pwc-archive/papers-with-abstracts huggingface dataset:
Are Generation Z Less Car-centric Than Millennials? A Nationwide Analysis Through the Lens of Youth Licensing
Authors: Kailai Wang
Date: 2023-10-07 00:00:00

The debate on whether young Americans are becoming less reliant on automobiles is still ongoing. This research compares driver's license acquisition patterns between Millennials and their succeeding Generation Z during late adolescence. It also examines factors influencing teenagers' decisions to obtain driver's licenses. The findings suggest that the decline in licensing rates may be attributed in part to generational shifts in attitudes and cultural changes, such as Generation Z's inclination toward educational trips and their digital upbringing. This research underscores the implications for planners, practitioners, and policymakers in adapting to potential shifts in American car culture.
Bargain your share: The role of workers bargaining power for labor share, with reference to transition economies
Authors: Marjan Petreski, Stefan Tanevski
Date: 2023-10-07 00:00:00

The objective of the paper is to understand the role of workers bargaining for the labor share in transition economies. We rely on a share-capital schedule, whereby workers bargaining power is represented as a move off the schedule. Quantitative indicators of bargaining power are amended with own-constructed qualitative indices from textual information describing the legal enabling environment for bargaining in each country. Multiple data constraints impose reliance on a cross-sectional empirical model estimated with IV methods, whereby former unionization rates and the time since the adoption of the ILO Collective Bargaining Convention are used as exogenous instruments. The sample is composed of 23 industrial branches in 69 countries, of which 28 transition ones. In general, we find the stronger bargaining power to influence higher labor share, when the former is measured either quantitatively or qualitatively. On the contrary, higher bargaining power results in lower labor share in transition economies. This is likely a matter of delayed response to wage pushes, reconciled with the increasing role of MNCs which did not confront the workers power rise per se, but introduced automation and changed market structure amid labor-market flexibilization, which eventually deferred bargaining power-s positive effect on labor share.
Does Artificial Intelligence benefit UK businesses? An empirical study of the impact of AI on productivity
Authors: Sam Hainsworth
Date: 2023-10-06 00:00:00
Tasks: regression

Media hype and technological breakthroughs are fuelling the race to adopt Artificial Intelligence amongst the business community, but is there evidence to suggest this will increase productivity? This paper uses 2015-2019 microdata from the UK Office for National Statistics to identify if the adoption of Artificial Intelligence techniques increases labour productivity in UK businesses. Using fixed effects estimation (Within Group) with a log-linear regression specification the paper concludes that there is no statistically significant impact of AI adoption on labour productivity.
Theoretical Economics as Successive Approximations of Statistical Moments
Authors: Victor Olkhov
Date: 2023-09-28 00:00:00

This paper studies the links between the descriptions of macroeconomic variables and statistical moments of market trade, price, and return. The randomness of market trade values and volumes during the averaging interval {\Delta} results in the random properties of price and return. We describe how averages and volatilities of price and return depend on the averages, volatilities, and correlations of market trade values and volumes. The averages, volatilities, and correlations of market trade, price, and return can behave randomly during the long interval {\Delta}2>>{\Delta}. To describe their statistical properties during the long interval {\Delta}2, we introduce the secondary averaging procedure of trade, price, and return. We explain why, in the coming years, predictions of market-based probabilities of price and return will be limited by Gaussian distributions. We discuss the roots of the internal weakness of the commonly used hedging tool, Value-at-Risk, that cannot be solved and remains the source of additional risks and losses. One should consider theoretical economics as a set of successive approximations, each of which describes the next array of the n-th statistical moments of market trades, price, return, and macroeconomic variables, which are repeatedly averaged during the sequence of increasing time intervals.
The Mediating Effect of Blockchain Technology on the Cryptocurrency Purchase Intention
Authors: İbrahim Halil Efendioğlu, Gökhan Akel, Bekir Değirmenci, Dilek Aydoğdu, Kamile Elmasoğlu, Hande Begüm Bumin Doyduk, Arzu Şeker, Hatice Bahçe
Date: 2023-09-28 00:00:00

