pwc-archive/papers-with-abstracts huggingface dataset:
Coalescent processes emerging from large deviations
Authors: Ethan Levien
Date: 2023-08-28 00:00:00

The classical model for the genealogies of a neutrally evolving population in a fixed environment is due to Kingman. Kingman's coalescent process, which produces a binary tree, universally emerges from many microscopic models in which the variance in the number of offspring is finite. It is understood that power-law offspring distributions with infinite variance can result in a very different type of coalescent structure with merging of more than two lineages. Here we investigate the regime where the variance of the offspring distribution is finite but comparable to the population size. This is achieved by studying a model in which the log offspring sizes have a stretched exponential form. Such offspring distributions are motivated by biology, where they emerge from a toy model of growth in a heterogenous environment, but also mathematics and statistical physics, where limit theorems and phase transitions for sums over random exponentials have received considerable attention due to their appearance in the partition function of Derrida's Random Energy Model (REM). We find that the limit coalescent is a $\beta$-coalescent -- a previously studied model emerging from evolutionary dynamics models with heavy-tailed offspring distributions. We also discuss the connection to previous results on the REM.
What can we learn from the dynamics of the Covid-19 epidemic ?
Authors: Michel Peyrard
Date: 2023-08-27 00:00:00

We investigate the mechanisms behind the quasi-periodic outbursts on the Covid-19 epidemics. Data for France and Germany show that the patterns of outbursts exhibit a qualitative change in early 2022, which appears in a change in their average period, and which is confirmed by time-frequency analysis. This provides a signal which can be used to discriminate among several mechanisms. Two main ideas have been proposed to explain periodicity in epidemics. One involves memory effects and another considers exchanges between epidemic clusters and a reservoir of population. We test these two approaches in the particular case of the Covid-19 epidemics and show that the "cluster model" is the only one which appears to be able to explain the observed pattern with realistic parameters. A last section discusses our results in the context of early studies of epidemics, and we stress the importance to work with models with a limited number of parameters, which moreover can be sufficiently well estimated, to draw conclusions on the general mechanisms behind the observations.
Compartment model with retarded transition rates
Authors: Teo Granger, Thomas Michelitsch, Bernard Collet, Michael Bestehorn, Alejandro Riascos
Date: 2023-08-28 00:00:00
Tasks: model, Navigate

Our study is devoted to a four-compartment epidemic model of a constant population of independent random walkers. Each walker is in one of four compartments (S-susceptible, C-infected but not infectious (period of incubation), I-infected and infectious, R-recovered and immune) characterizing the states of health. The walkers navigate independently on a periodic 2D lattice. Infections occur by collisions of susceptible and infectious walkers. Once infected, a walker undergoes the delayed cyclic transition pathway S $\to$ C $\to$ I $\to$ R $\to$ S. The random delay times between the transitions (sojourn times in the compartments) are drawn from independent probability density functions (PDFs). We analyze the existence of the endemic equilibrium and stability of the globally healthy state and derive a condition for the spread of the epidemics which we connect with the basic reproduction number $R_0>1$. We give quantitative numerical evidence that a simple approach based on random walkers offers an appropriate microscopic picture of the dynamics for this class of epidemics.
Donut Regression Discontinuity Designs
Authors: Cladia Noack, Chistoph Rothe
Date: 2023-08-28 00:00:00
Tasks: regression

We study the econometric properties of so-called donut regression discontinuity (RD) designs, a robustness exercise which involves repeating estimation and inference without the data points in some area around the treatment threshold. This approach is often motivated by concerns that possible systematic sorting of units, or similar data issues, in some neighborhood of the treatment threshold might distort estimation and inference of RD treatment effects. We show that donut RD estimators can have substantially larger bias and variance than contentional RD estimators, and that the corresponding confidence intervals can be substantially longer. We also provide a formal testing framework for comparing donut and conventional RD estimation results.
Bandwidth Selection for Treatment Choice with Binary Outcomes
Authors: Takuya Ishihara
Date: 2023-08-28 00:00:00
Tasks: regression

