pwc-archive/papers-with-abstracts huggingface dataset:
Breaking the general election effect. The impact of the 2020 US presidential election on Chinese economy and counter strategies
Authors: Junjie Zhao
Date: 2023-04-23 00:00:00

The study of US-China relations has always been a crucial topic in our economic development [4][5][7], and the US presidential election plays an integral role in shaping these relations. The presidential election is held every four years, and it is crucial to assess the impact of the 2020 election on China to prepare for the potential effects of the 2024 US presidential election on the Chinese economy [8][16][20]. To achieve this, we have gathered statistical data from nearly 70 years and analyzed data related to the US economy. We have classified the collected data and utilized the analytic hierarchy process [1][2][3] to evaluate the President's policy implementation.This approach allowed us to obtain a comprehensive ranking of the indicators [6][9][11][33]. We then quantified the index data and employed the entropy weight method to calculate the weight of each index data. Finally, we used the weighted total score calculation to evaluate the economic status of the United States in a hierarchical manner after the election of Presidents Trump and Biden [15][18]. We optimized the index system by incorporating additional dimension indexes such as "foreign policy". We then crawled China's specific development data from 1990-2020 and substituted it into the model for analysis and evaluation. This enabled us to obtain detailed quantitative index data of the degree of influence [10][12][14]. To address China's shortcomings in science and technology innovation, we recommend strengthening economic cooperation with developed countries, diversifying market development, and actively expanding the domestic market through feasible solutions [13][16][23][36].
Strategic Responses to Personalized Pricing and Demand for Privacy: An Experiment
Authors: Inácio Bó, Li Chen, Rustamdjan Hakimov
Date: 2023-04-22 00:00:00

We consider situations where consumers are aware that a statistical model determines the price of a product based on their observed behavior. Using a novel experiment varying the context similarity between participant data and a product, we find that participants manipulate their responses to a survey about personal characteristics, and manipulation is more successful when the contexts are similar. Moreover, participants demand less privacy, and make less optimal privacy choices when the contexts are less similar. Our findings highlight the importance of data privacy policies in the age of big data, where behavior in seemingly unrelated contexts might affect prices.
How 'one-size-fits-all' public works contract does it better? An assessment of infrastructure provision in Italy
Authors: Massimo Finocchiaro Castroa, Calogero Guccio, Ilde Rizzo
Date: 2023-04-21 00:00:00
Tasks: All

Public infrastructure procurement is crucial as a prerequisite for public and private investments and for economic and social capital growth. However, low performance in execution severely hinders infrastructure provision and benefits delivery. One of the most sensitive phases in public infrastructure procurement is the design because of the strategic relationship that it potentially creates between procurers and contractors in the execution stage, affecting the costs and the duration of the contract. In this paper, using recent developments in non-parametric frontiers and propensity score matching, we evaluate the performance in the execution of public works in Italy. The analysis provides robust evidence of significant improvement of performance where procurers opt for a design and build contracts, which lead to lower transaction costs, allowing contractors to better accommodate the project in the execution. Our findings bear considerable policy implications.
The quality of school track assignment decisions by teachers
Authors: Joppe de Ree, Matthijs Oosterveen, Dinand Webbink
Date: 2023-04-20 00:00:00

We study the quality of secondary school track assignment decisions in the Netherlands, using a regression discontinuity design. In 6th grade, primary school teachers assign each student to a secondary school track. If a student scores above a track-specific cutoff on the standardized end-of-primary education test, the teacher can upwardly revise this assignment. By comparing students just left and right of these cutoffs, we find that between 50-90% of the students are "trapped in track": these students are on the high track after four years, only if they started on the high track in first year. The remaining (minority of) students are "always low": they are always on the low track after four years, independently of where they started. These proportions hold for students near the cutoffs that shift from the low to the high track in first year by scoring above the cutoff. Hence, for a majority of these students the initial (unrevised) track assignment decision is too low. The results replicate across most of the secondary school tracks, from the vocational to the academic tracks, and stand out against an education system with a lot of upward and downward track mobility.
Long memory, fractional integration and cointegration analysis of real convergence in Spain
Authors: Mariam Kamal, Josu Arteche
Date: 2023-04-03 00:00:00

This paper investigates economic convergence in terms of real income per capita among the autonomous regions of Spain. In order to converge, the series should cointegrate. This necessary condition is checked using two testing strategies recently proposed for fractional cointegration, finding no evidence of cointegration, which rules out the possibility of convergence between all or some of the Spanish regions. As an additional contribution, an extension of the critical values of one of the tests of fractional cointegration is provided for a different number of variables and sample sizes from those originally provided by the author, fitting those considered in this paper.
The structure of strategy-proof rules
Authors: Jorge Alcalde-Unzu, Marc Vorsatz
Date: 2023-04-25 00:00:00

