pwc-archive/papers-with-abstracts huggingface dataset:
Scale Economies and Aggregate Productivity
Authors: Joel Kariel, Anthony Savagar
Date: 2024-11-27 00:00:00

We develop a theoretical framework to investigate the link between rising scale economies and stagnating productivity. Our model features heterogeneous firms, imperfect competition, and firm selection. We demonstrate that scale economies generated by fixed costs have distinct impacts on aggregate productivity compared to those driven by returns to scale. Using UK data, we estimate long-run increases in both fixed costs and returns to scale. Our model implies that this should increase aggregate productivity through improved firm selection and resource allocation. However, increasing markups can offset the productivity gain. Higher markups cushion low-productivity firms' revenues, allowing them to survive, and constrain firm output, which limits exploitation of scale economies.
Belief patterns with information processing
Authors: Federico Vaccari
Date: 2024-11-26 00:00:00
Tasks: Bayesian Inference, Decision Making

This paper presents a model of costly information acquisition where decision-makers can choose whether to elaborate information superficially or precisely. The former action is costless, while the latter entails a processing cost. Within this framework, decision-makers' beliefs may polarize even after they have access to the same evidence. From the perspective of a Bayesian observer who neglects information processing constraints, the decision-makers' optimal behavior and belief updating may appear consistent with biases such as disconfirmation, underreaction to information, and confirmation bias. However, these phenomena emerge naturally within the model and are fully compatible with standard Bayesian inference and rational decision-making when accounting for the costs of information acquisition.
Causal Inference in Finance: An Expertise-Driven Model for Instrument Variables Identification and Interpretation
Authors: Ying Chen, Ziwei Xu, Kotaro Inoue, Ryutaro Ichise
Date: 2024-11-26 00:00:00
Tasks: Causal Inference, counterfactual, feature selection, Knowledge Graphs

Instrumental Variable (IV) provides a source of treatment randomization that is conditionally independent of the outcomes, responding to the challenges of counterfactual and confounding biases. In finance, IV construction typically relies on pre-designed synthetic IVs, with effectiveness measured by specific algorithms. This classic paradigm cannot be generalized to address broader issues that require more and specific IVs. Therefore, we propose an expertise-driven model (ETE-FinCa) to optimize the source of expertise, instantiate IVs by the expertise concept, and interpret the cause-effect relationship by integrating concept with real economic data. The results show that the feature selection based on causal knowledge graphs improves the classification performance than others, with up to a 11.7% increase in accuracy and a 23.0% increase in F1-score. Furthermore, the high-quality IVs we defined can identify causal relationships between the treatment and outcome variables in the Two-Stage Least Squares Regression model with statistical significance.
Behavioral Expectations in New Keynesian DSGE Models: Evidence from India's COVID-19 Recovery and Vaccination Program
Authors: Arpan Chakraborty, Siddhartha Chattopadhyay
Date: 2024-11-26 00:00:00

This paper extends the New Keynesian Dynamic Stochastic General Equilibrium (DSGE) framework by incorporating behavioral expectations to analyze the moments of India's output gap and inflation rate, with a particular focus on the impacts of COVID-19 and vaccination programs. While DSGE models traditionally rely on rational expectations, we demonstrate that behavioral expectations more accurately capture the distributional characteristics of India's output gap and inflation rates data. Utilizing both Hodrick-Prescott and Kalman filters, we estimate the output gap and establish congruence with the moments of the simulated output gap. Concurrently, employing the initial negative demand shock values of the output gap, we calibrate the persistence parameters of negative aggregate demand (AD) and positive aggregate supply (AS) shocks, alongside the initial magnitude of the positive supply shock to achieve correspondence with post-COVID actual average values of the output gap and inflation rate. To ensure model precision, we implement Mahalanobis distance minimization for model calibration. Our findings elucidate that vaccination programs generated significant positive supply shocks, counterbalancing the prolonged negative demand shock precipitated by the COVID-19 pandemic. Moreover, the analysis reveals that the positive supply shocks' persistence parameter exceeds that of the negative demand shocks, indicating the exceptional efficacy of India's vaccination strategy. Furthermore, this research advances the scholarly contributions of Dasgupta and Rajeev (2023) by furnishing a quantitative DSGE framework that complements their static simple Keynesian analysis.
The Economics of Climate Adaptation: An Assessment
Authors: Anna Josephson, Rodrigo Guerra Su, Greg Collins, Katharine Jacobs
Date: 2024-11-25 00:00:00

