pwc-archive/papers-with-abstracts huggingface dataset:
Cryptocurrency Market Consolidation in 2020--2021
Authors: Jarosław Kwapień, Marcin Wątorek, Stanisław Drożdż
Date: 2021-12-13 00:00:00
Tasks: Time Series, Time Series Analysis

Time series of price returns for 80 of the most liquid cryptocurrencies listed on Binance are investigated for the presence of detrended cross-correlations. A spectral analysis of the detrended correlation matrix and a topological analysis of the minimal spanning trees calculated based on this matrix are applied for different positions of a moving window. The cryptocurrencies become more strongly cross-correlated among themselves than they used to be before. The average cross-correlations increase with time on a specific time scale in a way that resembles the Epps effect amplification when going from past to present. The minimal spanning trees also change their topology and, for the short time scales, they become more centralized with increasing maximum node degrees, while for the long time scales they become more distributed, but also more correlated at the same time. Apart from the inter-market dependencies, the detrended cross-correlations between the cryptocurrency market and some traditional markets, like the stock markets, commodity markets, and Forex, are also analyzed. The cryptocurrency market shows higher levels of cross-correlations with the other markets during the same turbulent periods, in which it is strongly cross-correlated itself.
COVID-19 Forecasts via Stock Market Indicators
Authors: Yi Liang, James Unwin
Date: 2021-12-13 00:00:00

Reliable short term forecasting can provide potentially lifesaving insights into logistical planning, and in particular, into the optimal allocation of resources such as hospital staff and equipment. By reinterpreting COVID-19 daily cases in terms of candlesticks, we are able to apply some of the most popular stock market technical indicators to obtain predictive power over the course of the pandemics. By providing a quantitative assessment of MACD, RSI, and candlestick analyses, we show their statistical significance in making predictions for both stock market data and WHO COVID-19 data. In particular, we show the utility of this novel approach by considering the identification of the beginnings of subsequent waves of the pandemic. Finally, our new methods are used to assess whether current health policies are impacting the growth in new COVID-19 cases.
Quantile Regression under Limited Dependent Variable
Authors: Javier Alejo, Gabriel Montes-Rojas
Date: 2021-12-13 00:00:00
Tasks: quantile regression, regression

A new Stata command, ldvqreg, is developed to estimate quantile regression models for the cases of censored (with lower and/or upper censoring) and binary dependent variables. The estimators are implemented using a smoothed version of the quantile regression objective function. Simulation exercises show that it correctly estimates the parameters and it should be implemented instead of the available quantile regression methods when censoring is present. An empirical application to women's labor supply in Uruguay is considered.
Cointegrating Polynomial Regressions with Power Law Trends: Environmental Kuznets Curve or Omitted Time Effects?
Authors: Yicong Lin, Hanno Reuvers
Date: 2020-09-04 00:00:00
Tasks: regression, Relation, valid

The environmental Kuznets curve predicts an inverted U-shaped relationship between environmental pollution and economic growth. Current analyses frequently employ models which restrict nonlinearities in the data to be explained by the economic growth variable only. We propose a Generalized Cointegrating Polynomial Regression (GCPR) to allow for an alternative source of nonlinearity. More specifically, the GCPR is a seemingly unrelated regression with (1) integer powers of deterministic and stochastic trends for the individual units, and (2) a common flexible global trend. We estimate this GCPR by nonlinear least squares and derive its asymptotic distribution. Endogeneity of the regressors will introduce nuisance parameters into the limiting distribution but a simulation-based approach nevertheless enables us to conduct valid inference. A multivariate subsampling KPSS test is proposed to verify the correct specification of the cointegrating relation. Our simulation study shows good performance of the simulated inference approach and subsampling KPSS test. We illustrate the GCPR approach using data for Austria, Belgium, Finland, the Netherlands, Switzerland, and the UK. A single global trend accurately captures all nonlinearities leading to a linear cointegrating relation between GDP and CO2 for all countries. This suggests that the environmental improvement of the last years is due to economic factors different from GDP.
Housing Price Prediction Model Selection Based on Lorenz and Concentration Curves: Empirical Evidence from Tehran Housing Market
Authors: Mohammad Mirbagherijam
Date: 2021-12-12 00:00:00
Tasks: Model Selection, Prediction, regression

