pwc-archive/papers-with-abstracts huggingface dataset:
Peer Effects and Herd Behavior: An Empirical Study Based on the "Double 11" Shopping Festival
Authors: Hambur Wang
Date: 2024-11-29 00:00:00
Tasks: Decision Making, Marketing

This study employs a Bayesian Probit model to empirically analyze peer effects and herd behavior among consumers during the "Double 11" shopping festival, using data collected through a questionnaire survey. The results demonstrate that peer effects significantly influence consumer decision-making, with the probability of participation in the shopping event increasing notably when roommates are involved. Additionally, factors such as gender, online shopping experience, and fashion consciousness significantly impact consumers' herd behavior. This research not only enhances the understanding of online shopping behavior among college students but also provides empirical evidence for e-commerce platforms to formulate targeted marketing strategies. Finally, the study discusses the fragility of online consumption activities, the need for adjustments in corporate marketing strategies, and the importance of promoting a healthy online culture.
Pollution and Mortality: Evidence from early 20th Century Sweden
Authors: Michael Haylock, Martin Karlsson, Maksym Obrizan
Date: 2024-12-02 00:00:00

Economic growth in Sweden during the early 20th Century was largely driven by industry. A significant contributor to this growth was the installation of different kinds of engines used to power factories. We use newly digitized data on engines and their energy source by industry sector, and combine this with municipality-level data of workers per industry sector to construct a new variable reflecting economic output using dirty engines. In turn, we assess the average externality of dirty output on mortality in the short-run, as defined by deaths over the population in the baseline year. Our results show substantial increases of up to 17% higher mortality in cities where large increases to dirty engine installations occurred, which is largely driven by the elderly. We also run a placebo test using clean powered industry and find no effect on mortality.
A Rule-Based Methodology for Company Identification: Application to the Downstream Space Sector
Authors: Kenza Bousedra, Pierre Pelletier
Date: 2024-12-03 00:00:00
Tasks: Articles, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

This paper proposes an original methodology based on Named Entity Recognition (NER) to identify companies involved in downstream space activities, i.e., companies that provide services or products exploiting data and technology from space. Using a rule-based approach, the method leverages a corpus of texts from digitized French press articles to extract company names related to the downstream space segment. This approach allowed the detection of 88 new downstream space companies, enriching the existing database of the sector by 33\%. The paper details the identification process and provides guidelines for future replications, applying the method to other geographic areas, or adapting it to other industries where new entrants are challenging to identify using traditional activity classifications.
Decoding Financial Behaviour: An Analysis of urbanised households in India using AIDIS 77th round
Authors: Divya Sharma
Date: 2024-12-02 00:00:00

This research paper delves into the financial behavior of urbanized households in India, specifically focusing on million-plus agglomerations. Using data from the 77th round of the All India Debt and Investment Survey, the study analyzes assets, borrowing patterns, and the usage of financial instruments like bank accounts, e-wallets, and life insurance. The research explores demographic factors, household structures, and district-level parameters to understand the intricate financial landscape. With a focus on low-income households, the study identifies income, education, type of employment, and branches per capita as significant factors influencing financial behavior. The study contributes valuable insights for financial institutions, policymakers, and researchers seeking a comprehensive understanding of the financial lives of urban households in India.
The Promise and Peril of Generative AI: Evidence from GPT-4 as Sell-Side Analysts
Authors: Edward Li, Zhiyuan Tu, Dexin Zhou
Date: 2024-12-02 00:00:00

We investigate how advanced large language models (LLMs), specifically GPT-4, process corporate disclosures to forecast earnings. Using earnings press releases issued around GPT-4's knowledge cutoff date, we address two questions: (1) Do GPT-generated earnings forecasts outperform analysts in accuracy? (2) How is GPT's performance related to its processing of textual and quantitative information? Our findings suggest that GPT forecasts are significantly less accurate than those of analysts. This underperformance can be traced to GPT's distinct textual and quantitative approaches: its textual processing follows a consistent, generalized pattern across firms, highlighting its strengths in language tasks. In contrast, its quantitative processing capabilities vary significantly across firms, revealing limitations tied to the uneven availability of domain-specific training data. Additionally, there is some evidence that GPT's forecast accuracy diminishes beyond its knowledge cutoff, underscoring the need to evaluate LLMs under hindsight-free conditions. Overall, this study provides a novel exploration of the "black box" of GPT-4's information processing, offering insights into LLMs' potential and challenges in financial applications.
Neural Network Approach to Demand Estimation and Dynamic Pricing in Retail
Authors: Kirill Safonov
Date: 2024-12-01 00:00:00