The cost of the impacts of climate change have already proven to be larger than previously believed. Understanding the costs and benefits of adapting to the changing climate is necessary to make targeted and appropriate investment decisions. In this paper, we use a narrative review to synthesize the current literature on the economic case for climate adaptation, with the objective of assessing the value (economic and otherwise) of climate change adaptation, as well as the strength of the methods and evidence that have been used to date. We find that skepticism is warranted about many of the estimates about costs and benefits of climate adaptation and their underlying assumptions, due to a range of complexities associated with (1) uncertainty in distinguishing the economic impacts of climate change from seasonal variability; (2) difficulties in non-market valuation; (3) lack of consistent data collection over time at multiple scales; and (4) distributional inequities in access to proactive adaptation and recovery funding. While useful for broad stroke advocacy purposes, these estimates fall short of the refinement and rigor needed to inform investment decision-making, particularly at micro and local scales. Most estimates rely on cost benefit analysis and do not effectively address these issues. An emergent and promising literature tackles alternative estimation strategies and attempts to address some of them, including the complexities of uncertainty and non-market valuation.
The Impact of Banking Competition on Interest Rates for Household Consumption Loans in the Euro Area
Authors: Alexander Rom
Date: 2024-11-20 00:00:00

This paper investigates the impact of banking competition on interest rates for household consumption loans in the Euro Area from 2014 to 2020. Utilizing a panel data regression approach, we analyze how various factors, including local banking competition, influence the interest rates set by banks across 13 Euro-area countries. Our key independent variable, local banking competition, is measured by the number of commercial bank branches per 100,000 adults. Control variables include the ECB interest rate, euro exchange rate, real GDP growth rate, inflation rate, unemployment rate, bank business volumes, and country risk. We address potential endogeneity and heterogeneity biases and employ both Fixed Effects and Hausman-Taylor models to ensure robust results. Our findings indicate that higher local banking competition is associated with a slight increase in interest rates for household loans. Additionally, factors such as ECB interest rate, country risk, and euro appreciation significantly affect interest rates. The results offer insights into how competitive dynamics in the banking sector influence borrowing costs for households, providing valuable implications for policymakers and financial institutions in the Euro Area.
Household Resource Allocation Dynamics and Policies: Integrating Future Earnings of Children, Fertility, Pension, Health, and Education
Authors: Sushmita Kumari, Siddharth Gavhale
Date: 2024-11-27 00:00:00

This study presents a model that examines how families make decisions about having children, managing resources, and planning for their financial security in light of social and economic factors. It explores the balance between the number of children and the quality of life parents wish to provide, including education and future income prospects. By considering parents' expectations about their children's future earnings, the model gives new insights into how families allocate their resources. It also looks at how decisions about savings and pensions are connected to daily spending and family planning, showing the complex links between these factors. The findings offer valuable guidance for policies that address these interrelated challenges and better support families in managing their resources.
Remote Surgery with 5G or 6G: Knowledge Production and Diffusion Globally and in the German Case
Authors: Marina Martinelli, André Tosi Furtado
Date: 2024-11-26 00:00:00

This paper is a comprehensive exploring of technology capability in 5G/6G TIS, explicitly focusing on the potential of remote surgery globally and in Germany. The paper's main contribution is its ability to anticipate new debates on the interplay between TIS and contexts, with particular emphasis on the national and international levels. Our findings, derived from a Bibliometrics study of industry-academic relationships, highlight crucial collaborations in Germany, positioning the country as a strategic actor in international TIS and, by extension, in applying 5G/6G technological systems to remote surgery due to its knowledge production capability. We propose policies that can stimulate interaction between smaller suppliers and larger companies, which can act as intermediaries and provide access to international markets.
Independence and indifferent points imply continuity
Authors: Gerrit Bauch
Date: 2024-11-26 00:00:00

