pwc-archive/papers-with-abstracts huggingface dataset:
Can the creation of separate bidding zones within countries create imbalances in PV uptake? Evidence from Sweden
Authors: Johanna Fink
Date: 2023-12-26 00:00:00

This paper estimates how electricity price divergence within Sweden has affected incentives to invest in photovoltaic (PV) generation between 2016 and 2022 based on a synthetic control approach. Sweden is chosen as the research subject since it is together with Italy the only EU country with multiple bidding zones and is facing dramatic divergence in electricity prices between low-tariff bidding zones in Northern and high-tariff bidding zones in Southern Sweden since 2020. The results indicate that PV uptake in municipalities located north of the bidding zone border is reduced by 40.9-48% compared to their Southern counterparts. Based on these results, the creation of separate bidding zones within countries poses a threat to the expansion of PV generation and other renewables since it disincentivizes investment in areas with low electricity prices.
Mental Perception of Quality: Green Marketing as a Catalyst for Brand Quality Enhancement
Authors: Saleh Ghobbe, Mahdi Nohekhan
Date: 2023-12-26 00:00:00
Tasks: Marketing

The environmental conservation issue has led consumers to rethink the products they purchase. Nowadays, many consumers are willing to pay more for products that genuinely adhere to environmental standards to support the environment. Consequently, concepts like green marketing have gradually infiltrated marketing literature, making environmental considerations one of the most important activities for companies. Accordingly, this research investigates the impacts of green marketing strategy on perceived brand quality (case study: food exporting companies). The study population comprises 345 employees and managers from companies such as Kalleh, Solico, Pemina, Sorbon, Mac, Pol, and Casel. Using Cochran's formula, a sample of 182 individuals was randomly selected. This research is practical; the required data were collected through surveys and questionnaires. The findings indicate that (1) green marketing strategy has a significant positive effect on perceived brand quality, (2) green products have a significant positive effect on perceived brand quality, (3) green promotion has a significant positive effect on perceived brand quality, (4) green distribution has a significant positive effect on perceived brand quality, and (5) green pricing has a significant positive effect on perceived brand quality.
Dynamics of Global Emission Permit Prices and Regional Social Cost of Carbon under Noncooperation
Authors: Yongyang Cai, Khyati Malik, Hyeseon Shin
Date: 2023-12-24 00:00:00

We build a dynamic multi-region model of climate and economy with emission permit trading among 12 aggregated regions in the world. We solve for the dynamic Nash equilibrium under noncooperation, wherein each region adheres to the emission cap constraints following commitments that were first outlined in the 2015 Paris Agreement and later strengthened under the Glasgow Pact. Our model shows that the emission permit price reaches $845 per ton of carbon by 2050, and global average temperature is expected to reach 1.7{\deg}C above the pre-industrial level by the end of this century. We demonstrate, both theoretically and numerically, that a regional carbon tax is complementary to the global cap-and-trade system, and the optimal regional carbon tax is equal to the difference between the regional marginal abatement cost and the permit price. We show that optimal regional carbon tax has significant heterogeneity between regions, and the tax needs to be implemented in both the developed and the developing regions.
Learning from diversity: jati fractionalization, social expectations and improved sanitation practices in India
Authors: Sania Ashraf, Cristina Bicchieri, Upasak Das, Tanu Gupta, Alex Shpenev
Date: 2023-12-23 00:00:00
Tasks: Diversity