We establish that all strategy-proof social choice rules in strict preference domains follow necessarily a two-step procedure. In the first step, agents are asked to reveal some specific information about their preferences. Afterwards, a subrule that is dictatorial or strategy-proof of range 2 must be applied, and the selected subrule may differ depending on the answers of the first step. As a consequence, the strategy-proof rules that have been identified in the literature for some domains can be reinterpreted in terms of our procedure and, more importantly, this procedure serves as a guide for determining the structure of the strategy-proof rules in domains that have not been explored yet.
Communication in the Infinitely Repeated Prisoner's Dilemma: Theory and Experiments
Authors: Maximilian Andres
Date: 2023-04-24 00:00:00

So far, the theory of equilibrium selection in the infinitely repeated prisoner's dilemma is insensitive to communication possibilities. To address this issue, we incorporate the assumption that communication reduces -- but does not entirely eliminate -- an agent's uncertainty that the other agent follows a cooperative strategy into the theory. Because of this, agents still worry about the payoff from cooperating when the other one defects, i.e. the sucker's payoff S, and, games with communication are more conducive to cooperation than games without communication. This theory is supported by data from laboratory experiments, and by machine learning based evaluation of the communication content.
Monotone comparative statics for submodular functions, with an application to aggregated deferred acceptance
Authors: Alfred Galichon, Yu-Wei Hsieh, Maxime Sylvestre
Date: 2023-04-24 00:00:00
Tasks: Combinatorial Optimization

We propose monotone comparative statics results for maximizers of submodular functions, as opposed to maximizers of supermodular functions as in the classical theory put forth by Veinott, Topkis, Milgrom, and Shannon among others. We introduce matrons, a natural structure that is dual to sublattices that generalizes existing structures such as matroids and polymatroids in combinatorial optimization and M-sets in discrete convex analysis. Our monotone comparative statics result is based on a natural order on matrons, which is dual in some sense to Veinott's strong set order on sublattices. As an application, we propose a deferred acceptance algorithm that operates in the case of divisible goods, and we study its convergence properties.
Homophily and infections: static and dynamic effects
Authors: Matteo Bizzarri, Fabrizio Panebianco, Paolo Pin
Date: 2023-04-24 00:00:00

We analyze the effect of homophily in the diffusion of a harmful state between two groups of agents that differ in immunization rates. Homophily has a very different impact on the steady state infection level (that is increasing in homophily when homophily is small, and decreasing when high), and on the cumulative number of infections generated by a deviation from the steady state (that, instead, is decreasing in homophily when homophily is small, and increasing when high). If immunization rates are endogenous, homophily has the opposite impact on the two groups. However, the sign of the group-level impact is the opposite if immunization is motivated by infection risk or by peer pressure. If motivations are group-specific, homophily can be harmful to both groups.
Choice Structures in Games
Authors: Paolo Galeazzi, Johannes Marti
Date: 2023-04-23 00:00:00

Following the decision-theoretic approach to game theory, we extend the analysis of Epstein & Wang and of Di Tillio from hierarchies of preference relations to hierarchies of choice functions. We then construct the universal choice structure containing all these choice hierarchies, and show how the universal preference structure of Di Tillio is embedded in it.
Preference Evolution under Partner Choice
Authors: Ziwei Wang, Jiabin Wu
Date: 2023-04-23 00:00:00
Tasks: Friction

We present a model that investigates preference evolution with endogenous matching. In the short run, individuals' subjective preferences influence partner selection and behavior in strategic interactions, which affect their material payoffs. These payoffs, in turn, determine how preferences evolve in the long run. To properly model the "match-to-interact" process, we combine stable matching and equilibrium concepts. Our analysis shows that endogenous matching gives rise to the evolutionary stability of a class of preferences that exhibit both affinity bias and efficient play. Such preferences stand out in the evolutionary process because they are able to force positive assortative matching and joint payoff maximization. Under incomplete information, a strong form of affinity bias, parochialism, is necessary for a preference to prevail in evolution, because stronger incentives are required to engage in self-sorting with information friction.
Compatibility between Stability and Strategy-Proofness: A Single-Peaked Preferences Investigation
Authors: Pinaki Mandal
Date: 2023-04-22 00:00:00