The expected utility theorem of von Neumann and Morgenstern (1947) has been a milestone in economics, describing rational behavior by two axioms on a weak preference on lotteries on a finite set of outcomes: the Independence Axiom and the Continuity Axiom. For a weak preference fulfilling the Independence Axiom, I prove that continuity is equivalent to the existence of a set indifferent lotteries spanning a hyperplane.
The Role of the Assumptions for the Existence of a General Equilibrium
Authors: Pablo Ahumada
Date: 2024-11-26 00:00:00

General Equilibrium Theory is the benchmark of economics, especially its results concerning the efficient allocation of resources, known as the First and Second Welfare Theorems. Yet, General Equilibrium Theory is beyond the scope of most economists. This paper is pitched as the first entry point into the theory. General Equilibrium Theory proves that at least one state of equilibrium always exists. In its most general approach, it uses fixed-point theorems to this end. This paper discusses the assumptions on individuals' behaviour and the structure of the system of exchange that guarantee that the conditions of the fixed-point theorems are satisfied. The purpose is to lay bare the role each plays in proving the existence of equilibrium and provide a clear picture of the relationship between the assumptions and the result. The discussion is presented in the simplest possible setting that captures the fundamental features of commodity exchange.
Speech Separation using Neural Audio Codecs with Embedding Loss
Authors: Jia Qi Yip, Chin Yuen Kwok, Bin Ma, Eng Siong Chng
Date: 2024-11-27 00:00:00
Tasks: Speech Separation

Neural audio codecs have revolutionized audio processing by enabling speech tasks to be performed on highly compressed representations. Recent work has shown that speech separation can be achieved within these compressed domains, offering faster training and reduced inference costs. However, current approaches still rely on waveform-based loss functions, necessitating unnecessary decoding steps during training. We propose a novel embedding loss for neural audio codec-based speech separation that operates directly on compressed audio representations, eliminating the need for decoding during training. To validate our approach, we conduct comprehensive evaluations using both objective metrics and perceptual assessment techniques, including intrusive and non-intrusive methods. Our results demonstrate that embedding loss can be used to train codec-based speech separation models with a 2x improvement in training speed and computational cost while achieving better DNSMOS and STOI performance on the WSJ0-2mix dataset across 3 different pre-trained codecs.
Disentangled-Transformer: An Explainable End-to-End Automatic Speech Recognition Model with Speech Content-Context Separation
Authors: Pu Wang, Hugo Van hamme
Date: 2024-11-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition

End-to-end transformer-based automatic speech recognition (ASR) systems often capture multiple speech traits in their learned representations that are highly entangled, leading to a lack of interpretability. In this study, we propose the explainable Disentangled-Transformer, which disentangles the internal representations into sub-embeddings with explicit content and speaker traits based on varying temporal resolutions. Experimental results show that the proposed Disentangled-Transformer produces a clear speaker identity, separated from the speech content, for speaker diarization while improving ASR performance.
k2SSL: A Faster and Better Framework for Self-Supervised Speech Representation Learning
Authors: Yifan Yang, Jianheng Zhuo, Zengrui Jin, Ziyang Ma, Xiaoyu Yang, Zengwei Yao, Liyong Guo, Wei Kang, Fangjun Kuang, Long Lin, Daniel Povey, Xie Chen
Date: 2024-11-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), GPU, Representation Learning, Self-Supervised Learning, speech-recognition, Speech Recognition, Speech Representation Learning

Self-supervised learning (SSL) has achieved great success in speech-related tasks. While Transformer and Conformer architectures have dominated SSL backbones, encoders like Zipformer, which excel in automatic speech recognition (ASR), remain unexplored in SSL. Concurrently, inefficiencies in data processing within existing SSL training frameworks, such as fairseq, pose challenges in managing the growing volumes of training data. To address these issues, we propose k2SSL, an open-source framework that offers faster, more memory-efficient, and better-performing self-supervised speech representation learning, focusing on downstream ASR tasks. The optimized HuBERT and proposed Zipformer-based SSL systems exhibit substantial reductions in both training time and memory usage during SSL training. Experiments on LibriSpeech demonstrate that Zipformer Base significantly outperforms HuBERT and WavLM, achieving up to a 34.8% relative WER reduction compared to HuBERT Base after fine-tuning, along with a 3.5x pre-training speedup in GPU hours. When scaled to 60k hours of LibriLight data, Zipformer Large exhibits remarkable efficiency, matching HuBERT Large's performance while requiring only 5/8 pre-training steps.
Image Generation with Supervised Selection Based on Multimodal Features for Semantic Communications
Authors: Chengyang Liang, Dong Li
Date: 2024-11-26 00:00:00
Tasks: Image Generation, Image Reconstruction, Semantic Communication