This study contributes a house price prediction model selection in Tehran City based on the area between Lorenz curve (LC) and concentration curve (CC) of the predicted price by using 206,556 observed transaction data over the period from March 21, 2018, to February 19, 2021. Several different methods such as generalized linear models (GLM) and recursive partitioning and regression trees (RPART), random forests (RF) regression models, and neural network (NN) models were examined house price prediction. We used 90% of all data samples which were chosen randomly to estimate the parameters of pricing models and 10% of remaining datasets to test the accuracy of prediction. Results showed that the area between the LC and CC curves (which are known as ABC criterion) of real and predicted prices in the test data sample of the random forest regression model was less than by other models under study. The comparison of the calculated ABC criteria leads us to conclude that the nonlinear regression models such as RF regression models give an accurate prediction of house prices in Tehran City.
Will enterprise digital transformation affect diversification strategy?
Authors: Ge-zhi Wu, Da-ming You
Date: 2021-12-13 00:00:00

This paper empirically examines the impact of enterprise digital transformation on the level of enterprise diversification. It is found that the digital transformation of enterprises has significantly improved the level of enterprise diversification, and the conclusion has passed a series of robustness tests and endogenous tests. Through mechanism analysis, we find that the promotion effect of enterprise digital transformation on enterprise diversification is mainly realized through market power channel and firm risk channel, the pursuit of establishing market power, monopoly profits and challenge the monopolistic position of market occupiers based on digital transformation and the decentralization strategy to deal with the risks associated with digital transformation are important reasons for enterprises to adopt diversification strategy under the background of digital transformation. Although the organization costs channel, transaction costs channel, block holder control channel, industry type and information asymmetry channel have some influence on the main effect of this paper, they are not the main channel because they have not passed the inter group regression coefficient difference test statistically.
An installation-level model of China's coal sector shows how its decarbonization and energy security plans will reduce overseas coal imports
Authors: Jorrit Gosens, Alex Turnbull, Frank Jotzo
Date: 2021-12-13 00:00:00

China aims for net-zero carbon emissions by 2060, and an emissions peak before 2030. This will reduce its consumption of coal for power generation and steel making. Simultaneously, China aims for improved energy security, primarily with expanded domestic coal production and transport infrastructure. Here, we analyze effects of both these pressures on seaborne coal imports, with a purpose-built model of China's coal production, transport, and consumption system with installation-level geospatial and technical detail. This represents a 1000-fold increase in granularity versus earlier models, allowing representation of aspects that have previously been obscured. We find that reduced Chinese coal consumption affects seaborne imports much more strongly than domestic supply. Recent expansions of rail and port capacity, which reduce costs of getting domestic coal to Southern coastal provinces, will further reduce demand for seaborne thermal coal and amplify the effect of decarbonisation on coal imports. Seaborne coking coal imports are also likely to fall, because of expanded supply of cheap and high quality coking coal from neighbouring Mongolia.
U.S. Long-Term Earnings Outcomes by Sex, Race, Ethnicity, and Place of Birth
Authors: Kevin L. McKinney, John M. Abowd, Hubert P. Janicki
Date: 2021-12-10 00:00:00
Tasks: counterfactual, quantile regression