Prevalence of open defecation is associated with adverse health effects, detrimental not only for the individual but also the community. Therefore, neighborhood characteristics can influence collective progressive behavior such as improved sanitation practices. This paper uses primary data collected from rural and urban areas of Bihar to study the relationship between jati (sub-castes) level fractionalization within the community and toilet ownership and its usage for defecation. The findings indicate a diversity dividend wherein jati fractionalization is found to improve toilet ownership and usage significantly. While exploring the channels, we find social expectations to play an important role, where individuals from diverse communities tend to believe that there is a higher prevalence of toilet usage within the community. To assess the reasons for the existence of these social expectations, we use data from an egocentric network survey on a sub-sample of the households. The findings reveal that in fractionalized communities, the neighbors with whom our respondents interacted are more likely to be from different jatis. They are also more likely to use toilets and approve of its usage due to health reasons. Discussions about toilets are more common among neighbors from fractionalized communities, which underscore the discernible role of social learning. The inferences drawn from the paper have significant implications for community level behavioral change interventions that aim at reducing open defecation.
In the Line of Fire: A Systematic Review and Meta-Analysis of Job Burnout Among Nurses
Authors: Zahra Ghasemi Kooktapeh, Hakimeh Dustmohammadloo, Hooman Mehrdoost, Farivar Fatehi
Date: 2023-12-22 00:00:00
Tasks: Articles

Using a systematic review and meta-analysis, this study investigates the impact of the COVID-19 pandemic on job burnout among nurses. We review healthcare articles following the PRISMA 2020 guidelines and identify the main aspects and factors of burnout among nurses during the pandemic. Using the Maslach Burnout questionnaire, we searched PubMed, ScienceDirect, and Google Scholar, three open-access databases, for relevant sources measuring emotional burnout, personal failure, and nurse depersonalization. Two reviewers extract and screen data from the sources and evaluate the risk of bias. The analysis reveals that 2.75% of nurses experienced job burnout during the pandemic, with a 95% confidence interval and rates varying from 1.87% to 7.75%. These findings emphasize the need for interventions to address the pandemic's effect on job burnout among nurses and enhance their well-being and healthcare quality. We recommend considering individual, organizational, and contextual factors influencing healthcare workers' burnout. Future research should focus on identifying effective interventions to lower burnout in nurses and other healthcare professionals during pandemics and high-stress situations.
Optimal Strategies for the Decumulation of Retirement Savings under Differing Appetites for Liquidity and Investment Risks
Authors: Benjamin Avanzi, Lewis de Felice
Date: 2023-12-22 00:00:00

A retiree's appetite for risk is a common input into the lifetime utility models that are traditionally used to find optimal strategies for the decumulation of retirement savings. In this work, we consider a retiree with potentially differing appetites for the key financial risks of decumulation: liquidity risk and investment risk. We set out to determine whether these differing risk appetites have a significant impact on the retiree's optimal choice of decumulation strategy. To do so, we design and implement a framework which selects the optimal decumulation strategy from a general set of admissible strategies in line with a retiree's goals, and under differing appetites for the key risks of decumulation. Overall, we find significant evidence to suggest that a retiree's differing appetites for different decumulation risks will impact their optimal choice of strategy at retirement. Through an illustrative example calibrated to the Australian context, we find results which are consistent with actual behaviours in this jurisdiction (in particular, a shallow market for annuities), which lends support to our framework and may provide some new insight into the so-called annuity puzzle.
The Returns to Science in the Presence of Technological Risk
Authors: Matt Clancy
Date: 2023-12-21 00:00:00

Scientific and technological progress has historically been very beneficial to humanity but this does not always need to be true. Going forward, science may enable bad actors to cause genetically engineered pandemics that are more frequent and deadly than prior pandemics. I develop a quantitative economic model to assess the social returns to science, taking into account benefits to health and income, and forecast damages from new biological capabilities enabled by science. I set parameters for this model based on historical trends and forecasts from a large forecasting tournament of domain experts and superforecasters, which included forecasts about genetically engineered pandemic events. The results depend on the forecast likelihood that new scientific capabilities might lead to the end of our advanced civilization - there is substantial disagreement about this probability from participants in the forecasting tournament I use. If I set aside this remote possibility, I find the expected future social returns to science are strongly positive. Otherwise, the desirability of accelerating science depends on the value placed on the long-run future, in addition to which set of (quite different) forecasts of extinction risk are preferred. I also explore the sensitivity of these conclusions to a range of alternative assumptions.
Interdependent Total Factor Productivity in an Input-Output model
Authors: Thomas M. Bombarde, Andrew L. Krause
Date: 2023-12-23 00:00:00