Semantic communication (SemCom) has emerged as a promising technique for the next-generation communication systems, in which the generation at the receiver side is allowed with semantic features' recovery. However, the majority of existing research predominantly utilizes a singular type of semantic information, such as text, images, or speech, to supervise and choose the generated source signals, which may not sufficiently encapsulate the comprehensive and accurate semantic information, and thus creating a performance bottleneck. In order to bridge this gap, in this paper, we propose and investigate a SemCom framework using multimodal information to supervise the generated image. To be specific, in this framework, we first extract semantic features at both the image and text levels utilizing the Convolutional Neural Network (CNN) architecture and the Contrastive Language-Image Pre-Training (CLIP) model before transmission. Then, we employ a generative diffusion model at the receiver to generate multiple images. In order to ensure the accurate extraction and facilitate high-fidelity image reconstruction, we select the "best" image with the minimum reconstruction errors by taking both the aided image and text semantic features into account. We further extend multimodal semantic communication (MMSemCom) system to the multiuser scenario for orthogonal transmission. Experimental results demonstrate that the proposed framework can not only achieve the enhanced fidelity and robustness in image transmission compared with existing communication systems but also sustain a high performance in the low signal-to-noise ratio (SNR) conditions.
Refining Focus in AI for Lung Cancer: Comparing Lesion-Centric and Chest-Region Models with Performance Insights from Internal and External Validation
Authors: Fakrul Islam Tushar
Date: 2024-11-25 00:00:00
Tasks: Cancer Classification, Lung Cancer Diagnosis

Background: AI-based classification models are essential for improving lung cancer diagnosis. However, the relative performance of lesion-level versus chest-region models in internal and external datasets remains unclear. Purpose: This study evaluates the performance of lesion-level and chest-region models for lung cancer classification, comparing their effectiveness across internal Duke Lung Nodule Dataset 2024 (DLND24) and external (LUNA16, NLST) datasets, with a focus on subgroup analyses by demographics, histology, and imaging characteristics. Materials and Methods: Two AI models were trained: one using lesion-centric patches (64,64,64) and the other using chest-region patches (512,512,8). Internal validation was conducted on DLND24, while external validation utilized LUNA16 and NLST datasets. The models performances were assessed using AUC-ROC, with subgroup analyses for demographic, clinical, and imaging factors. Statistical comparisons were performed using DeLongs test. Gradient-based visualizations and probability distribution were further used for analysis. Results: The lesion-level model consistently outperformed the chest-region model across datasets. In internal validation, the lesion-level model achieved an AUC of 0.71(CI: 0.61-0.81), compared to 0.68(0.57-0.77) for the chest-region model. External validation showed similar trends, with AUCs of 0.90(0.87-0.92) and 0.81(0.79-0.82) on LUNA16 and NLST, respectively. Subgroup analyses revealed significant advantages for lesion-level models in certain histological subtypes (adenocarcinoma) and imaging conditions (CT manufacturers). Conclusion: Lesion-level models demonstrate superior classification performance, especially for external datasets and challenging subgroups, suggesting their clinical utility for precision lung cancer diagnostics.
Spatial separation of closely-spaced users in measured distributed massive MIMO channels
Authors: YingJie Xu, Michiel Sandra, Xuesong Cai, Sara Willhammar, Fredrik Tufvesson
Date: 2024-11-27 00:00:00
Tasks: Fairness

