pwc-archive/papers-with-abstracts huggingface dataset:
Estimating Digital Product Trade through Corporate Revenue Data
Authors: Viktor Stojkoski, Philipp Koch, Eva Coll, Cesar A. Hidalgo
Date: 2023-10-03 00:00:00

Despite global efforts to harmonize international trade statistics, our understanding of digital trade and its implications remains limited. Here, we introduce a method to estimate bilateral exports and imports for dozens of sectors starting from the corporate revenue data of large digital firms. This method allows us to provide estimates for digitally ordered and delivered trade involving digital goods (e.g. video games), productized services (e.g. digital advertising), and digital intermediation fees (e.g. hotel rental), which together we call digital products. We use these estimates to study five key aspects of digital trade. We find that, compared to trade in physical goods, digital product exports are more spatially concentrated, have been growing faster, and can offset trade balance estimates, like the United States trade deficit on physical goods. We also find that countries that have decoupled economic growth from greenhouse gas emissions tend to have larger digital exports and that digital products exports contribute positively to the complexity of economies. This method, dataset, and findings provide a new lens to understand the impact of international trade in digital products.
Learning Probability Distributions of Day-Ahead Electricity Prices
Authors: Jozef Barunik, Lubos Hanus
Date: 2023-10-04 00:00:00

We propose a novel machine learning approach to probabilistic forecasting of hourly day-ahead electricity prices. In contrast to recent advances in data-rich probabilistic forecasting that approximate the distributions with some features such as moments, our method is non-parametric and selects the best distribution from all possible empirical distributions learned from the data. The model we propose is a multiple output neural network with a monotonicity adjusting penalty. Such a distributional neural network can learn complex patterns in electricity prices from data-rich environments and it outperforms state-of-the-art benchmarks.
Student debt and behavioral bias: a trillion dollar problem
Authors: Praful Raj
Date: 2023-10-03 00:00:00
Tasks: Management

This literature review elucidates the implications of behavioral biases, particularly those stemming from overconfidence and framing, on the intertemporal choices made by students on their underline demand preferences for student loans. A secondary objective is to understand the potential utility of social media to assist students and young borrowers with the debt repayment process and management of their loan tenures. A close examination of the literature reveals a substantial influence of these behavioral and cognitive principles on the intertemporal choices made by students towards debt repayments. This affects not only the magnitude of loans they acquire but also the anchoring of the terms of loan conditions associated with repayment. Furthermore, I establish that harnessing social media as the potential to cultivate financial literacy and enhanced understanding of loan terms to expedite the process of debt redemption. This review could serve as a valuable repository for students, scholars, and policymakers alike, in order to expound on the cognitive biases that students and consumers often face when applying and entering into loan contract.
Review on Decarbonizing the Transportation Sector in China: Overview, Analysis, and Perspectives
Authors: Jiewei Li, Ling Jin, Han Deng, Lin Yang
Date: 2023-10-01 00:00:00
Tasks: Decision Making

This review identifies challenges and effective strategies to decarbonize China's rapidly growing transportation sector, currently the third largest carbon emitter, considering China's commitment to peak carbon emissions by 2030 and achieve carbon neutrality by 2060. Key challenges include rising travel demand, unreached peak car ownership, declining bus ridership, gaps between energy technology research and practical application, and limited institutional capacity for decarbonization. This review categorizes current decarbonization measures, strategies, and policies in China's transportation sector using the "Avoid, Shift, Improve" framework, complemented by a novel strategic vector of "Institutional Capacity & Technology Development" to capture broader development perspectives. This comprehensive analysis aims to facilitate informed decision-making and promote collaborative strategies for China's transition to a sustainable transportation future.
Reconstructing supply networks
Authors: Luca Mungo, Alexandra Brintrup, Diego Garlaschelli, François Lafond
Date: 2023-09-30 00:00:00