Cryptocurrencies, enabling secure digital asset transfers without a central authority, are experiencing increasing interest. With the increasing number of global and Turkish investors, it is evident that interest in digital assets will continue to rise sustainably, even in the face of financial fluctuations. However, it remains uncertain whether consumers perceive blockchain technology's ease of use and usefulness when purchasing cryptocurrencies. This study aims to explain blockchain technology's perceived ease of use and usefulness in cryptocurrency purchases by considering factors such as quality customer service, reduced costs, efficiency, and reliability. To achieve this goal, data were obtained from 463 participants interested in cryptocurrencies in different regions of Turkey. The data were analyzed using SPSS Process Macro programs. The analysis results indicate that perceived ease of use and usefulness mediate the effects of customer service and reduced costs, efficiency, and security on purchase intention.
Equivalence between individual and group strategy-proofness under stability
Authors: Pinaki Mandal
Date: 2023-10-08 00:00:00

This paper studies the (group) strategy-proofness aspect of two-sided matching markets under stability. For a one-to-one matching market, we show an equivalence between individual and group strategy-proofness under stability. We obtain this equivalence assuming the domain satisfies a richness condition. However, the result cannot be extended to the many-to-one matching markets. We further consider a setting with single-peaked preferences and characterize all domains compatible for stability and (group) strategy-proofness.
Is Arrow's Dictator a Drinker?
Authors: Jeffrey Uhlmann
Date: 2023-10-07 00:00:00

We critique the formulation of Arrow's no-dictator condition to show that it does not correspond to the accepted informal/intuitive interpretation. This has implications for the theorem's scope of applicability.
An Information Theory Approach to the Stock and Cryptocurrency Market: A Statistical Equilibrium Perspective
Authors: Emanuele Citera, Francesco De Pretis
Date: 2023-10-07 00:00:00

We study the stochastic structure of cryptocurrency rates of returns as compared to stock returns by focusing on the associated cross-sectional distributions. We build two datasets. The first comprises forty-six major cryptocurrencies, and the second includes all the companies listed in the S&P 500. We collect individual data from January 2017 until December 2022. We then apply the Quantal Response Statistical Equilibrium (QRSE) model to recover the cross-sectional frequency distribution of the daily returns of cryptocurrencies and S&P 500 companies. We study the stochastic structure of these two markets and the properties of investors' behavior over bear and bull trends. Finally, we compare the degree of informational efficiency of these two markets.
Privacy-oriented manipulation of speaker representations
Authors: Francisco Teixeira, Alberto Abad, Bhiksha Raj, Isabel Trancoso
Date: 2023-10-10 00:00:00
Tasks: Speaker Recognition, Speech Synthesis

Speaker embeddings are ubiquitous, with applications ranging from speaker recognition and diarization to speech synthesis and voice anonymisation. The amount of information held by these embeddings lends them versatility, but also raises privacy concerns. Speaker embeddings have been shown to contain information on age, sex, health and more, which speakers may want to keep private, especially when this information is not required for the target task. In this work, we propose a method for removing and manipulating private attributes from speaker embeddings that leverages a Vector-Quantized Variational Autoencoder architecture, combined with an adversarial classifier and a novel mutual information loss. We validate our model on two attributes, sex and age, and perform experiments with ignorant and fully-informed attackers, and with in-domain and out-of-domain data.
Discriminative Speech Recognition Rescoring with Pre-trained Language Models
Authors: Prashanth Gurunath Shivakumar, Jari Kolehmainen, Yile Gu, Ankur Gandhe, Ariya Rastrow, Ivan Bulyko
Date: 2023-10-10 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Second pass rescoring is a critical component of competitive automatic speech recognition (ASR) systems. Large language models have demonstrated their ability in using pre-trained information for better rescoring of ASR hypothesis. Discriminative training, directly optimizing the minimum word-error-rate (MWER) criterion typically improves rescoring. In this study, we propose and explore several discriminative fine-tuning schemes for pre-trained LMs. We propose two architectures based on different pooling strategies of output embeddings and compare with probability based MWER. We conduct detailed comparisons between pre-trained causal and bidirectional LMs in discriminative settings. Experiments on LibriSpeech demonstrate that all MWER training schemes are beneficial, giving additional gains upto 8.5\% WER. Proposed pooling variants achieve lower latency while retaining most improvements. Finally, our study concludes that bidirectionality is better utilized with discriminative training.
Compression Ratio Learning and Semantic Communications for Video Imaging
Authors: BoWen Zhang, Zhijin Qin, Geoffrey Ye Li
Date: 2023-10-10 00:00:00
Tasks: compressed sensing, Compressive Sensing, Semantic Communication, Video Compressive Sensing