Aiming for the sixth generation (6G) wireless communications, distributed massive multiple-input multiple-output (MIMO) systems hold significant potential for spatial multiplexing. In order to evaluate the ability of a distributed massive MIMO system to spatially separate closely spaced users, this paper presents an indoor channel measurement campaign. The measurements are carried out at a carrier frequency of 5.6 GHz with a bandwidth of 400 MHz, employing distributed antenna arrays with a total of 128 elements. Multiple scalar metrics are selected to evaluate spatial separability in line-of-sight, non line-of-sight, and mixed conditions. Firstly, through studying the singular value spread, it is shown that in line-of-sight conditions, better user orthogonality is achieved with a distributed MIMO setup compared to a co-located MIMO array. Furthermore, the dirty-paper coding (DPC) capacity and zero forcing (ZF) precoding sum-rate capacities are investigated across varying numbers of antennas and their topologies. The results show that in all three conditions, the less complex ZF precoder can be applied in distributed massive MIMO systems while still achieving a large fraction of the DPC capacity. Additionally, in line-of-sight conditions, both sum-rate capacities and user fairness benefit from more antennas and a more distributed antenna topology. However, in the given NLoS condition, the improvement in spatial separability through distributed antenna topologies is limited.
Learning Rate-Compatible Linear Block Codes: An Auto-Encoder Based Approach
Authors: Yukun Cheng, Wei Chen, Tianwei Hou, Geoffrey Ye Li, Bo Ai
Date: 2024-11-27 00:00:00

Artificial intelligence (AI) provides an alternative way to design channel coding with affordable complexity. However, most existing studies can only learn codes for a given size and rate, typically defined by a fixed network architecture and a set of parameters. The support of multiple code rates is essential for conserving bandwidth under varying channel conditions while it is costly to store multiple AI models or parameter sets. In this article, we propose an auto-encoder (AE) based rate-compatible linear block codes (RC-LBCs). The coding process associated with AI or non-AI decoders and multiple puncturing patterns is optimized in a data-driven manner. The superior performance of the proposed AI-based RC-LBC is demonstrated through our numerical experiments.
Signal Detection in Colored Noise Using the Condition Number of $F$-Matrices
Authors: Tharindu Udupitiya, Prathapasinghe Dharmawansa, Saman Atapattu, Chintha Tellambura, Merouane Debbah
Date: 2024-11-26 00:00:00

Signal detection in colored noise with an unknown covariance matrix has numerous applications across various scientific and engineering disciplines. The analysis focuses on the square of the condition number \(\kappa^2(\cdot)\), defined as the ratio of the largest to smallest eigenvalue \((\lambda_{\text{max}}/\lambda_{\text{min}})\) of the whitened sample covariance matrix \(\bm{\widehat{\Psi}}\), constructed from \(p\) signal-plus-noise samples and \(n\) noise-only samples, both \(m\)-dimensional. This statistic is denoted as \(\kappa^2(\bm{\widehat{\Psi}})\). A finite-dimensional characterization of the false alarm probability for this statistic under the null and alternative hypotheses has been an open problem. Therefore, in this work, we address this by deriving the cumulative distribution function (c.d.f.) of \(\kappa^2(\bm{\widehat{\Psi}})\) using the powerful orthogonal polynomial approach in random matrix theory. These c.d.f. expressions have been used to statistically characterize the performance of \(\kappa^2(\bm{\widehat{\Psi}})\).
A Digital Engineering Approach to Testing Modern AI and Complex Systems
Authors: Joseph R. Guerci, Sandeep Gogineni, Robert W. Schutz, Gavin I. McGee, Brian C. Watson, Hoan K. Nguyen, John Don Carlos, Daniel L. Stevens
Date: 2024-11-26 00:00:00
Tasks: Deep Learning

Modern AI (i.e., Deep Learning and its variants) is here to stay. However, its enigmatic black box nature presents a fundamental challenge to the traditional methods of test and validation (T&E). Or does it? In this paper we introduce a Digital Engineering (DE) approach to T&E (DE-T&E), combined with generative AI, that can achieve requisite mil spec statistical validation as well as uncover potential deleterious Black Swan events that might otherwise not be uncovered until it is too late. An illustration of these concepts is presented for an advanced modern radar example employing deep learning AI.