This paper contributes to the literature on parametric demand estimation by using deep learning to model consumer preferences. Traditional econometric methods often struggle with limited within-product price variation, a challenge addressed by the proposed neural network approach. The proposed method estimates the functional form of the demand and demonstrates higher performance in both simulations and empirical applications. Notably, under low price variation, the machine learning model outperforms econometric approaches, reducing the mean squared error of initial price parameter estimates by nearly threefold. In empirical setting, the ML model consistently predicts a negative relationship between demand and price in 100% of cases, whereas the econometric approach fails to do so in 20% of cases. The suggested model incorporates a wide range of product characteristics, as well as prices of other products and competitors.
Effects of time aggregation, product aggregation, and seasonality in measuring bullwhip ratio
Authors: Hau Mike Ma, Jiazhen Huo, Yongrui Duan
Date: 2024-12-01 00:00:00

The bullwhip study has received a lot of attention in the literature, but with conflicting results, especially in the context of data aggregation. In this paper, we investigate three widely studied factors in bullwhip measurement: time aggregation, product aggregation, and seasonality. In time aggregation, we decompose the variance into two components: the expectation of the subset variances and the variance of subset expectations, thus decomposing the bullwhip ratio into four components to explore the underlying mechanism of time aggregation. In product aggregation, the bullwhip ratio is analyzed in the context of products with either uncorrelated or correlated demands and orders. Seasonality is also examined to study its effect on the bullwhip ratio. Our key findings are: (a) Time aggregation can increase, decrease, or maintain the bullwhip ratio in different scenarios. (b) Aggregated bullwhip ratio of uncorrelated products is a weighted average of bullwhip ratios from individual products, with corresponding demand variance as the weights. However, aggregated bullwhip ratio of correlated products could break the boundaries. (c) Seasonality can be considered as a standalone product with a bullwhip ratio of one, which can drive the overall bullwhip ratio closer to one.
Macroeconomics of Racial Disparities: Discrimination, Labor Market, and Wealth
Authors: Guanyi Yang, Srinivasan Murali
Date: 2024-11-30 00:00:00

This paper examines the impact of racial discrimination in hiring on employment, wages, and wealth disparities between black and white workers. Using a labor search-and-matching model with racially prejudiced and non-prejudiced firms, we show that labor market frictions sustain discriminatory practices as an equilibrium outcome. These practices account for 57% of the racial unemployment gap, 48% of the average wage gap, and 16% of the median wealth gap. Discriminatory hiring also increases unemployment and wage volatility for black workers, increasing their labor market risks over the business cycle. Eliminating prejudiced firms reduces these disparities and improves the welfare of black workers as well as the overall economic welfare.
Capacity Constraints in Principal-Agent Problems
Authors: Aubrey Clark
Date: 2024-12-02 00:00:00

Adding a capacity constraint to a hidden-action principal-agent problem results in the same set of Pareto optimal contracts as the unconstrained problem where output is scaled down by a constant factor. This scaling factor is increasing in the agent's capacity to exert effort.
Tournaments with a Standard
Authors: Mikhail Drugov, Dmitry Ryvkin, Jun Zhang
Date: 2024-12-02 00:00:00