This study considers the treatment choice problem when outcome variables are binary. We focus on statistical treatment rules that plug in fitted values based on nonparametric kernel regression and show that optimizing two parameters enables the calculation of the maximum regret. Using this result, we propose a novel bandwidth selection method based on the minimax regret criterion. Finally, we perform a numerical analysis to compare the optimal bandwidth choices for the binary and normally distributed outcomes.
Identification and Estimation of Demand Models with Endogenous Product Entry and Exit
Authors: Victor Aguirregabiria, Alessandro Iaria, Senay Sokullu
Date: 2023-08-27 00:00:00

This paper deals with the endogeneity of firms' entry and exit decisions in demand estimation. Product entry decisions lack a single crossing property in terms of demand unobservables, which causes the inconsistency of conventional methods dealing with selection. We present a novel and straightforward two-step approach to estimate demand while addressing endogenous product entry. In the first step, our method estimates a finite mixture model of product entry accommodating latent market types. In the second step, it estimates demand controlling for the propensity scores of all latent market types. We apply this approach to data from the airline industry.
Break-Point Date Estimation for Nonstationary Autoregressive and Predictive Regression Models
Authors: Christis Katsouris
Date: 2023-08-26 00:00:00
Tasks: regression

In this article, we study the statistical and asymptotic properties of break-point estimators in nonstationary autoregressive and predictive regression models for testing the presence of a single structural break at an unknown location in the full sample. Moreover, we investigate aspects such as how the persistence properties of covariates and the location of the break-point affects the limiting distribution of the proposed break-point estimators.
Splash! Robustifying Donor Pools for Policy Studies
Authors: Jared Amani Greathouse, Mani Bayani, Jason Coupet
Date: 2023-08-25 00:00:00

Policy researchers using synthetic control methods typically choose a donor pool in part by using policy domain expertise so the untreated units are most like the treated unit in the pre intervention period. This potentially leaves estimation open to biases, especially when researchers have many potential donors. We compare how functional principal component analysis synthetic control, forward-selection, and the original synthetic control method select donors. To do this, we use Gaussian Process simulations as well as policy case studies from West German Reunification, a hotel moratorium in Barcelona, and a sugar-sweetened beverage tax in San Francisco. We then summarize the implications for policy research and provide avenues for future work.
Crowdsourced data indicates broadband has a positive impact on local business creation
Authors: Yifeng Philip Chen, Edward J. Oughton, Jakub Zagdanski, Maggie Mo Jia, Peter Tyler
Date: 2023-08-28 00:00:00

Broadband connectivity is regarded as generally having a positive macroeconomic effect, but we lack evidence as to how it affects key economic activity metrics, such as firm creation, at a very local level. This analysis models the impact of broadband Next Generation Access (NGA) on new business creation at the local level over the 2011-2015 period in England, United Kingdom, using high-resolution panel data. After controlling for a range of factors, we find that faster broadband speeds brought by NGA technologies have a positive effect on the rate of business growth. We find that in England between 2011-2015, on average a one percentage increase in download speeds is associated with a 0.0574 percentage point increase in the annual growth rate of business establishments. The primary hypothesised mechanism behind the estimated relationship is the enabling effect that faster broadband speeds have on innovative business models based on new digital technologies and services. Entrepreneurs either sought appropriate locations that offer high quality broadband infrastructure (contributing to new business establishment growth), or potentially enjoyed a competitive advantage (resulting in a higher survival rate). The findings of this study suggest that aspiring to reach universal high capacity broadband connectivity is economically desirable, especially as the costs of delivering such service decline.
Managing Congestion in Two-Sided Platforms: The Case of Online Rentals
Authors: Caterina Calsamiglia, Laura Doval, Alejandro Robinson-Cortés, Matthew Shum
Date: 2023-08-28 00:00:00

