pwc-archive/papers-with-abstracts huggingface dataset:
Optimal navigation of magnetic artificial microswimmers in blood capillaries with deep reinforcement learning
Authors: Lucas Amoudruz, Sergey Litvinov, Petros Koumoutsakos
Date: 2024-03-29 00:00:00
Tasks: Deep Reinforcement Learning

Biomedical applications such as targeted drug delivery, microsurgery, and sensing rely on reaching precise areas within the body in a minimally invasive way. Artificial bacterial flagella (ABFs) have emerged as potential tools for this task by navigating through the circulatory system with the help of external magnetic fields. While their swimming characteristics are well understood in simple settings, their controlled navigation through realistic capillary networks remains a significant challenge due to the complexity of blood flow and the high computational cost of detailed simulations. We address this challenge by conducting numerical simulations of ABFs in retinal capillaries, propelled by an external magnetic field. The simulations are based on a validated blood model that predicts the dynamics of individual red blood cells and their hydrodynamic interactions with ABFs. The magnetic field follows a control policy that brings the ABF to a prescribed target. The control policy is learned with an actor-critic, off-policy reinforcement learning algorithm coupled with a reduced-order model of the system. We show that the same policy robustly guides the ABF to a prescribed target in both the reduced-order model and the fine-grained blood simulations. This approach is suitable for designing robust control policies for personalized medicine at moderate computational cost.
New method for coherent imaging using incompatible sources
Authors: Boyang Li
Date: 2023-01-12 00:00:00

Non-invasive imaging plays a crucial role in the early detection, diagnosis, and treatment of numerous medical conditions. This article discusses recent advancements in coherent imaging techniques.The article begins by discussing the evolution of CDI technology, focusing on its higher resolution capabilities for detailed analysis and the use of white light in modern systems. Subsequently, advancements in CDI techniques are examined, such as imaging for white matter tract analysis. And its applications in real-time visualization of internal structures is highlighted, along with its application to various samples. Finally introduce a method to use coherent imaging method on incompatible non-coherent sources.
Marginal Treatment Effects and Monotonicity
Authors: Henrik Sigstad
Date: 2024-04-04 00:00:00

How robust are analyses based on marginal treatment effects (MTE) to violations of Imbens and Angrist (1994) monotonicity? In this note, I present weaker forms of monotonicity under which popular MTE-based estimands still identify the parameters of interest.
Bayesian Bi-level Sparse Group Regressions for Macroeconomic Density Forecasting
Authors: Matteo Mogliani, Anna Simoni
Date: 2024-04-03 00:00:00
Tasks: Econometrics, Time Series

We propose a Machine Learning approach for optimal macroeconomic density forecasting in a high-dimensional setting where the underlying model exhibits a known group structure. Our approach is general enough to encompass specific forecasting models featuring either many covariates, or unknown nonlinearities, or series sampled at different frequencies. By relying on the novel concept of bi-level sparsity in time-series econometrics, we construct density forecasts based on a prior that induces sparsity both at the group level and within groups. We demonstrate the consistency of both posterior and predictive distributions. We show that the posterior distribution contracts at the minimax-optimal rate and, asymptotically, puts mass on a set that includes the support of the model. Our theory allows for correlation between groups, while predictors in the same group can be characterized by strong covariation as well as common characteristics and patterns. Finite sample performance is illustrated through comprehensive Monte Carlo experiments and a real-data nowcasting exercise of the US GDP growth rate.
Moran's I 2-Stage Lasso: for Models with Spatial Correlation and Endogenous Variables
Authors: Sylvain Barde, Rowan Cherodian, Guy Tchuente
Date: 2024-04-03 00:00:00
Tasks: parameter estimation