We study tournaments where winning a rank-dependent prize requires passing a minimum performance standard. We show that, for any prize allocation, the optimal standard is always at a mode of performance that is weakly higher than the global mode and identify a necessary and sufficient condition for it to be at the global mode. When the prize scheme can be designed as well, the winner-take-all prize scheme is optimal for noise distributions with an increasing failure rate; and awarding equal prizes to all qualifying agents is optimal for noise distributions with a decreasing failure rate. For distributions with monotone likelihood ratios -- log-concave and log-convex, respectively -- these pay schemes are also optimal in a larger class of anonymous, monotone contracts that may depend on cardinal performance.
Text-based Audio Retrieval by Learning from Similarities between Audio Captions
Authors: Huang Xie, Khazar Khorrami, Okko Räsänen, Tuomas Virtanen
Date: 2024-12-02 00:00:00
Tasks: Retrieval, Sentence

This paper proposes to use similarities of audio captions for estimating audio-caption relevances to be used for training text-based audio retrieval systems. Current audio-caption datasets (e.g., Clotho) contain audio samples paired with annotated captions, but lack relevance information about audio samples and captions beyond the annotated ones. Besides, mainstream approaches (e.g., CLAP) usually treat the annotated pairs as positives and consider all other audio-caption combinations as negatives, assuming a binary relevance between audio samples and captions. To infer the relevance between audio samples and arbitrary captions, we propose a method that computes non-binary audio-caption relevance scores based on the textual similarities of audio captions. We measure textual similarities of audio captions by calculating the cosine similarity of their Sentence-BERT embeddings and then transform these similarities into audio-caption relevance scores using a logistic function, thereby linking audio samples through their annotated captions to all other captions in the dataset. To integrate the computed relevances into training, we employ a listwise ranking objective, where relevance scores are converted into probabilities of ranking audio samples for a given textual query. We show the effectiveness of the proposed method by demonstrating improvements in text-based audio retrieval compared to methods that use binary audio-caption relevances for training.
AlignFormer: Modality Matching Can Achieve Better Zero-shot Instruction-Following Speech-LLM
Authors: Ruchao Fan, Bo Ren, Yuxuan Hu, Rui Zhao, Shujie Liu, Jinyu Li
Date: 2024-12-02 00:00:00
Tasks: Instruction Following, Question Answering

Integrating speech into LLM (speech-LLM) has gaining increased attention recently. The mainstream solution is to connect a well-trained speech encoder and LLM with a neural adapter. However, the length mismatch between the speech and text sequences are not well handled, leading to imperfect modality matching between the speech and text. In this work, we propose a novel neural adapter, AlignFormer, to reduce the length gap between the two modalities. AlignFormer consists of CTC and dynamic-window QFormer layers, where the CTC alignment provides the dynamic window information for qformer layers. The LLM backbone is frozen in training to preserve its text capability, especially the instruction following capability. When training with only the ASR data, the proposed AlignFormer unlocks the instruction following capability for speech-LLM and the model can perform zero-shot speech translation (ST) and speech question answering (SQA) tasks. In fact, speech-LLM with AlignFormer can theoretically perform any tasks that the LLM backbone can deal with in the speech version. To evaluate the effectiveness of the instruction-following speech-LLM, we propose to use instruction following rate (IFR) and offer a systematic perspective for the IFR evaluation. In addition, we find that the audio position in training would affect the instruction following capability of speech-LLM and conduct an in-depth study on it. Our findings show that audio-first training achieves higher IFR than instruction-first training. The AlignFormer can achieve a near 100% IFR with audio-first training and game-changing improvements from zero to non-zero IFR on some evaluation data with instruction-first training. We believe that this study is a big step towards the perfect speech and text modality matching in the LLM embedding space.
Detecting Spoof Voices in Asian Non-Native Speech: An Indonesian and Thai Case Study
Authors: Aulia Adila, Candy Olivia Mawalim, Masashi Unoki
Date: 2024-12-02 00:00:00

