pwc-archive/papers-with-abstracts huggingface dataset:
The miscalibration of the honeybee odometer
Authors: Laura Luebbert, Lior Pachter
Date: 2024-05-08 00:00:00
Tasks: Articles

We examine a series of articles on honeybee odometry and navigation published between 1996 and 2010, and find inconsistencies in results, duplicated figures, indications of data manipulation, and incorrect calculations. This suggests that redoing the experiments in question is warranted.
Single color digital H&E staining with In-and-Out Net
Authors: Mengkun Chen, Yen-Tung Liu, Fadeel Sher Khan, Matthew C. Fox, Jason S. Reichenberg, Fabiana C. P. S. Lopes, Katherine R. Sebastian, Mia K. Markey, James W. Tunnell
Date: 2024-05-22 00:00:00
Tasks: Image Registration, Virtual Staining

Virtual staining streamlines traditional staining procedures by digitally generating stained images from unstained or differently stained images. While conventional staining methods involve time-consuming chemical processes, virtual staining offers an efficient and low infrastructure alternative. Leveraging microscopy-based techniques, such as confocal microscopy, researchers can expedite tissue analysis without the need for physical sectioning. However, interpreting grayscale or pseudo-color microscopic images remains a challenge for pathologists and surgeons accustomed to traditional histologically stained images. To fill this gap, various studies explore digitally simulating staining to mimic targeted histological stains. This paper introduces a novel network, In-and-Out Net, specifically designed for virtual staining tasks. Based on Generative Adversarial Networks (GAN), our model efficiently transforms Reflectance Confocal Microscopy (RCM) images into Hematoxylin and Eosin (H&E) stained images. We enhance nuclei contrast in RCM images using aluminum chloride preprocessing for skin tissues. Training the model with virtual H\&E labels featuring two fluorescence channels eliminates the need for image registration and provides pixel-level ground truth. Our contributions include proposing an optimal training strategy, conducting a comparative analysis demonstrating state-of-the-art performance, validating the model through an ablation study, and collecting perfectly matched input and ground truth images without registration. In-and-Out Net showcases promising results, offering a valuable tool for virtual staining tasks and advancing the field of histological image analysis.
Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learning
Authors: Maximilian Nägele, Jan Olle, Thomas Fösel, Remmy Zen, Florian Marquardt
Date: 2024-05-22 00:00:00
Tasks: Portfolio Optimization, reinforcement-learning, Reinforcement Learning

Markov decision processes (MDPs) are used to model a wide variety of applications ranging from game playing over robotics to finance. Their optimal policy typically maximizes the expected sum of rewards given at each step of the decision process. However, a large class of problems does not fit straightforwardly into this framework: Non-cumulative Markov decision processes (NCMDPs), where instead of the expected sum of rewards, the expected value of an arbitrary function of the rewards is maximized. Example functions include the maximum of the rewards or their mean divided by their standard deviation. In this work, we introduce a general mapping of NCMDPs to standard MDPs. This allows all techniques developed to find optimal policies for MDPs, such as reinforcement learning or dynamic programming, to be directly applied to the larger class of NCMDPs. Focusing on reinforcement learning, we show applications in a diverse set of tasks, including classical control, portfolio optimization in finance, and discrete optimization problems. Given our approach, we can improve both final performance and training time compared to relying on standard MDPs.
On the Identifying Power of Monotonicity for Average Treatment Effects
Authors: Yuehao Bai, Shunzhuang Huang, Sarah Moon, Azeem M. Shaikh, Edward J. Vytlacil
Date: 2024-05-23 00:00:00