Network reconstruction is a well-developed sub-field of network science, but it has only recently been applied to production networks, where nodes are firms and edges represent customer-supplier relationships. We review the literature that has flourished to infer the topology of these networks by partial, aggregate, or indirect observation of the data. We discuss why this is an important endeavour, what needs to be reconstructed, what makes it different from other network reconstruction problems, and how different researchers have approached the problem. We conclude with a research agenda.
The Distributional Impact of Inflation in Pakistan: A Case Study of a New Price Focused Microsimulation Framework, PRICES
Authors: Cathal ODonoghue, Beenish Amjad, Jules Linden, Nora Lustig, Denisa Sologon, Yang Wang
Date: 2023-09-30 00:00:00
Tasks: Survey

This paper develops a microsimulation model to simulate the distributional impact of price changes using Household Budget Survey data, income survey data and an Input Output Model. The primary purpose is to describe the model components. The secondary purpose is to demonstrate one component of the model by assessing the distributional and welfare impact of recent price changes in Pakistan. Over the period of November 2020 to November 2022, headline inflation 41.5 percent, with food and transportation prices increasing most. The analysis shows that despite large increases in energy prices, the importance of energy prices for the welfare losses due to inflation is limited because energy budget shares are small and inflation is relatively low. The overall distributional impact of recent price changes is mildly progressive, but household welfare is impacted significantly irrespective of households position along the income distribution. The biggest driver of welfare losses at the bottom of the income distribution was food price inflation, while inflation in other goods and services was the biggest driver at the top. To compensate households for increased living costs, transfers would need to be on average 40 percent of pre-inflation expenditure, assuming constant incomes. Behavioural responses to price changes have a negligible impact on the overall welfare cost to households.
General Equilibrium Theory for Climate Change
Authors: Robert M. Anderson, Haosui Duanmu
Date: 2023-10-05 00:00:00

We propose two general equilibrium models, quota equilibrium and emission tax equilibrium. The government specifies quotas or taxes on emissions, then refrains from further action. Quota equilibrium exists; the allocation of emission property rights strongly impacts the distribution of welfare. If the only externality arises from total net emissions, quota equilibrium is constrained Pareto Optimal. Every quota equilibrium can be realized as an emission tax equilibrium and vice versa. However, for certain tax rates, emission tax equilibrium may not exist, or may exhibit high multiplicity. Full Pareto Optimality of quota equilibrium can often be achieved by setting the right quota.
A Formal Transaction Cost-Based Analysis of the Economic Feasibility of Ecosystems
Authors: Christoph F. Strnadl
Date: 2023-10-04 00:00:00

Ecosystems enjoy increasing attention due to their flexibility and innovative power. It is well known, however, that this type of network-based economic governance structures occupies a potentially unstable position between the two stable (governance) endpoints, namely the firm (i.e., hierarchical governance) and the (open) market (i.e., coordination through the monetary system). This paper develops a formal (mathematical) theory of the economic value of (generic) ecosystem by extending transaction costs economics using certain elements from service-dominant logic. Within a first-best setting of rational actors, we derive analytical solutions for the hub-and-spoke and generic ecosystem configurations under some uniformity assumptions of ecosystem participants. Additionally, we are able to infer a generic condition for the welfare-maximizing and utility-maximizing price of the hub-and-spoke configuration in the familiar form of Lerner indices and elasticities. Relinquishing a first-best rational actors approach, we additionally derive several general propositions on (i) necessary conditions for the economic feasibility of ecosystem-based transactions, (ii) scaling requirements for ecosystem stability, and (iii) a generic feasibility condition for arbitrary provider-consumer ecosystems. Finally, we present an algebraic definition of business ecosystems and relate it to existing informal definition attempts. Thereby we demonstrate that the property of "being an ecosystem" of a network of transacting actors cannot be decided on structural grounds alone.
Matching with Generalized Sequential Reserves
Authors: Orhan Aygün, Bertan Turhan
Date: 2023-10-04 00:00:00