This study focuses on building effective spoofing countermeasures (CMs) for non-native speech, specifically targeting Indonesian and Thai speakers. We constructed a dataset comprising both native and non-native speech to facilitate our research. Three key features (MFCC, LFCC, and CQCC) were extracted from the speech data, and three classic machine learning-based classifiers (CatBoost, XGBoost, and GMM) were employed to develop robust spoofing detection systems using the native and combined (native and non-native) speech data. This resulted in two types of CMs: Native and Combined. The performance of these CMs was evaluated on both native and non-native speech datasets. Our findings reveal significant challenges faced by Native CM in handling non-native speech, highlighting the necessity for domain-specific solutions. The proposed method shows improved detection capabilities, demonstrating the importance of incorporating non-native speech data into the training process. This work lays the foundation for more effective spoofing detection systems in diverse linguistic contexts.
SSDM 2.0: Time-Accurate Speech Rich Transcription with Non-Fluencies
Authors: Jiachen Lian, Xuanru Zhou, Zoe Ezzes, Jet Vonk, Brittany Morin, David Baquirin, Zachary Mille, Maria Luisa Gorno Tempini, Gopala Krishna Anumanchipalli
Date: 2024-11-29 00:00:00
Tasks: In-Context Learning

Speech is a hierarchical collection of text, prosody, emotions, dysfluencies, etc. Automatic transcription of speech that goes beyond text (words) is an underexplored problem. We focus on transcribing speech along with non-fluencies (dysfluencies). The current state-of-the-art pipeline SSDM suffers from complex architecture design, training complexity, and significant shortcomings in the local sequence aligner, and it does not explore in-context learning capacity. In this work, we propose SSDM 2.0, which tackles those shortcomings via four main contributions: (1) We propose a novel \textit{neural articulatory flow} to derive highly scalable speech representations. (2) We developed a \textit{full-stack connectionist subsequence aligner} that captures all types of dysfluencies. (3) We introduced a mispronunciation prompt pipeline and consistency learning module into LLM to leverage dysfluency \textit{in-context pronunciation learning} abilities. (4) We curated Libri-Dys and open-sourced the current largest-scale co-dysfluency corpus, \textit{Libri-Co-Dys}, for future research endeavors. In clinical experiments on pathological speech transcription, we tested SSDM 2.0 using nfvPPA corpus primarily characterized by \textit{articulatory dysfluencies}. Overall, SSDM 2.0 outperforms SSDM and all other dysfluency transcription models by a large margin. See our project demo page at \url{https://berkeley-speech-group.github.io/SSDM2.0/}.
A Context-Based Numerical Format Prediction for a Text-To-Speech System
Authors: Yaser Darwesh, Lit Wei Wern, Mumtaz Begum Mustafa
Date: 2024-11-19 00:00:00
Tasks: text-to-speech, Text to Speech

Many of the existing TTS systems cannot accurately synthesize text containing a variety of numerical formats, resulting in reduced intelligibility of the synthesized speech. This research aims to develop a numerical format classifier that can classify six types of numeric contexts. Experiments were carried out using the proposed context-based feature extraction technique, which is focused on extracting keywords, punctuation marks, and symbols as the features of the numbers. Support Vector Machine, K-Nearest Neighbors Linear Discriminant Analysis, and Decision Tree were used as classifiers. We have used the 10-fold cross-validation technique to determine the classification accuracy in terms of recall and precision. It can be found that the proposed solution is better than the existing feature extraction technique with improvement to the classification accuracy by 30% to 37%. The use of the number format classification can increase the intelligibility of the TTS systems.
Multi-resolution Guided 3D GANs for Medical Image Translation
Authors: Juhyung Ha, Jong Sung Park, David Crandall, Eleftherios Garyfallidis, Xuhong Zhang
Date: 2024-11-30 00:00:00
Tasks: Generative Adversarial Network, Image Quality Assessment, Translation

Medical image translation is the process of converting from one imaging modality to another, in order to reduce the need for multiple image acquisitions from the same patient. This can enhance the efficiency of treatment by reducing the time, equipment, and labor needed. In this paper, we introduce a multi-resolution guided Generative Adversarial Network (GAN)-based framework for 3D medical image translation. Our framework uses a 3D multi-resolution Dense-Attention UNet (3D-mDAUNet) as the generator and a 3D multi-resolution UNet as the discriminator, optimized with a unique combination of loss functions including voxel-wise GAN loss and 2.5D perception loss. Our approach yields promising results in volumetric image quality assessment (IQA) across a variety of imaging modalities, body regions, and age groups, demonstrating its robustness. Furthermore, we propose a synthetic-to-real applicability assessment as an additional evaluation to assess the effectiveness of synthetic data in downstream applications such as segmentation. This comprehensive evaluation shows that our method produces synthetic medical images not only of high-quality but also potentially useful in clinical applications. Our code is available at github.com/juhha/3D-mADUNet.
Good, Cheap, and Fast: Overfitted Image Compression with Wasserstein Distortion
Authors: Jona Ballé, Luca Versari, Emilien Dupont, Hyunjik Kim, Matthias Bauer
Date: 2024-11-30 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Compression, MS-SSIM, SSIM