In two-sided matching markets, ensuring both stability and strategy-proofness poses a significant challenge; it is impossible when agents' preferences are unrestricted. But what if agents' preferences have specific restricted structures? Such scenarios frequently arise in real-world applications. This study explores the possibility of achieving both stability and strategy-proofness by focusing on scenarios where agents' preferences follow a structured pattern called single-peakedness. We focus on the simplest case - the well-known marriage problem, which is a one-to-one matching market. Despite its simplicity, this model is a useful starting point for exploration in many cases. Our main contribution is identifying all single-peaked subdomains on which stability and (weak/strong group) strategy-proofness are compatible, which we present through two key results. The first one characterizes all single-peaked subdomains with stable and (weakly group) strategy-proof matching rules, and identifies such a matching rule on these domains. The second one is an impossibility result that shows the incompatibility between stability and strong group strategy-proofness on single-peaked subdomains.
A Partial Order for Strictly Positive Coalitional Games and a Link from Risk Aversion to Cooperation
Authors: Jian Yang
Date: 2023-04-20 00:00:00

We deal with coalitional games possessing strictly positive values. Individually rational allocations of such a game has clear fractional interpretations. Many concepts, including the long-existing core and other stability notions more recently proposed by Yang \cite{Y22}, can all be re-cast in this fractional mode. The latter allows a certain ranking between games, which we deem as in the sense of ``centripetality'', to imply a clearly describable shift in the games' stable solutions. %These trends would be preserved after the imposition of the restriction that fractions be positive as well. When coalitions' values are built on both random outcomes and a common positively homogeneous reward function characterizing players' enjoyments from their shares, the above link could help explain why aversion to risk often promotes cooperation.
Partition-based Stability of Coalitional Games
Authors: Jian Yang
Date: 2023-04-20 00:00:00
Tasks: Blocking

We are concerned with the stability of a coalitional game, i.e., a transferable-utility (TU) cooperative game. First, the concept of core can be weakened so that the blocking of changes is limited to only those with multilateral backings. This principle of consensual blocking, as well as the traditional core-defining principle of unilateral blocking and one straddling in between, can all be applied to partition-allocation pairs. Each such pair is made up of a partition of the grand coalition and a corresponding allocation vector whose components are individually rational and efficient for the various constituent coalitions of the given partition. For the resulting strong, medium, and weak stability concepts, the first is core-compatible in that the traditional core exactly contains those allocations that are associated through this strong stability concept with the all-consolidated partition consisting of only the grand coalition. Probably more importantly, the latter medium and weak stability concepts are universal. By this, we mean that any game, no matter how ``poor'' it is, has its fair share of stable solutions. There is also a steepest ascent method to guide the convergence process to a mediumly stable partition-allocation pair from any starting partition.
Leveraging Audio-Tagging Assisted Sound Event Detection using Weakified Strong Labels and Frequency Dynamic Convolutions
Authors: Tanmay Khandelwal, Rohan Kumar Das, Andrew Koh, Eng Siong Chng
Date: 2023-04-25 00:00:00
Tasks: Audio Tagging, Event Detection, Sound Event Detection

Jointly learning from a small labeled set and a larger unlabeled set is an active research topic under semi-supervised learning (SSL). In this paper, we propose a novel SSL method based on a two-stage framework for leveraging a large unlabeled in-domain set. Stage-1 of our proposed framework focuses on audio-tagging (AT), which assists the sound event detection (SED) system in Stage-2. The AT system is trained utilizing a strongly labeled set converted into weak predictions referred to as weakified set, a weakly labeled set, and an unlabeled set. This AT system then infers on the unlabeled set to generate reliable pseudo-weak labels, which are used with the strongly and weakly labeled set to train a frequency dynamic convolutional recurrent neural network-based SED system at Stage-2 in a supervised manner. Our system outperforms the baseline by 45.5% in terms of polyphonic sound detection score on the DESED real validation set.
Improving Speech Translation Accuracy and Time Efficiency with Fine-tuned wav2vec 2.0-based Speech Segmentation
Authors: Ryo Fukuda, Katsuhito Sudoh, Satoshi Nakamura
Date: 2023-04-25 00:00:00
Tasks: Segmentation, Translation

Speech translation (ST) automatically converts utterances in a source language into text in another language. Splitting continuous speech into shorter segments, known as speech segmentation, plays an important role in ST. Recent segmentation methods trained to mimic the segmentation of ST corpora have surpassed traditional approaches. Tsiamas et al. proposed a segmentation frame classifier (SFC) based on a pre-trained speech encoder called wav2vec 2.0. Their method, named SHAS, retains 95-98% of the BLEU score for ST corpus segmentation. However, the segments generated by SHAS are very different from ST corpus segmentation and tend to be longer with multiple combined utterances. This is due to SHAS's reliance on length heuristics, i.e., it splits speech into segments of easily translatable length without fully considering the potential for ST improvement by splitting them into even shorter segments. Longer segments often degrade translation quality and ST's time efficiency. In this study, we extended SHAS to improve ST translation accuracy and efficiency by splitting speech into shorter segments that correspond to sentences. We introduced a simple segmentation algorithm using the moving average of SFC predictions without relying on length heuristics and explored wav2vec 2.0 fine-tuning for improved speech segmentation prediction. Our experimental results reveal that our speech segmentation method significantly improved the quality and the time efficiency of speech translation compared to SHAS.
MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled Data
Authors: Muhammad Shafique, Sizhuo Liu, Philip Schniter, Rizwan Ahmad
Date: 2023-04-25 00:00:00
Tasks: Denoising, Image Reconstruction, MRI Reconstruction