This paper introduces a general theory of sequential reserves by formulating a parameterized family of generalized sequential (GS) choice rules for institutions consisting of multiple divisions (or reserve categories). We show that the cumulative offer mechanism (COM) is the unique stable and strategy-proof mechanism when institutions' choice rules are in the GS family. Choice rules in real-life market design applications appear naturally in the GS family. We present two such real-world applications: affirmative action implementations in India and Chinese high school admissions.
A new proof for the existence of Nash equilibrium
Authors: Davide Carpentiere, Stephen Watson
Date: 2023-10-02 00:00:00

We present a new proof for the existence of a Nash equilibrium, which involves no fixed point theorem. The self-contained proof consists of two parts. The first part introduces the notions of root function and pre-equilibrium. The second part shows the existence of pre-equilibria and Nash equilibria.
Separately Convex and Separately Continuous Preferences: On Results of Schmeidler, Shafer, and Bergstrom-Parks-Rader
Authors: Metin Uyanik, Aniruddha Ghosh, M. Ali Khan
Date: 2023-10-01 00:00:00

We provide necessary and sufficient conditions for a correspondence taking values in a finite-dimensional Euclidean space to be open so as to revisit the pioneering work of Schmeidler (1969), Shafer (1974), Shafer-Sonnenschein (1975) and Bergstrom-Rader-Parks (1976) to answer several questions they and their followers left open. We introduce the notion of separate convexity for a correspondence and use it to relate to classical notions of continuity while giving salience to the notion of separateness as in the interplay of separate continuity and separate convexity of binary relations. As such, we provide a consolidation of the convexity-continuity postulates from a broad inter-disciplinary perspective and comment on how the qualified notions proposed here have implications of substantive interest for choice theory.
Latent Filling: Latent Space Data Augmentation for Zero-shot Speech Synthesis
Authors: Jae-Sung Bae, Joun Yeop Lee, Ji-Hyun Lee, Seongkyu Mun, Taehwa Kang, Hoon-Young Cho, Chanwoo Kim
Date: 2023-10-05 00:00:00
Tasks: Data Augmentation, Speech Synthesis, text-to-speech, Text to Speech

Previous works in zero-shot text-to-speech (ZS-TTS) have attempted to enhance its systems by enlarging the training data through crowd-sourcing or augmenting existing speech data. However, the use of low-quality data has led to a decline in the overall system performance. To avoid such degradation, instead of directly augmenting the input data, we propose a latent filling (LF) method that adopts simple but effective latent space data augmentation in the speaker embedding space of the ZS-TTS system. By incorporating a consistency loss, LF can be seamlessly integrated into existing ZS-TTS systems without the need for additional training stages. Experimental results show that LF significantly improves speaker similarity while preserving speech quality.
The VoiceMOS Challenge 2023: Zero-shot Subjective Speech Quality Prediction for Multiple Domains
Authors: Erica Cooper, Wen-Chin Huang, Yu Tsao, Hsin-Min Wang, Tomoki Toda, Junichi Yamagishi
Date: 2023-10-04 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

We present the second edition of the VoiceMOS Challenge, a scientific event that aims to promote the study of automatic prediction of the mean opinion score (MOS) of synthesized and processed speech. This year, we emphasize real-world and challenging zero-shot out-of-domain MOS prediction with three tracks for three different voice evaluation scenarios. Ten teams from industry and academia in seven different countries participated. Surprisingly, we found that the two sub-tracks of French text-to-speech synthesis had large differences in their predictability, and that singing voice-converted samples were not as difficult to predict as we had expected. Use of diverse datasets and listener information during training appeared to be successful approaches.
VITS-Based Singing Voice Conversion Leveraging Whisper and multi-scale F0 Modeling
Authors: Ziqian Ning, Yuepeng Jiang, Zhichao Wang, Bin Zhang, Lei Xie
Date: 2023-10-04 00:00:00
Tasks: Decoder, Voice Conversion