Thick two-sided matching platforms, such as the room-rental market, face the challenge of showing relevant objects to users to reduce search costs. Many platforms use ranking algorithms to determine the order in which alternatives are shown to users. Ranking algorithms may depend on simple criteria, such as how long a listing has been on the platform, or incorporate more sophisticated aspects, such as personalized inferences about users' preferences. Using rich data on a room rental platform, we show how ranking algorithms can be a source of unnecessary congestion, especially when the ranking is invariant across users. Invariant rankings induce users to view, click, and request the same rooms in the platform we study, greatly limiting the number of matches it creates. We estimate preferences and simulate counterfactuals under different ranking algorithms varying the degree of user personalization and variation across users. In our case, increased personalization raises both user match utility and congestion, which leads to a trade-off. We find that the current outcome is inefficient as it lies below the possibility frontier, and propose alternatives that improve upon it.
Equity Pay In Networked Teams
Authors: Krishna Dasaratha, Benjamin Golub, Anant Shah
Date: 2023-08-28 00:00:00

A group of agents each exert effort to produce a joint output, with the complementarities between their efforts represented by a (weighted) network. Under equity compensation, a principal motivates the agents to work by giving them shares of the output. We describe the optimal equity allocation. It is characterized by a neighborhood balance condition: any two agents receiving equity have the same (weighted) total equity assigned to their neighbors. We also study the problem of selecting the team of agents who receive positive equity, and show this team must form a tight-knit subset of the complementarity network, with any pair being complementary to one another or jointly to another team member. Finally, we give conditions under which the amount of equity used for compensation is increasing in the strength of a team's complementarities and discuss several other applications.
Reputation Effects with Endogenous Records
Authors: Harry Pei
Date: 2023-08-26 00:00:00

A patient firm interacts with a sequence of consumers. The firm is either an honest type who supplies high quality and never erases its records, or an opportunistic type who chooses what quality to supply and may erase its records at a low cost. We show that in every equilibrium, the firm has an incentive to build a reputation for supplying high quality until its continuation value exceeds its commitment payoff, but its ex ante payoff must be close to its minmax value when it has a sufficiently long lifespan. Therefore, even a small fraction of opportunistic types can wipe out the firm's returns from building reputations. Even if the honest type can commit to reveal information about its history according to any disclosure policy, the opportunistic type's payoff cannot exceed its equilibrium payoff when the consumers receive no information.
Self-Enforced Job Matching
Authors: Ce Liu, Ziwei Wang, HanZhe Zhang
Date: 2023-08-26 00:00:00

The classic two-sided many-to-one job matching model assumes that firms treat workers as substitutes and workers ignore colleagues when choosing where to work. Relaxing these assumptions may lead to nonexistence of stable matchings. However, matching is often not a static allocation, but an ongoing process with long-lived firms and short-lived workers. We show that stability is always guaranteed dynamically when firms are patient, even with complementarities in firm technologies and peer effects in worker preferences. While no-poaching agreements are anti-competitive, they can maintain dynamic stability in markets that are otherwise unstable, which may contribute to their prevalence in labor markets.
Multivariate Majorization in Principal-Agents Models
Authors: Nicholas C Bedard, Jacob K Goeree, Ningyi Sun
Date: 2023-08-26 00:00:00
Tasks: Decision Making

We introduce a definition of multivariate majorization that is new to the economics literature. Our majorization technique allows us to generalize Mussa and Rosen's (1978) "ironing" to a broad class of multivariate principal-agents problems. Specifically, we consider adverse selection problems in which agents' types are one dimensional but informational externalities create a multidimensional ironing problem. Our majorization technique applies to discrete and continuous type spaces alike and we demonstrate its usefulness for contract theory and mechanism design. We further show that multivariate majorization yields a natural extension of second-order stochastic dominance to multiple dimensions and derive its implications for decision making under multivariate risk.
Using UK Biobank data to establish population-specific atlases from whole body MRI
Authors: Sophie Starck, Vasiliki Sideri-Lampretsa, Jessica J. M. Ritter, Veronika A. Zimmer, Rickmer Braren, Tamara T. Mueller, Daniel Rueckert
Date: 2023-08-28 00:00:00
Tasks: Diagnostic, Specificity