We propose a novel estimation procedure for models with endogenous variables in the presence of spatial correlation based on Eigenvector Spatial Filtering. The procedure, called Moran's $I$ 2-Stage Lasso (Mi-2SL), uses a two-stage Lasso estimator where the Standardised Moran's I is used to set the Lasso tuning parameter. Unlike existing spatial econometric methods, this has the key benefit of not requiring the researcher to explicitly model the spatial correlation process, which is of interest in cases where they are only interested in removing the resulting bias when estimating the direct effect of covariates. We show the conditions necessary for consistent and asymptotically normal parameter estimation assuming the support (relevant) set of eigenvectors is known. Our Monte Carlo simulation results also show that Mi-2SL performs well against common alternatives in the presence of spatial correlation. Our empirical application replicates Cadena and Kovak (2016) instrumental variables estimates using Mi-2SL and shows that in that case, Mi-2SL can boost the performance of the first stage.
Improved Semi-Parametric Bounds for Tail Probability and Expected Loss: Theory and Applications
Authors: Zhaolin Li, Artem Prokhorov
Date: 2024-04-03 00:00:00
Tasks: Management

Many management decisions involve accumulated random realizations for which only the first and second moments of their distribution are available. The sharp Chebyshev-type bound for the tail probability and Scarf bound for the expected loss are widely used in this setting. We revisit the tail behavior of such quantities with a focus on independence. Conventional primal-dual approaches from optimization are ineffective in this setting. Instead, we use probabilistic inequalities to derive new bounds and offer new insights. For non-identical distributions attaining the tail probability bounds, we show that the extreme values are equidistant regardless of the distributional differences. For the bound on the expected loss, we show that the impact of each random variable on the expected sum can be isolated using an extension of the Korkine identity. We illustrate how these new results open up abundant practical applications, including improved pricing of product bundles, more precise option pricing, more efficient insurance design, and better inventory management. For example, we establish a new solution to the optimal bundling problem, yielding a 17% uplift in per-bundle profits, and a new solution to the inventory problem, yielding a 5.6% cost reduction for a model with 20 retailers.
Consumer Behavior under Benevolent Price Discrimination
Authors: Alexander Erlei, Mattheus Brenig, Nils Engelbrecht
Date: 2024-04-04 00:00:00

Extensive research shows that consumers are generally averse to price discrimination. However, instruments of differential pricing can benefit consumer surplus and alleviate inequity through targeted price discounts. This paper examines how these outcome considerations influence consumer reactions to price discrimination. Six studies with 3951 participants show that a large share of consumers is willing to costly switch away from a store that introduces a discount for low-income consumers. This happens irrespective of whether income differences are due to luck or merit. While the price-discriminating store does attract some new high-income consumers, it cannot compensate the loss of existing consumers. Allowing for altruistic preferences by simulating a market mechanism increases costly support for price discounts, but does not alleviate consumer aversions. Finally, we provide evidence that warm glow drives costly support for price discounts.
The economic consequences of geopolitical fragmentation: Evidence from the Cold War
Authors: Rodolfo G. Campos, Benedikt Heid, Jacopo Timini
Date: 2024-04-04 00:00:00

The Cold War was the defining episode of geopolitical fragmentation in the twentieth century. Trade between East and West across the Iron Curtain (a symbolical and physical barrier dividing Europe into two distinct areas) was restricted, but the severity of these restrictions varied over time. We quantify the trade and welfare effects of the Iron Curtain and show how the difficulty of trading across the Iron Curtain fluctuated throughout the Cold War. Using a novel dataset on trade between the two economic blocs and a quantitative trade model, we find that while the Iron Curtain at its height represented a tariff equivalent of 48% in 1951, trade between East and West gradually became easier until the fall of the Berlin Wall in 1989. Despite the easing of trade restrictions, we estimate that the Iron Curtain roughly halved East-West trade flows and caused substantial welfare losses in the Eastern bloc countries that persisted until the end of the Cold War. Conversely, the Iron Curtain led to an increase in intra-bloc trade, especially in the Eastern bloc, which outpaced the integration of Western Europe in the run-up to the formation of the European Union.
Collaboratively adding context to social media posts reduces the sharing of false news
Authors: Thomas Renault, David Restrepo Amariles, Aurore Troussel
Date: 2024-04-03 00:00:00
Tasks: Fact Checking