This paper introduces the T23 team's system submitted to the Singing Voice Conversion Challenge 2023. Following the recognition-synthesis framework, our singing conversion model is based on VITS, incorporating four key modules: a prior encoder, a posterior encoder, a decoder, and a parallel bank of transposed convolutions (PBTC) module. We particularly leverage Whisper, a powerful pre-trained ASR model, to extract bottleneck features (BNF) as the input of the prior encoder. Before BNF extraction, we perform pitch perturbation to the source signal to remove speaker timbre, which effectively avoids the leakage of the source speaker timbre to the target. Moreover, the PBTC module extracts multi-scale F0 as the auxiliary input to the prior encoder, thereby capturing better pitch variations of singing. We design a three-stage training strategy to better adapt the base model to the target speaker with limited target speaker data. Official challenge results show that our system has superior performance in naturalness, ranking 1st and 2nd respectively in Task 1 and 2. Further ablation justifies the effectiveness of our system design.
A Fused Deep Denoising Sound Coding Strategy for Bilateral Cochlear Implants
Authors: Tom Gajecki, Waldo Nogueira
Date: 2023-10-02 00:00:00
Tasks: Denoising, Speech Enhancement

Cochlear implants (CIs) provide a solution for individuals with severe sensorineural hearing loss to regain their hearing abilities. When someone experiences this form of hearing impairment in both ears, they may be equipped with two separate CI devices, which will typically further improve the CI benefits. This spatial hearing is particularly crucial when tackling the challenge of understanding speech in noisy environments, a common issue CI users face. Currently, extensive research is dedicated to developing algorithms that can autonomously filter out undesired background noises from desired speech signals. At present, some research focuses on achieving end-to-end denoising, either as an integral component of the initial CI signal processing or by fully integrating the denoising process into the CI sound coding strategy. This work is presented in the context of bilateral CI (BiCI) systems, where we propose a deep-learning-based bilateral speech enhancement model that shares information between both hearing sides. Specifically, we connect two monaural end-to-end deep denoising sound coding techniques through intermediary latent fusion layers. These layers amalgamate the latent representations generated by these techniques by multiplying them together, resulting in an enhanced ability to reduce noise and improve learning generalization. The objective instrumental results demonstrate that the proposed fused BiCI sound coding strategy achieves higher interaural coherence, superior noise reduction, and enhanced predicted speech intelligibility scores compared to the baseline methods. Furthermore, our speech-in-noise intelligibility results in BiCI users reveal that the deep denoising sound coding strategy can attain scores similar to those achieved in quiet conditions.
Deep learning-based image super-resolution of a novel end-expandable optical fiber probe for application in esophageal cancer diagnostics
Authors: Xiaohui Zhang, Mimi Tan, Mansour Nabil, Richa Shukla, Shaleen Vasavada, Sharmila Anandasabapathy, Mark A. Anastasio, Elena Petrova
Date: 2023-10-03 00:00:00
Tasks: Image Super-Resolution, Specificity, Super-Resolution

Significance: Endoscopic screening for esophageal cancer may enable early cancer diagnosis and treatment. While optical microendoscopic technology has shown promise in improving specificity, the limited field of view (<1 mm) significantly reduces the ability to survey large areas efficiently in esophageal cancer screening. Aim: To improve the efficiency of endoscopic screening, we proposed a novel end-expandable endoscopic optical fiber probe for larger field of visualization and employed a deep learning-based image super-resolution (DL-SR) method to overcome the issue of limited sampling capability. Approach: To demonstrate feasibility of the end-expandable optical fiber probe, DL-SR was applied on simulated low-resolution (LR) microendoscopic images to generate super-resolved (SR) ones. Varying the degradation model of image data acquisition, we identified the optimal parameters for optical fiber probe prototyping. The proposed screening method was validated with a human pathology reading study. Results: For various degradation parameters considered, the DL-SR method demonstrated different levels of improvement of traditional measures of image quality. The endoscopist interpretations of the SR images were comparable to those performed on the high-resolution ones. Conclusions: This work suggests avenues for development of DL-SR-enabled end-expandable optical fiber probes to improve high-yield esophageal cancer screening.
Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit with internal disorders
Authors: Dirk Elias Schut, Rachael Maree Wood, Anna Katharina Trull, Rob Schouten, Robert van Liere, Tristan van Leeuwen, Kees Joost Batenburg
Date: 2023-10-03 00:00:00
Tasks: Image Registration, Image Segmentation, Line Detection, Semantic Segmentation