In the context of a binary outcome, treatment, and instrument, Balke and Pearl (1993, 1997) establish that the monotonicity condition of Imbens and Angrist (1994) has no identifying power beyond instrument exogeneity for average potential outcomes and average treatment effects in the sense that adding it to instrument exogeneity does not decrease the identified sets for those parameters whenever those restrictions are consistent with the distribution of the observable data. This paper shows that this phenomenon holds in a broader setting with a multi-valued outcome, treatment, and instrument, under an extension of the monotonicity condition that we refer to as generalized monotonicity. We further show that this phenomenon holds for any restriction on treatment response that is stronger than generalized monotonicity provided that these stronger restrictions do not restrict potential outcomes. Importantly, many models of potential treatments previously considered in the literature imply generalized monotonicity, including the types of monotonicity restrictions considered by Kline and Walters (2016), Kirkeboen et al. (2016), and Heckman and Pinto (2018), and the restriction that treatment selection is determined by particular classes of additive random utility models. We show through a series of examples that restrictions on potential treatments can provide identifying power beyond instrument exogeneity for average potential outcomes and average treatment effects when the restrictions imply that the generalized monotonicity condition is violated. In this way, our results shed light on the types of restrictions required for help in identifying average potential outcomes and average treatment effects.
Exogenous Consideration and Extended Random Utility
Authors: Roy Allen
Date: 2024-05-22 00:00:00
Tasks: counterfactual

In a consideration set model, an individual maximizes utility among the considered alternatives. I relate a consideration set additive random utility model to classic discrete choice and the extended additive random utility model, in which utility can be $-\infty$ for infeasible alternatives. When observable utility shifters are bounded, all three models are observationally equivalent. Moreover, they have the same counterfactual bounds and welfare formulas for changes in utility shifters like price. For attention interventions, welfare cannot change in the full consideration model but is completely unbounded in the limited consideration model. The identified set for consideration set probabilities has a minimal width for any bounded support of shifters, but with unbounded support it is a point: identification "towards" infinity does not resemble identification "at" infinity.
Towards an Optimal Staking Design: Balancing Security, User Growth, and Token Appreciation
Authors: Nicolas Oderbolz, Beatrix Marosvölgyi, Matthias Hafner
Date: 2024-05-23 00:00:00
Tasks: POS

This paper examines the economic and security implications of Proof-of-Stake (POS) designs, providing a survey of POS design choices and their underlying economic principles in prominent POS-blockchains. The paper argues that POS-blockchains are essentially platforms that connect three groups of agents: users, validators, and investors. To meet the needs of these groups, blockchains must balance trade-offs between security, user adoption, and investment into the protocol. We focus on the security aspect and identify two different strategies: increasing the quality of validators (static security) vs. increasing the quantity of stakes (dynamic security). We argue that quality comes at the cost of quantity, identifying a trade-off between the two strategies when designing POS systems. We test our qualitative findings using panel analysis on collected data. The analysis indicates that enhancing the quality of the validator set through security measures like slashing and minimum staking amounts may decrease dynamic security. Further, the analysis reveals a strategic divergence among blockchains, highlighting the absence of a single, universally optimal staking design solution. The optimal design hinges upon a platform's specific objectives and its developmental stage. This research compels blockchain developers to meticulously assess the trade-offs outlined in this paper when developing their staking designs.
Is the EJRA proportionate and therefore justified? A critical review of the EJRA policy at Cambridge
Authors: Oliver Linton, Raghavendra Rau, Patrick Baert, Peter Bossaerts, Jon Crowcroft, G. R. Evans, Paul Ewart, Nick Gay, Paul Kattuman, Stefan Scholtes, Hamid Sabourian, Richard J. Smith
Date: 2024-05-23 00:00:00

This paper critically evaluates the HESA (Higher Education Statistics Agency) Data Report for the Employer Justified Retirement Age (EJRA) Review Group at the University of Cambridge (\cite{CambridgeHESA2024}), identifying significant methodological flaws and misinterpretations. Our analysis reveals issues such as unclear application of data filters, inconsistent variable treatment, and erroneous statistical conclusions. The Report suggests that the EJRA increased job creation rates at Cambridge, but we show Cambridge consistently had lower job creation rates for Established Academic Careers compared to other Russell Group universities, both before and after EJRA implementation in 2011, with no evidence for a significant change in this deficit post implementation. This suggests that EJRA is not a significant factor driving job creation rates. Since other universities without an EJRA exhibit higher job creation rates, this suggests job creation can be sustained without such a policy. We conclude that the EJRA did not achieve its intended goal of increasing opportunities for young academics and may have exacerbated existing disparities compared to other leading universities. We recommend EJRA be abolished at Cambridge since it does not meet its aims and could be viewed as unlawful age discrimination.\newline \it{This version is a revision reflecting some of the comments made by members of the university EJRA review group in public discussion, see \cite{Holmes24}.}
Learning to Import through Production Networks
Authors: Kenan Huremović, Federico Nutarelli, Francesco Serti, Fernando Vega-Redondo
Date: 2024-05-22 00:00:00