Camera sensors have been widely used in intelligent robotic systems. Developing camera sensors with high sensing efficiency has always been important to reduce the power, memory, and other related resources. Inspired by recent success on programmable sensors and deep optic methods, we design a novel video compressed sensing system with spatially-variant compression ratios, which achieves higher imaging quality than the existing snapshot compressed imaging methods with the same sensing costs. In this article, we also investigate the data transmission methods for programmable sensors, where the performance of communication systems is evaluated by the reconstructed images or videos rather than the transmission of sensor data itself. Usually, different reconstruction algorithms are designed for applications in high dynamic range imaging, video compressive sensing, or motion debluring. This task-aware property inspires a semantic communication framework for programmable sensors. In this work, a policy-gradient based reinforcement learning method is introduced to achieve the explicit trade-off between the compression (or transmission) rate and the image distortion. Numerical results show the superiority of the proposed methods over existing baselines.
Domain Expansion via Network Adaptation for Solving Inverse Problems
Authors: Nebiyou Yismaw, Ulugbek S. Kamilov, M. Salman Asif
Date: 2023-10-10 00:00:00
Tasks: Domain Adaptation

Deep learning-based methods deliver state-of-the-art performance for solving inverse problems that arise in computational imaging. These methods can be broadly divided into two groups: (1) learn a network to map measurements to the signal estimate, which is known to be fragile; (2) learn a prior for the signal to use in an optimization-based recovery. Despite the impressive results from the latter approach, many of these methods also lack robustness to shifts in data distribution, measurements, and noise levels. Such domain shifts result in a performance gap and in some cases introduce undesired artifacts in the estimated signal. In this paper, we explore the qualitative and quantitative effects of various domain shifts and propose a flexible and parameter efficient framework that adapt pretrained networks to such shifts. We demonstrate the effectiveness of our method for a number of natural image, MRI, and CT reconstructions tasks under domain, measurement model, and noise-level shifts. Our experiments demonstrate that our method provides significantly better performance and parameter efficiency compared to existing domain adaptation techniques.
Efficient Predictive Coding of Intra Prediction Modes
Authors: Kevin Reuzé, Wassim Hamidouche, Pierrick Philippe, Olivier Déforges
Date: 2023-10-09 00:00:00
Tasks: Clustering, Decoder, Prediction

The high efficiency video coding (HEVC) standard and the joint exploration model (JEM) codec incorporate 35 and 67 intra prediction modes (IPMs) respectively, which are essential for efficient compression of Intra coded blocks. These IPMs are transmitted to the decoder through a coding scheme. In our paper, we present an innovative approach to construct a dedicated coding scheme for IPM based on contextual information. This approach comprises three key steps: prediction, clustering, and coding, each of which has been enhanced by introducing new elements, namely, labels for prediction, tests for clustering, and codes for coding. In this context, we have proposed a method that utilizes a genetic algorithm to minimize the rate cost, aiming to derive the most efficient coding scheme while leveraging the available labels, tests, and codes. The resulting coding scheme, expressed as a binary tree, achieves the highest coding efficiency for a given level of complexity. In our experimental evaluation under the HEVC standard, we observed significant bitrate gains while maintaining coding efficiency under the JEM codec. These results demonstrate the potential of our approach to improve compression efficiency, particularly under the HEVC standard, while preserving the coding efficiency of the JEM codec.
Cross-Task Data Augmentation by Pseudo-label Generation for Region Based Coronary Artery Instance Segmentation
Authors: Sandesh Pokhrel, Sanjay Bhandari, Eduard Vazquez, Yash Raj Shrestha, Binod Bhattarai
Date: 2023-10-08 00:00:00
Tasks: Coronary Artery Segmentation, Data Augmentation, Instance Segmentation, Pseudo Label, Segmentation, Semantic Segmentation