A large percentage of apples are affected by internal disorders after long-term storage, which makes them unacceptable in the supply chain. CT imaging is a promising technique for in-line detection of these disorders. Therefore, it is crucial to understand how different disorders affect the image features that can be observed in CT scans. This paper presents a workflow for creating datasets of image pairs of photographs of apple slices and their corresponding CT slices. By having CT and photographic images of the same part of the apple, the complementary information in both images can be used to study the processes underlying internal disorders and how internal disorders can be measured in CT images. The workflow includes data acquisition, image segmentation, image registration, and validation methods. The image registration method aligns all available slices of an apple within a single optimization problem, assuming that the slices are parallel. This method outperformed optimizing the alignment separately for each slice. The workflow was applied to create a dataset of 1347 slice photographs and their corresponding CT slices. The dataset was acquired from 107 'Kanzi' apples that had been stored in controlled atmosphere (CA) storage for 8 months. In this dataset, the distance between annotations in the slice photograph and the matching CT slice was, on average, $1.47 \pm 0.40$ mm. Our workflow allows collecting large datasets of accurately aligned photo-CT image pairs, which can help distinguish internal disorders with a similar appearance on CT. With slight modifications, a similar workflow can be applied to other fruits or MRI instead of CT scans.
Spectro-spatial hyperspectral image reconstruction from interferometric acquisitions
Authors: Daniele Picone, Mohamad Jouni, Mauro Dalla-Mura
Date: 2023-10-03 00:00:00
Tasks: Image Reconstruction

In the last decade, novel hyperspectral cameras have been developed with particularly desirable characteristics of compactness and short acquisition time, retaining their potential to obtain spectral/spatial resolution competitive with respect to traditional cameras. However, a computational effort is required to recover an interpretable data cube. In this work we focus our attention on imaging spectrometers based on interferometry, for which the raw acquisition is an image whose spectral component is expressed as an interferogram. Previous works have focused on the inversion of such acquisition on a pixel-by-pixel basis within a Bayesian framework, leaving behind critical information on the spatial structure of the image data cube. In this work, we address this problem by integrating a spatial regularization for image reconstruction, showing that the combination of spectral and spatial regularizers leads to enhanced performances with respect to the pixelwise case. We compare our results with Plug-and-Play techniques, as its strategy to inject a set of denoisers from the literature can be implemented seamlessly with our physics-based formulation of the optimization problem.
A Restoration Network as an Implicit Prior
Authors: Yuyang Hu, Mauricio Delbracio, Peyman Milanfar, Ulugbek S. Kamilov
Date: 2023-10-02 00:00:00
Tasks: Image Restoration, Super-Resolution

Image denoisers have been shown to be powerful priors for solving inverse problems in imaging. In this work, we introduce a generalization of these methods that allows any image restoration network to be used as an implicit prior. The proposed method uses priors specified by deep neural networks pre-trained as general restoration operators. The method provides a principled approach for adapting state-of-the-art restoration models for other inverse problems. Our theoretical result analyzes its convergence to a stationary point of a global functional associated with the restoration operator. Numerical results show that the method using a super-resolution prior achieves state-of-the-art performance both quantitatively and qualitatively. Overall, this work offers a step forward for solving inverse problems by enabling the use of powerful pre-trained restoration models as priors.