This paper is part of the Global Income Dynamics Project cross-country comparison of earnings inequality, volatility, and mobility. Using data from the U.S. Census Bureau's Longitudinal Employer-Household Dynamics (LEHD) infrastructure files we produce a uniform set of earnings statistics for the U.S. From 1998 to 2019, we find U.S. earnings inequality has increased and volatility has decreased. The combination of increased inequality and reduced volatility suggest earnings growth differs substantially across different demographic groups. We explore this further by estimating 12-year average earnings for a single cohort of age 25-54 eligible workers. Differences in labor supply (hours paid and quarters worked) are found to explain almost 90% of the variation in worker earnings, although even after controlling for labor supply substantial earnings differences across demographic groups remain unexplained. Using a quantile regression approach, we estimate counterfactual earnings distributions for each demographic group. We find that at the bottom of the earnings distribution differences in characteristics such as hours paid, geographic division, industry, and education explain almost all the earnings gap, however above the median the contribution of the differences in the returns to characteristics becomes the dominant component.
The Burst Market: the Next Leap for Humanity
Authors: Vincent Yuansang Zha
Date: 2021-12-05 00:00:00

Contemporary society grapples with a critical challenge in knowledge sharing: the scarcity of rapid, yet specific advice from relevant individuals. This situation underscores a deficiency in the existing labor market, hereafter referred to as the Conventional Market (CM), which struggles to support "Burst Jobs" - brief conversational tasks. This paper identifies high transaction costs, owing to technological limitations, as the primary reason for this inefficiency, causing huge job opportunity losses and human intelligence underutilization. In response, this study introduces the concept of an online Burst Market (BM), an innovative platform where individuals can instantaneously offer advice for a fee through video or audio conversations lasting as briefly as a few seconds or minutes. This paper examines the BM's potential to revolutionize job creation, alleviate poverty, address aging society problem, counteract AI-led unemployment, maximize human values with "Lifelong Working", and redefine the essence of job and the global economy.
Insurance design and arson-type risks
Authors: Jean-Gabriel Lauzier
Date: 2021-12-13 00:00:00
Tasks: Vocal Bursts Type Prediction

We design the insurance contract when the insurer faces arson-type risks. The optimal contract must be manipulation-proof. It is therefore continuous, it has a bounded slope, and it satisfies the no-sabotage condition when arson-type actions are free. Any contract that mixes a deductible, coinsurance and an upper limit is manipulation-proof. We also show that the ability to perform arson-type actions reduces the insured's welfare as less coverage is offered in equilibrium.
Envelope theorem and discontinuous optimisation: the case of positioning choice problems
Authors: Jean-Gabriel Lauzier
Date: 2021-12-13 00:00:00

This article examines differentiability properties of the value function of positioning choice problems, a class of optimisation problems in finite-dimensional Euclidean spaces. We show that positioning choice problems' value function is always almost everywhere differentiable even when the objective function is discontinuous. To obtain this result we first show that the Dini superdifferential is always well-defined for the maxima of positioning choice problems. This last property allows to state first-order necessary conditions in terms of Dini supergradients. We then prove our main result, which is an ad-hoc envelope theorem for positioning choice problems. Lastly, after discussing necessity of some key assumptions, we conjecture that similar theorems might hold in other spaces as well.
Ex-post moral hazard and manipulation-proof contracts
Authors: Jean-Gabriel Lauzier
Date: 2021-12-13 00:00:00

We examine the trade-off between the provision of incentives to exert costly effort (ex-ante moral hazard) and the incentives needed to prevent the agent from manipulating the profit observed by the principal (ex-post moral hazard). Formally, we build a model of two-stage hidden actions where the agent can both influence the expected revenue of a business and manipulate its observed profit. We show that manipulation-proofness is sensitive to the interaction between the manipulation technology and the probability distribution of the stochastic output. The optimal contract is manipulation-proof whenever the manipulation technology is linear. However, a convex manipulation technology sometimes leads to contracts with manipulations in equilibrium. Whenever the distribution satisfies the monotone likelihood ratio property, we can always find a manipulation technology for which the optimal contract is not manipulation-proof.
Robust Implementation with Costly Information
Authors: Harry Pei, Bruno Strulovici
Date: 2021-12-11 00:00:00

