Authors: Kevin Benson, Ing-Haw Cheng, John Hull, Charles Martineau, Yoshio Nozawa, Vasily Strela, Yuntao Wu, Jun Yuan
Date: 2024-12-05 00:00:00
Tasks: Attribute

We study the drivers of the Gilchrist and Zakraj\v{s}ek (2012) excess bond premium (EBP) through the lens of the news. The monthly attention the news pays to 180 topics (Bybee et al., 2024) captures up to 80% of the variation in the EBP, and this component of variation forecasts macroeconomic movements. Greater news attention to financial intermediaries and crises tends to drive up the EBP and portend macroeconomic downturns, while greater news attention to politics and science tends to drive down the EBP. Attention-based estimates of EBP largely drive out the forecast power of direct sentiment measures for macroeconomic fluctuations and predict the business cycle going back to the early 1900's. Overall, we attribute predictive variation about the EBP for macroeconomic movements to variation in news attention to financial intermediaries, crises, and politics.
Augmenting Minds or Automating Skills: The Differential Role of Human Capital in Generative AI's Impact on Creative Tasks
Authors: Meiling Huang, Ming Jin, Ning li
Date: 2024-12-05 00:00:00

Generative AI is rapidly reshaping creative work, raising critical questions about its beneficiaries and societal implications. This study challenges prevailing assumptions by exploring how generative AI interacts with diverse forms of human capital in creative tasks. Through two random controlled experiments in flash fiction writing and song composition, we uncover a paradox: while AI democratizes access to creative tools, it simultaneously amplifies cognitive inequalities. Our findings reveal that AI enhances general human capital (cognitive abilities and education) by facilitating adaptability and idea integration but diminishes the value of domain-specific expertise. We introduce a novel theoretical framework that merges human capital theory with the automation-augmentation perspective, offering a nuanced understanding of human-AI collaboration. This framework elucidates how AI shifts the locus of creative advantage from specialized expertise to broader cognitive adaptability. Contrary to the notion of AI as a universal equalizer, our work highlights its potential to exacerbate disparities in skill valuation, reshaping workplace hierarchies and redefining the nature of creativity in the AI era. These insights advance theories of human capital and automation while providing actionable guidance for organizations navigating AI integration amidst workforce inequalities.
E-Commerce in Africa: Divergent Impacts on Rural and Urban Economies
Authors: Jaelyn S. Liang, Rehaan S. Mundy, Shriya Jagwayan
Date: 2024-12-05 00:00:00

E-commerce is rapidly transforming economies across Africa, offering immense opportunities for economic growth, market expansion, and digital inclusion. This study investigates the effects of e-commerce on select African regions. By utilizing readiness factors, including mobile money deployment, GDP per capita, internet penetration, and digital infrastructure, the preparedness of African countries for e-commerce adoption is quantified, highlighting significant disparities. Through case studies in urban and rural areas, including Lagos, Kano, Nairobi, and the Rift Valley, the study shows e-commerce's significant effects on small and medium-sized enterprises (SMEs), employment, and market efficiency. Urban centers demonstrated significant gains in productivity and profitability, whereas rural regions experienced slower growth due to limited internet access and infrastructural barriers. Despite these challenges, localized solutions such as mobile money systems and agricultural e-commerce platforms are bridging gaps. This study highlights the significant potential of e-commerce in Africa while emphasizing the need for targeted investments and strategies to address existing regional disparities.
Economic Geography and Structural Change
Authors: Clement E. Bohr, Marti Mestieri, Frederic Robert-Nicoud
Date: 2024-12-04 00:00:00

As countries develop, the relative importance of agriculture declines and economic activity becomes spatially concentrated. We develop a model integrating structural change and regional disparities to jointly capture these phenomena. A key modeling innovation ensuring analytical tractability is the introduction of non-homothetic Cobb-Douglas preferences, which are characterized by constant unitary elasticity of substitution and non-constant income elasticity. As labor productivity increases over time, economic well-being rises, leading to a declining expenditure share on agricultural goods. Labor reallocates away from agriculture, and industry concentrates spatially, further increasing aggregate productivity: structural change and regional disparities are two mutually reinforcing outcomes and propagators of the growth process.
Never-ending Search for Innovation
Authors: Jean-Michel Benkert, Igor Letina
Date: 2024-12-04 00:00:00

We provide a model of investment in innovation that is dynamic, features multiple heterogeneous research projects of which only one potentially leads to success, and in each period, the researcher chooses the set of projects to invest in. We show that if a search for innovation starts, it optimally does not end until the innovation is found -- which will be never with a strictly positive probability.
Social media and suicide: empirical evidence from the quasi-exogenous geographical adoption of Twitter
Authors: Alexis Du, Thomas Renault
Date: 2024-12-04 00:00:00