Coronary Artery Diseases (CADs) although preventable, are one of the leading causes of death and disability. Diagnosis of these diseases is often difficult and resource intensive. Angiographic imaging segmentation of the arteries has evolved as a tool of assistance that helps clinicians make an accurate diagnosis. However, due to the limited amount of data and the difficulty in curating a dataset, the task of segmentation has proven challenging. In this study, we introduce the use of pseudo-labels to address the issue of limited data in the angiographic dataset to enhance the performance of the baseline YOLO model. Unlike existing data augmentation techniques that improve the model constrained to a fixed dataset, we introduce the use of pseudo-labels generated on a dataset of separate related task to diversify and improve model performance. This method increases the baseline F1 score by 9% in the validation data set and by 3% in the test data set.
Longitudinal gOSNR Monitoring by Receiver-side Digital Signal Processing in Multi-Span Optical Transmission System
Authors: Choloong Hahn, Junho Chang, Zhiping Jiang
Date: 2023-10-10 00:00:00

We propose the world first longitudinal gOSNR estimation by using correlation template method at Rx, without any monitoring devices located in the middle of the link. The proposed method is experimentally demonstrated in a 12-span link with commercial transceiver.
ISAC 4D Imaging System Based on 5G Downlink Millimeter Wave Signal
Authors: Bohao Lu, Zhiqing Wei, Lin Wang, Ruiyun Zhang, Zhiyong Feng
Date: 2023-10-10 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated Sensing and Communication(ISAC) has become a key technology for the 5th generation (5G) and 6th generation (6G) wireless communications due to its high spectrum utilization efficiency. Utilizing infrastructure such as 5G Base Stations (BS) to realize environmental imaging and reconstruction is important for promoting the construction of smart cities. Current 4D imaging methods utilizing Frequency Modulated Continuous Wave (FMCW) based Fast Fourier Transform (FFT) are not suitable for ISAC scenarios due to the higher bandwidth occupation and lower resolution. We propose a 4D (3D-Coordinates, Velocity) imaging method with higher sensing accuracy based on 2D-FFT with 2D-MUSIC utilizing standard 5G Downlink (DL) millimeter wave (mmWave) signals. To improve the sensing precision we also design a transceiver antenna array element arrangement scheme based on MIMO virtual aperture technique. We further propose a target detection algorithm based on multi-dimensional Constant False Alarm (CFAR) detection, which optimizes the ISAC imaging signal processing flow and reduces the computational pressure of signal processing. Simulation results show that our proposed method has better imaging results. The code is publicly available at https://github.com/MrHaobolu/ISAC\_4D\_IMaging.git.
Streaming Heteroscedastic Probabilistic PCA with Missing Data
Authors: Kyle Gilman, David Hong, Jeffrey A. Fessler, Laura Balzano
Date: 2023-10-10 00:00:00
Tasks: Astronomy