Objective: Acquiring fully sampled training data is challenging for many MRI applications. We present a self-supervised image reconstruction method, termed ReSiDe, capable of recovering images solely from undersampled data. Materials and Methods: ReSiDe is inspired by plug-and-play (PnP) methods, but unlike traditional PnP approaches that utilize pre-trained denoisers, ReSiDe iteratively trains the denoiser on the image or images that are being reconstructed. We introduce two variations of our method: ReSiDe-S and ReSiDe-M. ReSiDe-S is scan-specific and works with a single set of undersampled measurements, while ReSiDe-M operates on multiple sets of undersampled measurements and provides faster inference. Studies I, II, and III compare ReSiDe-S and ReSiDe-M against other self-supervised or unsupervised methods using data from T1- and T2-weighted brain MRI, MRXCAT digital perfusion phantom, and first-pass cardiac perfusion, respectively. Results: ReSiDe-S and ReSiDe-M outperform other methods in terms of peak signal-to-noise ratio and structural similarity index measure for Studies I and II, and in terms of expert scoring for Study III. Discussion: We present a self-supervised image reconstruction method and validate it in both static and dynamic MRI applications. These developments can benefit MRI applications where the availability of fully sampled training data is limited.
The Bjøntegaard Bible -- Why your Way of Comparing Video Codecs May Be Wrong
Authors: Christian Herglotz, Hannah Och, Anna Meyer, Geetha Ramasubbu, Lena Eichermüller, Matthias Kränzler, Fabian Brand, Kristian Fischer, Dat Thanh Nguyen, Andy Regensky, André Kaup
Date: 2023-04-25 00:00:00
Tasks: Quantization, SSIM, Video Compression

In this paper, we provide an in-depth assessment on the Bj{\o}ntegaard Delta. We construct a large data set of video compression performance comparisons using a diverse set of metrics including PSNR, VMAF, bitrate, and processing energies. These metrics are evaluated for visual data types such as classic perspective video, 360$^\circ$ video, point clouds, and screen content. As compression technology, we consider multiple hybrid video codecs as well as state-of-the-art neural network based compression methods. Using additional supporting points inbetween standard points defined by parameters such as the quantization parameter, we assess the interpolation error of the Bj{\o}ntegaard-Delta (BD) calculus and its impact on the final BD value. From the analysis, we find that the BD calculus is most accurate in the standard application of rate-distortion comparisons with mean errors below 0.5 percentage points. For other applications and special cases, e.g., VMAF quality, energy considerations, or inter-codec comparisons, the errors are higher (up to 5 percentage points), but can be halved by using a higher number of supporting points. We finally come up with recommendations on how to use the BD calculus such that the validity of the resulting BD-values is maximized. Main recommendations are as follows: First, relative curve differences should be plotted and analyzed. Second, the logarithmic domain should be used for saturating metrics such as SSIM and VMAF. Third, BD values below a certain threshold indicated by the subset error should not be used to draw recommendations. Fourth, using two supporting points is sufficient to obtain rough performance estimates.
A fast and flexible algorithm for microstructure reconstruction combining simulated annealing and deep learning
Authors: Zhenchuan Ma, Xiaohai He, Pengcheng Yan, Fan Zhang, Qizhi Teng
Date: 2023-04-25 00:00:00

The microstructure analyses of porous media have considerable research value for the study of macroscopic properties. As the premise of conducting these analyses, the accurate reconstruction of microstructure digital model is also an important component of the research. Computational reconstruction algorithms of microstructure have attracted much attention due to their low cost and excellent performance. However, it is still a challenge for computational reconstruction algorithms to achieve faster and more efficient reconstruction. The bottleneck lies in computational reconstruction algorithms, they are either too slow (traditional reconstruction algorithms) or not flexible to the training process (deep learning reconstruction algorithms). To address these limitations, we proposed a fast and flexible computational reconstruction algorithm, neural networks based on improved simulated annealing framework (ISAF-NN). The proposed algorithm is flexible and can complete training and reconstruction in a short time with only one two-dimensional image. By adjusting the size of input, it can also achieve reconstruction of arbitrary size. Finally, the proposed algorithm is experimentally performed on a variety of isotropic and anisotropic materials to verify the effectiveness and generalization.