Industries learn productivity improvements from their suppliers. The observed empirical importance of these interactions, often omitted by input-output models, mandates larger attention. This article embeds interdependent total factor productivity (TFP) growth into a general non-parametric input-output model. TFP growth is assumed to be Cobb-Douglas in TFP-stocks of adjacent sectors, where elasticities are the input-output coefficients. Studying how the steady state of the system reacts to changes in research effort bears insight for policy and the input-output literature. First, industries higher in the supply chain see a greater multiplication of their productivity gains. Second, the presence of `laggard' industries can bottleneck the the rest of the economy. By deriving these insights formally, we review a canonical method for aggregating TFP -- Hulten's Theorem -- and show the potential importance of backward linkages.
Inconsistency of Score-Elevated Reserve Policy for Indian Affirmative Action
Authors: Orhan Aygn, Bertan Turhan
Date: 2023-12-22 00:00:00

India has enacted an intricate affirmative action program through a reservation system since the 1950s. Notably, in 2008, a historic judgment by the Supreme Court of India (SCI) in the case of Ashoka Kumar Thakur vs. Union of India mandated a 27 percent reservation to the Other Backward Classes (OBC). The SCI's ruling suggested implementing the OBC reservation as a soft reserve without defining a procedural framework. The SCI recommended a maximum of 10 points difference between the cutoff scores of the open-category and OBC positions. We show that this directive conflicts with India's fundamental Supreme Court mandates on reservation policy. Moreover, we show that the score-elevated reserve policy proposed by S\"onmez and Yenmez (2022) is inconsistent with this directive.
Measuring the Concentration of Control in Contemporary Ethereum
Authors: Simon Brown
Date: 2023-12-22 00:00:00
Tasks: POS

Ethereum is undergoing significant changes to its architecture as it evolves. These changes include its switch to PoS consensus and the introduction of significant infrastructural changes that do not require a change to the core protocol, but that fundamentally affect the way users interact with the network. These changes represent an evolution toward a more modular architecture, in which there exists new exogenous vectors for centralization. This paper builds on previous studies of decentralization of Ethereum to reflect these recent significant changes, and Ethereum's new modular paradigm.
Exploring data augmentation in bias mitigation against non-native-accented speech
Authors: Yuanyuan Zhang, Aaricia Herygers, Tanvina Patel, Zhengjun Yue, Odette Scharenborg
Date: 2023-12-24 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, speech-recognition, Speech Recognition, Voice Conversion

Automatic speech recognition (ASR) should serve every speaker, not only the majority ``standard'' speakers of a language. In order to build inclusive ASR, mitigating the bias against speaker groups who speak in a ``non-standard'' or ``diverse'' way is crucial. We aim to mitigate the bias against non-native-accented Flemish in a Flemish ASR system. Since this is a low-resource problem, we investigate the optimal type of data augmentation, i.e., speed/pitch perturbation, cross-lingual voice conversion-based methods, and SpecAugment, applied to both native Flemish and non-native-accented Flemish, for bias mitigation. The results showed that specific types of data augmentation applied to both native and non-native-accented speech improve non-native-accented ASR while applying data augmentation to the non-native-accented speech is more conducive to bias reduction. Combining both gave the largest bias reduction for human-machine interaction (HMI) as well as read-type speech.
MaskCRT: Masked Conditional Residual Transformer for Learned Video Compression
Authors: Yi-Hsin Chen, Hong-Sheng Xie, Cheng-Wei Chen, Zong-Lin Gao, Martin Benjak, Wen-Hsiao Peng, Jörn Ostermann
Date: 2023-12-25 00:00:00
Tasks: MS-SSIM, SSIM, Video Compression