We build a novel database of around 285,000 notes from the Twitter Community Notes program to analyze the causal influence of appending contextual information to potentially misleading posts on their dissemination. Employing a difference in difference design, our findings reveal that adding context below a tweet reduces the number of retweets by almost half. A significant, albeit smaller, effect is observed when focusing on the number of replies or quotes. Community Notes also increase by 80% the probability that a tweet is deleted by its creator. The post-treatment impact is substantial, but the overall effect on tweet virality is contingent upon the timing of the contextual information's publication. Our research concludes that, although crowdsourced fact-checking is effective, its current speed may not be adequate to substantially reduce the dissemination of misleading information on social media.
Exploring Sustainable Clothing Consumption in Middle-Income Countries: A case study of Romanian consumers
Authors: Anastasia Cosma
Date: 2024-04-03 00:00:00

The overconsumption of consumers under today's increasingly scarce natural resources has overwhelmed the textile industry in middle-income countries, such as Romania. It is becoming more and more essential to encourage sustainable clothing consumption behaviors, such as purchasing recyclable clothes. Notwithstanding there is a limited number of studies trying to understand the intrinsic factors that motivate consumers' purchase intention toward sustainable clothes in middle-income countries. Moreover, the effect of consumers' environmental knowledge on determining their purchase intention of sustainable clothes remains understudied. Consequently, the purpose of this paper is to make a significant contribution to the sustainable consumption literature by providing a consolidated framework that explores the behavioral factors inclining Romanian consumers' purchase intention towards sustainable clothes. The foundation of this study combines consumers' social value orientation and the theory of planned behavior. the partial least square path modelling procedure was used to analyze the data of 1,018 Romanian respondents. The findings of this study show that altruistic value orientation, subjective norms, and sustainable attitudes have a positive effect on Romanian consumers' purchase intention of sustainable clothing. Thus, these insights provide essential practical implications of advocating for the consumption of sustainable clothes along with useful guidelines for practitioners in the textile industry among middle-income countries, especially in Romania, to reduce overconsumption.
From Time-inconsistency to Time-consistency for Optimal Stopping Problems
Authors: Sang Hu, Zihan Zhou
Date: 2024-04-03 00:00:00

For optimal stopping problems with time-inconsistent preference, we measure the inherent level of time-inconsistency by taking the time needed to turn the naive strategies into the sophisticated ones. In particular, when in a repeated experiment the naive agent can observe her actual sequence of actions which are inconsistent with what she has planned at the initial time, she then chooses her immediate action based on the observations on her later actual behavior. The procedure is repeated until her actual sequence of actions are consistent with her plan at any time. We show that for the preference value of cumulative prospect theory, in which the time-inconsistency is due to the probability distortion, the higher the degree of probability distortion, the more severe the level of time-inconsistency, and the more time required to turn the naive strategies into the sophisticated ones.
Balancing Efficiency and Equity in Classroom Assignment under Endogenous Peer Effects
Authors: Lei Bill Wang, Zhenbang Jiao, Om Prakash Bedant, Haoran Wang
Date: 2024-04-03 00:00:00
Tasks: counterfactual, Fairness