Reliable reference data in medical imaging is largely unavailable. Developing tools that allow for the comparison of individual patient data to reference data has a high potential to improve diagnostic imaging. Population atlases are a commonly used tool in medical imaging to facilitate this. Constructing such atlases becomes particularly challenging when working with highly heterogeneous datasets, such as whole-body images, which contain significant anatomical variations. In this work, we propose a pipeline for generating a standardised whole-body atlas for a highly heterogeneous population by partitioning the population into anatomically meaningful subgroups. Using magnetic resonance images from the UK Biobank dataset, we create six whole-body atlases representing a healthy population average. We furthermore unbias them, and this way obtain a realistic representation of the population. In addition to the anatomical atlases, we generate probabilistic atlases that capture the distributions of abdominal fat (visceral and subcutaneous) and five abdominal organs across the population (liver, spleen, pancreas, left and right kidneys). Our pipeline effectively generates high-quality, realistic whole-body atlases with clinical applicability. The probabilistic atlases show differences in fat distribution between subjects with medical conditions such as diabetes and cardiovascular diseases and healthy subjects in the atlas space. With this work, we make the constructed anatomical and label atlases publically available, with the expectation that they will support medical research involving whole-body MR images.
Enhancement of the Prefiltered Rotationally Invariant Non-local PCA Algorithm for MRI
Authors: Shiao Li
Date: 2023-08-27 00:00:00
Tasks: Denoising

Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique offering high-resolution 3D images and valuable insights into human tissue conditions. Even at present, the refinement of denoising methods for MRI remains a crucial concern for improving the quality of the images. This study aims to enhance the prefiltered rotationally invariant non-local principal component analysis (PRI-NL-PCA) algorithm. This paper relaxed the original restriction, using the particle swarm optimization and traversal method to determine the optimal parameters of the algorithm. This paper also combined the component filters of the original algorithm and picked the most suitable combination as the new collaborative algorithm. It was found that the original algorithm has already achieved the best possible outcome, apart from a few threshold parameters that need to be adjusted. The effective way to further enhance the performance is to attach only one NL-PCA filter before and after the pre-filtered rotationally invariant non-local mean (PRI-NLM) filter. Although the performance of the new collaborative algorithm is still a little short of advanced deep learning methods, it shows that the algorithm based on PCA denoising is indeed feasible. It requires only a few parameters to be adjusted, and it is conceivable that they can be determined directly from the image, granting it a strong general capacity for various body parts, and it merits further exploration. An auxiliary tool was also extracted from the new algorithm, encouraging further combination of it with other state-of-the-art methods to further improve their denoising performance.
Generalizable Learning Reconstruction for Accelerating MR Imaging via Federated Neural Architecture Search
Authors: Ruoyou Wu, Cheng Li, Juan Zou, Shanshan Wang
Date: 2023-08-27 00:00:00
Tasks: Efficient Neural Network, Fairness, Federated Learning, Image Reconstruction, Neural Architecture Search, Representation Learning