We study whether a planner can robustly implement a state-contingent social choice function when (i) agents must incur a cost to learn the state and (ii) the planner faces uncertainty regarding agents' preferences over outcomes, information costs, and beliefs and higher-order beliefs about one another's payoffs. We propose mechanisms that can approximately implement any desired social choice function when the perturbations concerning agents' payoffs have small ex ante probability. The mechanism is also robust to trembles in agents' strategies and when agents receive noisy information about the state.
Automatic Multi-Class Cardiovascular Magnetic Resonance Image Quality Assessment using Unsupervised Domain Adaptation in Spatial and Frequency Domains
Authors: Shahabedin Nabavi, Hossein Simchi, Mohsen Ebrahimi Moghaddam, Alejandro F. Frangi, Ahmad Ali Abin
Date: 2021-12-13 00:00:00
Tasks: Domain Adaptation, Image Quality Assessment, Image Reconstruction, Unsupervised Domain Adaptation, Weakly-supervised Learning

Population imaging studies rely upon good quality medical imagery before downstream image quantification. This study provides an automated approach to assess image quality from cardiovascular magnetic resonance (CMR) imaging at scale. We identify four common CMR imaging artefacts, including respiratory motion, cardiac motion, Gibbs ringing, and aliasing. The model can deal with images acquired in different views, including two, three, and four-chamber long-axis and short-axis cine CMR images. Two deep learning-based models in spatial and frequency domains are proposed. Besides recognising these artefacts, the proposed models are suitable to the common challenges of not having access to data labels. An unsupervised domain adaptation method and a Fourier-based convolutional neural network are proposed to overcome these challenges. We show that the proposed models reliably allow for CMR image quality assessment. The accuracies obtained for the spatial model in supervised and weakly supervised learning are 99.41+0.24 and 96.37+0.66 for the UK Biobank dataset, respectively. Using unsupervised domain adaptation can somewhat overcome the challenge of not having access to the data labels. The maximum achieved domain gap coverage in unsupervised domain adaptation is 16.86%. Domain adaptation can significantly improve a 5-class classification task and deal with considerable domain shift without data labels. Increasing the speed of training and testing can be achieved with the proposed model in the frequency domain. The frequency-domain model can achieve the same accuracy yet 1.548 times faster than the spatial model. This model can also be used directly on k-space data, and there is no need for image reconstruction.
Learning Token-based Representation for Image Retrieval
Authors: Hui Wu, Min Wang, Wengang Zhou, Yang Hu, Houqiang Li
Date: 2021-12-12 00:00:00
Tasks: Image Retrieval, Retrieval

In image retrieval, deep local features learned in a data-driven manner have been demonstrated effective to improve retrieval performance. To realize efficient retrieval on large image database, some approaches quantize deep local features with a large codebook and match images with aggregated match kernel. However, the complexity of these approaches is non-trivial with large memory footprint, which limits their capability to jointly perform feature learning and aggregation. To generate compact global representations while maintaining regional matching capability, we propose a unified framework to jointly learn local feature representation and aggregation. In our framework, we first extract deep local features using CNNs. Then, we design a tokenizer module to aggregate them into a few visual tokens, each corresponding to a specific visual pattern. This helps to remove background noise, and capture more discriminative regions in the image. Next, a refinement block is introduced to enhance the visual tokens with self-attention and cross-attention. Finally, different visual tokens are concatenated to generate a compact global representation. The whole framework is trained end-to-end with image-level labels. Extensive experiments are conducted to evaluate our approach, which outperforms the state-of-the-art methods on the Revisited Oxford and Paris datasets.
Exposure-Referred Signal-to-Noise Ratio for Digital Image Sensors
Authors: Abhiram Gnanasambandam, Stanley H. Chan
Date: 2021-12-10 00:00:00