Social media usage is often cited as a potential driver behind the rising suicide rates. However, distinguishing the causal effect - whether social media increases the risk of suicide - from reverse causality, where individuals already at higher risk of suicide are more likely to use social media, remains a significant challenge. In this paper, we use an instrumental variable approach to study the quasi-exogenous geographical adoption of Twitter and its causal relationship with suicide rates. Our analysis first demonstrates that Twitter's geographical adoption was driven by the presence of certain users at the 2007 SXSW festival, which led to long-term disparities in adoption rates across counties in the United States. Then, using a two-stage least squares (2SLS) regression and controlling for a wide range of geographic, socioeconomic and demographic factors, we find no significant relationship between Twitter adoption and suicide rates.
The Value of Solving Pains
Authors: Jürg Meierhofer, Nikola Pascher, Jochen Wulf
Date: 2024-12-04 00:00:00

We introduce a novel framework aimed at identifying and quantifying the value of customer pains as a critical element in service innovation. The proposed approach enhances existing end-to-end frameworks by offering a structured method to elaborate on and measure the value derived from solving these customer challenges. The effectiveness of the framework is validated by operationalizing it in an industrial case study, where the model parameters were captured specifically and the value of solving various operational and structural pains was evaluated numerically.
Unveiling Saving and Credit Dynamics: Insights from Financial Diaries and Surveys among Low-Income Households in Unauthorized Colonies in Delhi
Authors: Divya Sharma
Date: 2024-12-04 00:00:00

The paper presents findings from a comprehensive study examining the saving and credit behaviors of low-income households residing in unauthorized colonies within a metropolitan area. Utilizing a dual approach, the study engaged in prolonged fieldwork, including repeated fortnightly interviews with selected households and a one-time primary survey with a larger sample size. The research meticulously analyzed the financial lives of these households, focusing on their saving and credit behaviors and assessing the accessibility and intensity of usage of financial instruments available to them. Through suitable regression models, the study identified key factors influencing the usage of financial instruments among low-income households. Transaction costs, convenience, and financial knowledge emerged as significant determinants impacting both usage decisions and the intensity of usage. The research underscores the importance of addressing demand side factors to ensure widespread financial services usage among low-income groups. Efforts to reduce time costs, enhance product accessibility and liquidity, and augment financial literacy are essential for fostering financial inclusion in unauthorized colonies. The findings highlight the imperative of moving beyond mere financial access towards promoting universal usage to realize the full benefits of financial inclusion.
Spatial Econometric Analysis of Dana Point's Housing Market
Authors: Hannah Attar
Date: 2024-11-20 00:00:00

This paper investigates the determinants of home prices in Dana Point, California to analyze various factors influencing the real estate market. The results are based on a cross-sectional dataset that incorporates year and month-time dummies to account for temporal trends, as well as spatial variables that capture effects within and between clusters. To address endogeneity issues between square footage and price, parking is employed to instrument square footage and break the reverse causality link. The robustness of the instrument is confirmed through statistical tests, indicating a strong relationship with square footage. Additionally, this study employs the use of Probability models to test Tobit's robustness at estimating the dummy-transformed price variable. Spatial trends are analyzed through fixed effects, random effects, as well as Spatial Autoregressive models absorbing cluster factors, which highlight the differences in price dynamics across various clusters within Dana Point.
Categorize and randomize: a permissive model of stochastic choice
Authors: Ester Sudano
Date: 2024-12-04 00:00:00

We model stochastic choices with categorization. The agent preliminarly groups alternatives in homogenous disjoint classes, then randomly chooses one class and randomly picks an item within the selected class. We give a formal definition of a choice generated by this procedure, and provide an axiomatic characterization. The characterizing properties allow an external analyst to elicit that categorization is applied. In a broader interpretation, the model allows to describe the observed choice as the composition of independent subchoices. This composition preserves rationalizability by Random Utility Maximization. A generalization of the model subsumes Luce model and Nested Logit.
Integrated Minimum Mean Squared Error Algorithms for Combined Acoustic Echo Cancellation and Noise Reduction
Authors: Arnout Roebben, Toon van Waterschoot, Jan Wouters, Marc Moonen
Date: 2024-12-05 00:00:00
Tasks: Acoustic echo cancellation