This paper presents a three-step empirical framework for optimizing classroom assignments under endogenous peer effects, using data from the China Education Panel Survey (CEPS). We design \textit{PeerNN}, a neural network that mimics endogenous network formation as a discrete choice model, generating a friendship-intensity matrix ($\Omega$) that captures student popularity. \textbf{Step 2: Estimating Peer Effects.} We measure the peer effect friends' average 6th-grade class rank weighted by $\Omega$ on 8th-grade cognitive test score. Incorporating $\Omega$ into the linear-in-means model induces endogeneity. Using quasi-random classroom assignments, we instrument friends' average 6th-grade class rank with the average classmates' 6th-grade class rank (unweighted by $\Omega$). Our main regression result shows that a 10\% improvement in friends' 6th-grade class rank raises 8th-grade cognitive test scores by 0.13 SD. Positive $\beta$ implies maximizing (minimizing) the popularity of high (low) achievers optimizes outcomes. \textbf{Step 3: Simulating Policy Trade-offs.} We use estimates from Step 1 and Step 2 to simulate optimal classroom assignments. We first implement a genetic algorithm (GA) to maximize average peer effect and observe a 1.9\% improvement. However, serious inequity issues arise: low-achieving students are hurt the most in the pursuit of the higher average peer effect. We propose an \textit{Algorithmically Fair GA} (AFGA), achieving a 1.2\% gain while ensuring more equitable educational outcomes. These results underscore that efficiency-focused classroom assignment policies can exacerbate inequality. We recommend incorporating fairness considerations when designing classroom assignment policies that account for endogenous spillovers.
Beyond Social Media Analogues
Authors: Gregory M. Dickinson
Date: 2024-04-02 00:00:00

The steady flow of social-media cases toward the Supreme Court shows a nation reworking its fundamental relationship with technology. The cases raise a host of questions ranging from difficult to impossible: how to nurture a vibrant public square when a few tech giants dominate the flow of information, how social media can be at the same time free from conformist groupthink and also protected against harmful disinformation campaigns, and how government and industry can cooperate on such problems without devolving toward censorship. To such profound questions, this Essay offers a comparatively modest contribution -- what not to do. Always the lawyer's instinct is toward analogy, considering what has come before and how it reveals what should come next. Almost invariably, that is the right choice. The law's cautious evolution protects society from disruptive change. But almost is not always, and, with social media, disruptive change is already upon us. Using social-media laws from Texas and Florida as a case study, this Essay shows how social-media's distinct features render it poorly suited to analysis by analogy and argues that courts should instead shift their attention toward crafting legal doctrines targeted to address social media's unique ills.
Chasing Contests
Authors: Zhuo Chen, Yun Liu
Date: 2024-04-03 00:00:00

This paper proposes a dynamic research contest, namely chasing contest, in which two asymmetric contestants exert costly effort to accomplish two breakthroughs. The contestants are asymmetric in that one of them is present-biased and has already achieved one breakthrough (the leader), whereas the other is time-consistent and needs to achieve two breakthroughs to win (the chaser). The principal can choose between two disclosure policies: immediately announcing the chaser's first breakthrough (public chasing contest) or announcing only the final result (hidden chasing contest). We characterize the unique x-start and y-stop equilibrium under both disclosure policies, in which the leader starts working from an instant x to the end while the chaser stops exerting effort by the instant y. In addition, the chaser will never stop earlier in the hidden chasing contest, whereas a late deadline extends the leader's effort in the public contest.
Farkas' Lemma and Complete Indifference
Authors: Florian Herold, Christoph Kuzmics
Date: 2024-04-03 00:00:00
Tasks: All, LEMMA

In a finite two player game consider the matrix of one player's payoff difference between any two consecutive pure strategies. Define the half space induced by a column vector of this matrix as the set of vectors that form an obtuse angle with this column vector. We use Farkas' lemma to show that this player can be made indifferent between all pure strategies if and only if the union of all these half spaces covers the whole vector space. This result leads to a necessary (and almost sufficient) condition for a game to have a completely mixed Nash equilibrium. We demonstrate its usefulness by providing the class of all symmetric two player three strategy games that have a unique and completely mixed symmetric Nash equilibrium.
Towards Transcranial 3D Ultrasound Localization Microscopy of the Nonhuman Primate Brain
Authors: Paul Xing, Vincent Perrot, Adan Ulises Dominguez-Vargas, Stephan Quessy, Numa Dancause, Jean Provost
Date: 2024-04-04 00:00:00