Conditional coding has lately emerged as the mainstream approach to learned video compression. However, a recent study shows that it may perform worse than residual coding when the information bottleneck arises. Conditional residual coding was thus proposed, creating a new school of thought to improve on conditional coding. Notably, conditional residual coding relies heavily on the assumption that the residual frame has a lower entropy rate than that of the intra frame. Recognizing that this assumption is not always true due to dis-occlusion phenomena or unreliable motion estimates, we propose a masked conditional residual coding scheme. It learns a soft mask to form a hybrid of conditional coding and conditional residual coding in a pixel adaptive manner. We introduce a Transformer-based conditional autoencoder. Several strategies are investigated with regard to how to condition a Transformer-based autoencoder for inter-frame coding, a topic that is largely under-explored. Additionally, we propose a channel transform module (CTM) to decorrelate the image latents along the channel dimension, with the aim of using the simple hyperprior to approach similar compression performance to the channel-wise autoregressive model. Experimental results confirm the superiority of our masked conditional residual transformer (termed MaskCRT) to both conditional coding and conditional residual coding. On commonly used datasets, MaskCRT shows comparable BD-rate results to VTM-17.0 under the low delay P configuration in terms of PSNR-RGB and outperforms VTM-17.0 in terms of MS-SSIM-RGB. It also opens up a new research direction for advancing learned video compression.
3DGR-CT: Sparse-View CT Reconstruction with a 3D Gaussian Representation
Authors: Yingtai Li, Xueming Fu, Han Li, Shang Zhao, Ruiyang Jin, S. Kevin Zhou
Date: 2023-12-25 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction, Novel View Synthesis

Sparse-view computed tomography (CT) reduces radiation exposure by acquiring fewer projections, making it a valuable tool in clinical scenarios where low-dose radiation is essential. However, this often results in increased noise and artifacts due to limited data. In this paper we propose a novel 3D Gaussian representation (3DGR) based method for sparse-view CT reconstruction. Inspired by recent success in novel view synthesis driven by 3D Gaussian splatting, we leverage the efficiency and expressiveness of 3D Gaussian representation as an alternative to implicit neural representation. To unleash the potential of 3DGR for CT imaging scenario, we propose two key innovations: (i) FBP-image-guided Guassian initialization and (ii) efficient integration with a differentiable CT projector. Extensive experiments and ablations on diverse datasets demonstrate the proposed 3DGR-CT consistently outperforms state-of-the-art counterpart methods, achieving higher reconstruction accuracy with faster convergence. Furthermore, we showcase the potential of 3DGR-CT for real-time physical simulation, which holds important clinical applications while challenging for implicit neural representations.
Perceptual Quality Assessment for Video Frame Interpolation
Authors: Jinliang Han, Xiongkuo Min, Yixuan Gao, Jun Jia, Lei Sun, Zuowei Cao, Yonglin Luo, Guangtao Zhai
Date: 2023-12-25 00:00:00
Tasks: Full reference image quality assessment, Full-Reference Image Quality Assessment, Image Quality Assessment, Triplet, Video Frame Interpolation