In many speech recording applications, noise and acoustic echo corrupt the desired speech. Consequently, combined noise reduction (NR) and acoustic echo cancellation (AEC) is required. Generally, a cascade approach is followed, i.e., the AEC and NR are designed in isolation by selecting a separate signal model, formulating a separate cost function, and using a separate solution strategy. The AEC and NR are then cascaded one after the other, not accounting for their interaction. In this paper, however, an integrated approach is proposed to consider this interaction in a general multi-microphone/multi-loudspeaker setup. Therefore, a single signal model of either the microphone signal vector or the extended signal vector, obtained by stacking microphone and loudspeaker signals, is selected, a single mean squared error cost function is formulated, and a common solution strategy is used. Using this microphone signal model, a multi channel Wiener filter (MWF) is derived. Using the extended signal model, an extended MWF (MWFext) is derived, and several equivalent expressions are found, which nevertheless are interpretable as cascade algorithms. Specifically, the MWFext is shown to be equivalent to algorithms where the AEC precedes the NR (AEC NR), the NR precedes the AEC (NR-AEC), and the extended NR (NRext) precedes the AEC and post-filter (PF) (NRext-AECPF). Under rank-deficiency conditions the MWFext is non-unique, such that this equivalence amounts to the expressions being specific, not necessarily minimum-norm solutions for this MWFext. The practical performances nonetheless differ due to non-stationarities and imperfect correlation matrix estimation, resulting in the AEC-NR and NRext-AEC-PF attaining best overall performance.
Comprehensive Audio Query Handling System with Integrated Expert Models and Contextual Understanding
Authors: Vakada Naveen, Arvind Krishna Sridhar, Yinyi Guo, Erik Visser
Date: 2024-12-05 00:00:00
Tasks: Audio Generation, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Chatbot, Question Answering, speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition

This paper presents a comprehensive chatbot system designed to handle a wide range of audio-related queries by integrating multiple specialized audio processing models. The proposed system uses an intent classifier, trained on a diverse audio query dataset, to route queries about audio content to expert models such as Automatic Speech Recognition (ASR), Speaker Diarization, Music Identification, and Text-to-Audio generation. A 3.8 B LLM model then takes inputs from an Audio Context Detection (ACD) module extracting audio event information from the audio and post processes text domain outputs from the expert models to compute the final response to the user. We evaluated the system on custom audio tasks and MMAU sound set benchmarks. The custom datasets were motivated by target use cases not covered in industry benchmarks and included ACD-timestamp-QA (Question Answering) as well as ACD-temporal-QA datasets to evaluate timestamp and temporal reasoning questions, respectively. First we determined that a BERT based Intent Classifier outperforms LLM-fewshot intent classifier in routing queries. Experiments further show that our approach significantly improves accuracy on some custom tasks compared to state-of-the-art Large Audio Language Models and outperforms models in the 7B parameter size range on the sound testset of the MMAU benchmark, thereby offering an attractive option for on device deployment.
Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation
Authors: Jie Bao, Zhixin Zhou, Wen Jung Li, Rui Luo
Date: 2024-12-05 00:00:00
Tasks: Domain Generalization, Image Generation, Image Segmentation, Image Stylization, Lesion Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Skin Lesion Segmentation, Style Transfer

Accurate medical image segmentation is essential for effective diagnosis and treatment planning but is often challenged by domain shifts caused by variations in imaging devices, acquisition conditions, and patient-specific attributes. Traditional domain generalization methods typically require inclusion of parts of the test domain within the training set, which is not always feasible in clinical settings with limited diverse data. Additionally, although diffusion models have demonstrated strong capabilities in image generation and style transfer, they often fail to preserve the critical structural information necessary for precise medical analysis. To address these issues, we propose a novel medical image segmentation method that combines diffusion models and Structure-Preserving Network for structure-aware one-shot image stylization. Our approach effectively mitigates domain shifts by transforming images from various sources into a consistent style while maintaining the location, size, and shape of lesions. This ensures robust and accurate segmentation even when the target domain is absent from the training data. Experimental evaluations on colonoscopy polyp segmentation and skin lesion segmentation datasets show that our method enhances the robustness and accuracy of segmentation models, achieving superior performance metrics compared to baseline models without style transfer. This structure-aware stylization framework offers a practical solution for improving medical image segmentation across diverse domains, facilitating more reliable clinical diagnoses.
YOLO-CCA: A Context-Based Approach for Traffic Sign Detection
Authors: Linfeng Jiang, Peidong Zhan, Ting Bai, Haoyong Yu
Date: 2024-12-05 00:00:00
Tasks: Autonomous Driving, Traffic Sign Detection