Heterogeneous data captured by different scanning devices and imaging protocols can affect the generalization performance of the deep learning magnetic resonance (MR) reconstruction model. While a centralized training model is effective in mitigating this problem, it raises concerns about privacy protection. Federated learning is a distributed training paradigm that can utilize multi-institutional data for collaborative training without sharing data. However, existing federated learning MR image reconstruction methods rely on models designed manually by experts, which are complex and computational expensive, suffering from performance degradation when facing heterogeneous data distributions. In addition, these methods give inadequate consideration to fairness issues, namely, ensuring that the model's training does not introduce bias towards any specific dataset's distribution. To this end, this paper proposes a generalizable federated neural architecture search framework for accelerating MR imaging (GAutoMRI). Specifically, automatic neural architecture search is investigated for effective and efficient neural network representation learning of MR images from different centers. Furthermore, we design a fairness adjustment approach that can enable the model to learn features fairly from inconsistent distributions of different devices and centers, and thus enforce the model generalize to the unseen center. Extensive experiments show that our proposed GAutoMRI has better performances and generalization ability compared with six state-of-the-art federated learning methods. Moreover, the GAutoMRI model is significantly more lightweight, making it an efficient choice for MR image reconstruction tasks. The code will be made available at https://github.com/ternencewu123/GAutoMRI.
Efficient Annotation for Medical Image Analysis: A One-Pass Selective Annotation Approach
Authors: Yuli Wang, Peiyu Duan, Zhangxing Bian, Anqi Feng, Yuan Xue
Date: 2023-08-25 00:00:00
Tasks: image-classification, Image Classification, Medical Image Analysis

Annotating biomedical images for supervised learning is a complex and labor-intensive task due to data diversity and its intricate nature. In this paper, we propose an innovative method, the efficient one-pass selective annotation (EPOSA), that significantly reduces the annotation burden while maintaining robust model performance. Our approach employs a variational autoencoder (VAE) to extract salient features from unannotated images, which are subsequently clustered using the DBSCAN algorithm. This process groups similar images together, forming distinct clusters. We then use a two-stage sample selection algorithm, called representative selection (RepSel), to form a selected dataset. The first stage is a Markov Chain Monte Carlo (MCMC) sampling technique to select representative samples from each cluster for annotations. This selection process is the second stage, which is guided by the principle of maximizing intra-cluster mutual information and minimizing inter-cluster mutual information. This ensures a diverse set of features for model training and minimizes outlier inclusion. The selected samples are used to train a VGG-16 network for image classification. Experimental results on the Med-MNIST dataset demonstrate that our proposed EPOSA outperforms random selection and other state-of-the-art methods under the same annotation budget, presenting a promising direction for efficient and effective annotation in medical image analysis.
Synthesizing 3D computed tomography from MRI or CBCT using 2.5D deep neural networks
Authors: Satoshi Kondo, Satoshi Kasai, Kousuke Hirasawa
Date: 2023-08-23 00:00:00

Deep learning techniques, particularly convolutional neural networks (CNNs), have gained traction for synthetic computed tomography (sCT) generation from Magnetic resonance imaging (MRI), Cone-beam computed tomography (CBCT) and PET. In this report, we introduce a method to syn-thesize CT from MRI or CBCT. Our method is based on multi-slice (2.5D) CNNs. 2.5D CNNs offer distinct advantages over 3D CNNs when dealing with volumetric data. In the experiments, we evaluate the performance of our method for two tasks, MRI-to-sCT and CBCT-to-sCT generation. Target organs for both tasks are brain and pelvis.
A time-causal and time-recursive analogue of the Gabor transform
Authors: Tony Lindeberg
Date: 2023-08-28 00:00:00

This paper presents a time-causal analogue of the Gabor filter, as well as a both time-causal and time-recursive analogue of the Gabor transform, where the proposed time-causal representations obey both temporal scale covariance and a cascade property with a simplifying kernel over temporal scales. The motivation behind these constructions is to enable theoretically well-founded time-frequency analysis over multiple temporal scales for real-time situations, or for physical or biological modelling situations, when the future cannot be accessed, and the non-causal access to future in Gabor filtering is therefore not viable for a time-frequency analysis of the system. We develop the theory for these representations, obtained by replacing the Gaussian kernel in Gabor filtering with a time-causal kernel, referred to as the time-causal limit kernel, which guarantees simplification properties from finer to coarser levels of scales in a time-causal situation, similar as the Gaussian kernel can be shown to guarantee over a non-causal temporal domain. In these ways, the proposed time-frequency representations guarantee well-founded treatment over multiple scales, in situations when the characteristic scales in the signals, or physical or biological phenomena, to be analyzed may vary substantially, and additionally all steps in the time-frequency analysis have to be fully time-causal.