Inspired by the success of generative image models, recent work on learned image compression increasingly focuses on better probabilistic models of the natural image distribution, leading to excellent image quality. This, however, comes at the expense of a computational complexity that is several orders of magnitude higher than today's commercial codecs, and thus prohibitive for most practical applications. With this paper, we demonstrate that by focusing on modeling visual perception rather than the data distribution, we can achieve a very good trade-off between visual quality and bit rate similar to "generative" compression models such as HiFiC, while requiring less than 1% of the multiply-accumulate operations (MACs) for decompression. We do this by optimizing C3, an overfitted image codec, for Wasserstein Distortion (WD), and evaluating the image reconstructions with a human rater study, showing that WD clearly outperforms LPIPS as an optimization objective. The study also reveals that WD outperforms other perceptual metrics such as LPIPS, DISTS, and MS-SSIM as a predictor of human ratings, remarkably achieving over 94% Pearson correlation with Elo scores.
DeepFGS: Fine-Grained Scalable Coding for Learned Image Compression
Authors: Yongqi Zhai, Yi Ma, Luyang Tang, Wei Jiang, Ronggang Wang
Date: 2024-11-30 00:00:00
Tasks: Decoder, Image Compression, MS-SSIM, SSIM

Scalable coding, which can adapt to channel bandwidth variation, performs well in today's complex network environment. However, most existing scalable compression methods face two challenges: reduced compression performance and insufficient scalability. To overcome the above problems, this paper proposes a learned fine-grained scalable image compression framework, namely DeepFGS. Specifically, we introduce a feature separation backbone to divide the image information into basic and scalable features, then redistribute the features channel by channel through an information rearrangement strategy. In this way, we can generate a continuously scalable bitstream via one-pass encoding. For entropy coding, we design a mutual entropy model to fully explore the correlation between the basic and scalable features. In addition, we reuse the decoder to reduce the parameters and computational complexity. Experiments demonstrate that our proposed DeepFGS outperforms previous learning-based scalable image compression models and traditional scalable image codecs in both PSNR and MS-SSIM metrics.
Multi-scale Feature Enhancement in Multi-task Learning for Medical Image Analysis
Authors: Phuoc-Nguyen Bui, Duc-Tai Le, Junghyun Bum, Hyunseung Choo
Date: 2024-11-30 00:00:00
Tasks: Classification, Decoder, Medical Image Analysis, Multi-Task Learning, Segmentation

Traditional deep learning methods in medical imaging often focus solely on segmentation or classification, limiting their ability to leverage shared information. Multi-task learning (MTL) addresses this by combining both tasks through shared representations but often struggles to balance local spatial features for segmentation and global semantic features for classification, leading to suboptimal performance. In this paper, we propose a simple yet effective UNet-based MTL model, where features extracted by the encoder are used to predict classification labels, while the decoder produces the segmentation mask. The model introduces an advanced encoder incorporating a novel ResFormer block that integrates local context from convolutional feature extraction with long-range dependencies modeled by the Transformer. This design captures broader contextual relationships and fine-grained details, improving classification and segmentation accuracy. To enhance classification performance, multi-scale features from different encoder levels are combined to leverage the hierarchical representation of the input image. For segmentation, the features passed to the decoder via skip connections are refined using a novel dilated feature enhancement (DFE) module, which captures information at different scales through three parallel convolution branches with varying dilation rates. This allows the decoder to detect lesions of varying sizes with greater accuracy. Experimental results across multiple medical datasets confirm the superior performance of our model in both segmentation and classification tasks, compared to state-of-the-art single-task and multi-task learning methods.