Traffic sign detection is crucial for improving road safety and advancing autonomous driving technologies. Due to the complexity of driving environments, traffic sign detection frequently encounters a range of challenges, including low resolution, limited feature information, and small object sizes. These challenges significantly hinder the effective extraction of features from traffic signs, resulting in false positives and false negatives in object detection. To address these challenges, it is essential to explore more efficient and accurate approaches for traffic sign detection. This paper proposes a context-based algorithm for traffic sign detection, which utilizes YOLOv7 as the baseline model. Firstly, we propose an adaptive local context feature enhancement (LCFE) module using multi-scale dilation convolution to capture potential relationships between the object and surrounding areas. This module supplements the network with additional local context information. Secondly, we propose a global context feature collection (GCFC) module to extract key location features from the entire image scene as global context information. Finally, we build a Transformer-based context collection augmentation (CCA) module to process the collected local context and global context, which achieves superior multi-level feature fusion results for YOLOv7 without bringing in additional complexity. Extensive experimental studies performed on the Tsinghua-Tencent 100K dataset show that the mAP of our method is 92.1\%. Compared with YOLOv7, our approach improves 3.9\% in mAP, while the amount of parameters is reduced by 2.7M. On the CCTSDB2021 dataset the mAP is improved by 0.9\%. These results show that our approach achieves higher detection accuracy with fewer parameters. The source code is available at \url{https://github.com/zippiest/yolo-cca}.
Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects
Authors: Yixin Zhang, Nicholas Konz, Kevin Kramer, Maciej A. Mazurowski
Date: 2024-12-05 00:00:00
Tasks: Image Segmentation, Interactive Segmentation, Segmentation, Semantic Segmentation, Zero Shot Segmentation

Image segmentation foundation models (SFMs) like Segment Anything Model (SAM) have achieved impressive zero-shot and interactive segmentation across diverse domains. However, they struggle to segment objects with certain structures, particularly those with dense, tree-like morphology and low textural contrast from their surroundings. These failure modes are crucial for understanding the limitations of SFMs in real-world applications. To systematically study this issue, we introduce interpretable metrics quantifying object tree-likeness and textural separability. On carefully controlled synthetic experiments and real-world datasets, we show that SFM performance (e.g., SAM, SAM 2, HQ-SAM) noticeably correlates with these factors. We link these failures to "textural confusion", where models misinterpret local structure as global texture, causing over-segmentation or difficulty distinguishing objects from similar backgrounds. Notably, targeted fine-tuning fails to resolve this issue, indicating a fundamental limitation. Our study provides the first quantitative framework for modeling the behavior of SFMs on challenging structures, offering interpretable insights into their segmentation capabilities.
Hipandas: Hyperspectral Image Joint Denoising and Super-Resolution by Image Fusion with the Panchromatic Image
Authors: Shuang Xu, Zixiang Zhao, Haowen Bai, Chang Yu, Jiangjun Peng, Xiangyong Cao, Deyu Meng
Date: 2024-12-05 00:00:00
Tasks: Denoising, Super-Resolution

Hyperspectral images (HSIs) are frequently noisy and of low resolution due to the constraints of imaging devices. Recently launched satellites can concurrently acquire HSIs and panchromatic (PAN) images, enabling the restoration of HSIs to generate clean and high-resolution imagery through fusing PAN images for denoising and super-resolution. However, previous studies treated these two tasks as independent processes, resulting in accumulated errors. This paper introduces \textbf{H}yperspectral \textbf{I}mage Joint \textbf{Pand}enoising \textbf{a}nd Pan\textbf{s}harpening (Hipandas), a novel learning paradigm that reconstructs HRHS images from noisy low-resolution HSIs (LRHS) and high-resolution PAN images. The proposed zero-shot Hipandas framework consists of a guided denoising network, a guided super-resolution network, and a PAN reconstruction network, utilizing an HSI low-rank prior and a newly introduced detail-oriented low-rank prior. The interconnection of these networks complicates the training process, necessitating a two-stage training strategy to ensure effective training. Experimental results on both simulated and real-world datasets indicate that the proposed method surpasses state-of-the-art algorithms, yielding more accurate and visually pleasing HRHS images.
Understanding the Impact of Evaluation Metrics in Kinetic Models for Consensus-based Segmentation
Authors: Raffaella Fiamma Cabini, Horacio Tettamanti, Mattia Zanella
Date: 2024-12-04 00:00:00
Tasks: Segmentation

In this article we extend a recently introduced kinetic model for consensus-based segmentation of images. In particular, we will interpret the set of pixels of a 2D image as an interacting particle system which evolves in time in view of a consensus-type process obtained by interactions between pixels and external noise. Thanks to a kinetic formulation of the introduced model we derive the large time solution of the model. We will show that the choice of parameters defining the segmentation task can be chosen from a plurality of loss functions characterising the evaluation metrics.