Hemodynamic changes occur in stroke and neurodegenerative diseases. Developing imaging techniques allowing the in vivo visualization and quantification of cerebral blood flow would help better understand the underlying mechanism of those cerebrovascular diseases. 3D ultrasound localization microscopy (ULM) is a novel technology that can map the microvasculature of the brain at large depth and has been mainly used until now in rodents. Here, we demonstrated the feasibility of 3D ULM of the nonhuman primate (NHP) brain with a single 256-channels programmable ultrasound scanner. We achieved a highly resolved vascular map of the macaque brain at large depth in presence of craniotomy and durectomy using an 8-MHz multiplexed matrix probe. We were able to distinguish vessels as small as 26.9 {\mu}m. We also demonstrated that transcranial imaging of the macaque brain at similar depth was feasible using a 3-MHz probe and achieved a resolution of 60.4 {\mu}m. This work paves the way to clinical application of 3D ULM.
CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learning
Authors: Ruoyou Wu, Jian Cheng, Cheng Li, Juan Zou, Jing Yang, Wenxin Fan, Yong Liang, Shanshan Wang
Date: 2024-04-04 00:00:00
Tasks: Diffusion  MRI, Representation Learning, Super-Resolution

Deep learning-based dMRI super-resolution methods can effectively enhance image resolution by leveraging the learning capabilities of neural networks on large datasets. However, these methods tend to learn a fixed scale mapping between low-resolution (LR) and high-resolution (HR) images, overlooking the need for radiologists to scale the images at arbitrary resolutions. Moreover, the pixel-wise loss in the image domain tends to generate over-smoothed results, losing fine textures and edge information. To address these issues, we propose a novel continuous super-resolution method for dMRI, called CSR-dMRI, which utilizes an anatomical structure-assisted implicit neural representation learning approach. Specifically, the CSR-dMRI model consists of two components. The first is the latent feature extractor, which primarily extracts latent space feature maps from LR dMRI and anatomical images while learning structural prior information from the anatomical images. The second is the implicit function network, which utilizes voxel coordinates and latent feature vectors to generate voxel intensities at corresponding positions. Additionally, a frequency-domain-based loss is introduced to preserve the structural and texture information, further enhancing the image quality. Extensive experiments on the publicly available HCP dataset validate the effectiveness of our approach. Furthermore, our method demonstrates superior generalization capability and can be applied to arbitrary-scale super-resolution, including non-integer scale factors, expanding its applicability beyond conventional approaches.
Terraced Compression Method with Automated Threshold Selection for Multidimensional Image Clustering of Heterogeneous Bodies
Authors: Jiatong Li, Gang Li, Nan Su Su Win, Ling Lin
Date: 2024-04-03 00:00:00
Tasks: Image Clustering

Multispectral transmission imaging provides strong benefits for early breast cancer screening. The frame accumulation method addresses the challenge of low grayscale and signal-to-noise ratio resulting from the strong absorption and scattering of light by breast tissue. This method introduces redundancy in data while improving the grayscale and signal-to-noise ratio of the image. Existing terraced compression algorithms effectively eliminate the data redundancy introduced by frame accumulation but necessitate significant time for manual debugging of threshold values. Hence, this paper proposes an improved terrace compression algorithm. The algorithm necessitates solely the input of the desired heterogeneous body size and autonomously calculates the optimal area threshold and gradient threshold by counting the grayscale and combining its distribution. Experimental acquisition involved multi-wavelength images of heterogeneous bodies exhibiting diverse textures, depths, and thicknesses. Subsequently, the method was applied after pre-processing to determine the thresholds for terraced compression at each wavelength, coupled with a window function for multi-dimensional image clustering. The results illustrate the method's efficacy in detecting and identifying various heterogeneous body types, depths, and thicknesses. This approach is expected to accurately identify the locations and types of breast tumors in the future, thus providing a more dependable tool for early breast cancer screening.