The quality of frames is significant for both research and application of video frame interpolation (VFI). In recent VFI studies, the methods of full-reference image quality assessment have generally been used to evaluate the quality of VFI frames. However, high frame rate reference videos, necessities for the full-reference methods, are difficult to obtain in most applications of VFI. To evaluate the quality of VFI frames without reference videos, a no-reference perceptual quality assessment method is proposed in this paper. This method is more compatible with VFI application and the evaluation scores from it are consistent with human subjective opinions. A new quality assessment dataset for VFI was constructed through subjective experiments firstly, to assess the opinion scores of interpolated frames. The dataset was created from triplets of frames extracted from high-quality videos using 9 state-of-the-art VFI algorithms. The proposed method evaluates the perceptual coherence of frames incorporating the original pair of VFI inputs. Specifically, the method applies a triplet network architecture, including three parallel feature pipelines, to extract the deep perceptual features of the interpolated frame as well as the original pair of frames. Coherence similarities of the two-way parallel features are jointly calculated and optimized as a perceptual metric. In the experiments, both full-reference and no-reference quality assessment methods were tested on the new quality dataset. The results show that the proposed method achieves the best performance among all compared quality assessment methods on the dataset.
DDistill-SR: Reparameterized Dynamic Distillation Network for Lightweight Image Super-Resolution
Authors: Yan Wang, Tongtong Su, Yusen Li, Jiuwen Cao, Gang Wang, Xiaoguang Liu
Date: 2023-12-22 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Recent research on deep convolutional neural networks (CNNs) has provided a significant performance boost on efficient super-resolution (SR) tasks by trading off the performance and applicability. However, most existing methods focus on subtracting feature processing consumption to reduce the parameters and calculations without refining the immediate features, which leads to inadequate information in the restoration. In this paper, we propose a lightweight network termed DDistill-SR, which significantly improves the SR quality by capturing and reusing more helpful information in a static-dynamic feature distillation manner. Specifically, we propose a plug-in reparameterized dynamic unit (RDU) to promote the performance and inference cost trade-off. During the training phase, the RDU learns to linearly combine multiple reparameterizable blocks by analyzing varied input statistics to enhance layer-level representation. In the inference phase, the RDU is equally converted to simple dynamic convolutions that explicitly capture robust dynamic and static feature maps. Then, the information distillation block is constructed by several RDUs to enforce hierarchical refinement and selective fusion of spatial context information. Furthermore, we propose a dynamic distillation fusion (DDF) module to enable dynamic signals aggregation and communication between hierarchical modules to further improve performance. Empirical results show that our DDistill-SR outperforms the baselines and achieves state-of-the-art results on most super-resolution domains with much fewer parameters and less computational overhead. We have released the code of DDistill-SR at https://github.com/icandle/DDistill-SR.
Digital twin-assisted three-dimensional electrical capacitance tomography for multiphase flow imaging
Authors: Shengnan Wang, Yi Li, Zhou Chen, Yunjie Yang
Date: 2023-12-22 00:00:00
Tasks: Computational Efficiency

Three-dimensional electrical capacitance tomography (3D-ECT) has shown promise for visualizing industrial multiphase flows. However, existing 3D-ECT approaches suffer from limited imaging resolution and lack assessment metrics, hampering their effectiveness in quantitative multiphase flow imaging. This paper presents a digital twin (DT)-assisted 3D-ECT, aiming to overcome these limitations and enhance our understanding of multiphase flow dynamics. The DT framework incorporates a 3D fluid-electrostatic field coupling model (3D-FECM) that digitally represents the physical 3D-ECT system, which enables us to simulate real multiphase flows and generate a comprehensive virtual multiphase flow 3D imaging dataset. Additionally, the framework includes a deep neural network named 3D deep back projection (3D-DBP), which learns multiphase flow features from the virtual dataset and enables more accurate 3D flow imaging in the real world. The DT-assisted 3D-ECT was validated through virtual and physical experiments, demonstrating superior image quality, noise robustness and computational efficiency compared to conventional 3D-ECT approaches. This research contributes to developing accurate and reliable 3D-ECT techniques and their implementation in multiphase flow systems across various industries.
Enhanced Color Palette Modeling for Lossless Screen Content Compression
Authors: Hannah Och, Shabhrish Reddy Uddehal, Tilo Strutz, André Kaup
Date: 2023-12-22 00:00:00

Soft context formation is a lossless image coding method for screen content. It encodes images pixel by pixel via arithmetic coding by collecting statistics for probability distribution estimation. Its main pipeline includes three stages, namely a context model based stage, a color palette stage and a residual coding stage. Each subsequent stage is only employed if the previous stage can not be applied since necessary statistics, e.g. colors or contexts, have not been learned yet. We propose the following enhancements: First, information from previous stages is used to remove redundant color palette entries and prediction errors in subsequent stages. Additionally, implicitly known stage decision signals are no longer explicitly transmitted. These enhancements lead to an average bit rate decrease of 1.07% on the evaluated data. Compared to VVC and HEVC, the proposed method needs roughly 0.44 and 0.17 bits per pixel less on average for 24-bit screen content images, respectively.