The signal-to-noise ratio (SNR) is a fundamental tool to measure the performance of an image sensor. However, confusions sometimes arise between the two types of SNRs. The first one is the output-referred SNR which measures the ratio between the signal and the noise seen at the sensor's output. This SNR is easy to compute, and it is linear in the log-log scale for most image sensors. The second SNR is the exposure-referred SNR, also known as the input-referred SNR. This SNR considers the noise at the input by including a derivative term to the output-referred SNR. The two SNRs have similar behaviors for sensors with a large full-well capacity. However, for sensors with a small full-well capacity, the exposure-referred SNR can capture some behaviors that the output-referred SNR cannot. While the exposure-referred SNR has been known and used by the industry for a long time, a theoretically rigorous derivation from a signal processing perspective is lacking. In particular, while various equations can be found in different sources of the literature, there is currently no paper that attempts to assemble, derive, and organize these equations in one place. This paper aims to fill the gap by answering four questions: (1) How is the exposure-referred SNR derived from first principles? (2) Is the output-referred SNR a special case of the exposure-referred SNR, or are they completely different? (3) How to compute the SNR efficiently? (4) What utilities can the SNR bring to solving imaging tasks? New theoretical results are derived for image sensors of any bit-depth and full-well capacity.
Actuated Reflector-Based Three-dimensional Ultrasound Imaging with Adaptive-Delay Synthetic Aperture Focusing
Authors: Yichuan Tang, Ryosuke Tsumura, Jakub T. Kaminski, Haichong K. Zhang
Date: 2021-12-13 00:00:00
Tasks: Anatomy

Three-dimensional (3D) ultrasound (US) imaging addresses the limitation in field-of-view (FOV) in conventional two-dimensional (2D) US imaging by providing 3D viewing of the anatomy. 3D US imaging has been extensively adapted for diagnosis and image-guided surgical intervention. However, conventional approaches to implement 3D US imaging require either expensive and sophisticated 2D array transducers, or external actuation mechanisms to move a one-dimensional array mechanically. Here, we propose a 3D US imaging mechanism using actuated acoustic reflector instead of the sensor elements for volume acquisition with significantly extended 3D FOV, which can be implemented with simple hardware and compact size. To improve image quality on the elevation plane, we introduce an adaptive-delay synthetic aperture focusing (AD-SAF) method for elevation beamforming. We first evaluated the proposed imaging mechanism and AD-SAF with simulated point targets and cysts targets. Results of point targets suggested improved image quality on the elevation plane, and results of cysts targets demonstrated a potential to improve 3D visualization of human anatomy. We built a prototype imaging system that has a 3D FOV of 38 mm (lateral) by 38 mm (elevation) by 50 mm (axial) and collected data in imaging experiments with phantoms. Experimental data showed consistency with simulation results. The AD-SAF method enhanced quantifying the cyst volume size in the breast mimicking phantom compared to without elevation beamforming. These results suggested that the proposed 3D US imaging mechanism could potentially be applied in clinical scenarios.
Contactless Electrocardiogram Monitoring with Millimeter Wave Radar
Authors: Jinbo Chen, Dongheng Zhang, Zhi Wu, Fang Zhou, Qibin Sun, Yan Chen
Date: 2021-12-13 00:00:00

The electrocardiogram (ECG) has always been an important biomedical test to diagnose cardiovascular diseases. Current approaches for ECG monitoring are based on body attached electrodes leading to uncomfortable user experience. Therefore, contactless ECG monitoring has drawn tremendous attention, which however remains unsolved. In fact, cardiac electrical-mechanical activities are coupling in a well-coordinated pattern. In this paper, we achieve contactless ECG monitoring by breaking the boundary between the cardiac mechanical and electrical activity. Specifically, we develop a millimeter-wave radar system to contactlessly measure cardiac mechanical activity and reconstruct ECG without any contact in. To measure the cardiac mechanical activity comprehensively, we propose a series of signal processing algorithms to extract 4D cardiac motions from radio frequency (RF) signals. Furthermore, we design a deep neural network to solve the cardiac related domain transformation problem and achieve end-to-end reconstruction mapping from RF input to the ECG output. The experimental results show that our contactless ECG measurements achieve timing accuracy of cardiac electrical events with median error below 14ms and morphology accuracy with median Pearson-Correlation of 90% and median Root-Mean-Square-Error of 0.081mv compared to the groudtruth ECG. These results indicate that the system enables the potential of contactless, continuous and accurate ECG monitoring.