Streaming principal component analysis (PCA) is an integral tool in large-scale machine learning for rapidly estimating low-dimensional subspaces from very high-dimensional data arriving at a high rate. However, modern datasets increasingly combine data from a variety of sources, and thus may exhibit heterogeneous quality across samples. Standard streaming PCA algorithms do not account for non-uniform noise, so their subspace estimates can quickly degrade. While the recently proposed Heteroscedastic Probabilistic PCA Technique (HePPCAT) addresses this heterogeneity, it was not designed to handle streaming data, which may exhibit non-stationary behavior. Moreover, HePPCAT does not allow for missing entries in the data, which can be common in streaming data. This paper proposes the Streaming HeteroscedASTic Algorithm for PCA (SHASTA-PCA) to bridge this divide. SHASTA-PCA employs a stochastic alternating expectation maximization approach that jointly learns the low-rank latent factors and the unknown noise variances from streaming data that may have missing entries and heteroscedastic noise, all while maintaining a low memory and computational footprint. Numerical experiments demonstrate the superior subspace estimation of our method compared to state-of-the-art streaming PCA algorithms in the heteroscedastic setting. Finally, we illustrate SHASTA-PCA applied to highly heterogeneous real data from astronomy.
Rate Compatible LDPC Neural Decoding Network: A Multi-Task Learning Approach
Authors: Yukun Cheng, Wei Chen, Lun Li, Bo Ai
Date: 2023-10-10 00:00:00
Tasks: Decoder, Multi-Task Learning

Deep learning based decoding networks have shown significant improvement in decoding LDPC codes, but the neural decoders are limited by rate-matching operations such as puncturing or extending, thus needing to train multiple decoders with different code rates for a variety of channel conditions. In this correspondence, we propose a Multi-Task Learning based rate-compatible LDPC ecoding network, which utilizes the structure of raptor-like LDPC codes and can deal with multiple code rates. In the proposed network, different portions of parameters are activated to deal with distinct code rates, which leads to parameter sharing among tasks. Numerical experiments demonstrate the effectiveness of the proposed method. Training the specially designed network under multiple code rates makes the decoder compatible with multiple code rates without sacrificing frame error rate performance.
Decomposition Based Interference Management Framework for Local 6G Networks
Authors: Samitha Gunarathne, Thushan Sivalingam, Nurul Huda Mahmood, Nandana Rajatheva, Matti Latva-aho
Date: 2023-10-09 00:00:00
Tasks: Management, Prediction

Managing inter-cell interference is among the major challenges in a wireless network, more so when strict quality of service needs to be guaranteed such as in ultra-reliable low latency communications (URLLC) applications. This study introduces a novel intelligent interference management framework for a local 6G network that allocates resources based on interference prediction. The proposed algorithm involves an advanced signal pre-processing technique known as empirical mode decomposition followed by prediction of each decomposed component using the sequence-to-one transformer algorithm. The predicted interference power is then used to estimate future signal-to-interference plus noise ratio, and subsequently allocate resources to guarantee the high reliability required by URLLC applications. Finally, an interference cancellation scheme is explored based on the predicted interference signal with the transformer model. The proposed sequence-to-one transformer model exhibits its robustness for interference prediction. The proposed scheme is numerically evaluated against two baseline algorithms, and is found that the root mean squared error is reduced by up to 55% over a baseline scheme.
Affine Frequency Division Multiplexing With Index Modulation
Authors: Yiwei Tao, Miaowen Wen, Yao Ge, Jun Li
Date: 2023-10-09 00:00:00
Tasks: Diversity

Affine frequency division multiplexing (AFDM) is a new multicarrier technique based on chirp signals tailored for high-mobility communications, which can achieve full diversity. In this paper, we propose an index modulation (IM) scheme based on the framework of AFDM systems, named AFDM-IM. In the proposed AFDM-IM scheme, the information bits are carried by the activation state of the subsymbols in discrete affine Fourier (DAF) domain in addition to the conventional constellation symbols. To efficiently perform IM, we divide the subsymbols in DAF domain into several groups and consider both the localized and distributed strategies. An asymptotically tight upper bound on the average bit error rate (BER) of the maximum-likelihood detection in the existence of channel estimation errors is derived in closed-form. Computer simulations are carried out to evaluate the performance of the proposed AFDM-IM scheme, whose results corroborate its superiority over the benchmark schemes in the linear time-varying channels. We also evaluate the BER performance of the index and modulated bits for the AFDM-IM scheme with and without satisfying the full diversity condition of AFDM. The results show that the index bits have a stronger diversity protection than the modulated bits even when the full diversity condition of AFDM is not satisfied.