Using administrative data on the universe of inter-firm transactions in Spain, we show that firms learn to import from their domestic suppliers and customers. Our identification strategy exploits the panel structure of the data, the firm-time variation across import origins, and the network structure. We find evidence of both upstream and downstream network effects, even after accounting for sectoral and spatial spillovers. We estimate that an increase of 10 percentage points in the share of suppliers (customers) that are importing from a given region increases the probability of starting importing from that region by 10.7\% (19.2\%). Connections with geographically distant domestic firms provide more useful information to start importing. Larger firms are more responsive to this information but less likely to disseminate it.
Monitoring the carbon emissions transition of global building end-use activity
Authors: Xiwang Xiang, Minda Ma
Date: 2024-05-22 00:00:00

The building sector is the largest emitter globally and as such is at the forefront of the net-zero emissions pathway. This study is the first to present a bottom-up assessment framework integrated with the decomposing structural decomposition method to evaluate the emission patterns and decarbonization process of global residential building operations and commercial building operation simultaneously over the last two decades. The results reveal that (1) the average carbon intensity of global commercial building operations has maintained an annual decline of 1.94% since 2000, and emission factors and industrial structures were generally the key to decarbonizing commercial building operations; (2) the operational carbon intensity of global residential buildings has maintained an annual decline of 1.2% over the past two decades, and energy intensity and average household size have been key to this decarbonization; and (3) the total decarbonization of commercial building operations and residential buildings worldwide was 230.28 and 338.1 mega-tons of carbon dioxide per yr, respectively, with a decarbonization efficiency of 10.05% and 9.4%. Overall, this study assesses the global historical progress in decarbonizing global building operations and closes the relevant gap, and it helps plan the stepwise carbon neutral pathway of future global buildings by the mid-century.
The Debt-Inflation Channel of the German (Hyper-)Inflation
Authors: Markus K. Brunnermeier, Sergio Correia, Stephan Luck, Emil Verner, Tom Zimmermann
Date: 2024-05-22 00:00:00

This paper studies how a large increase in the price level is transmitted to the real economy through firm balance sheets. Using newly digitized macro- and micro-level data from the German inflation of 1919-1923, we show that inflation led to a large reduction in real debt burdens and bankruptcies. Firms with higher nominal liabilities at the onset of inflation experienced a larger decline in interest expenses, a relative increase in their equity values, and higher employment during the inflation. The results are consistent with real effects of a debt-inflation channel that operates even when prices and wages are flexible.
Selecting Experimental Sites for External Validity
Authors: Michael Gechter, Keisuke Hirano, Jean Lee, Mahreen Mahmud, Orville Mondal, Jonathan Morduch, Saravana Ravindran, Abu S. Shonchoy
Date: 2024-05-21 00:00:00

Policy decisions often depend on evidence generated elsewhere. We take a Bayesian decision-theoretic approach to choosing where to experiment to optimize external validity. We frame external validity through a policy lens, developing a prior specification for the joint distribution of site-level treatment effects using a microeconometric structural model and allowing for other sources of heterogeneity. With data from South Asia, we show that, relative to basing policies on experiments in optimal sites, large efficiency losses result from instead using evidence from randomly-selected sites or, conversely, from sites with the largest expected treatment effects.
Doing the right thing (or not) in a lemons-like situation: on the role of social preferences and Kantian moral concerns
Authors: Ingela Alger, José Ignacio Rivero-Wildemauwe
Date: 2024-05-21 00:00:00

We conduct a laboratory experiment using framing to assess the willingness to ``sell a lemon'', i.e., to undertake an action that benefits self but hurts the other (the ``buyer''). We seek to disentangle the role of other-regarding preferences and (Kantian) moral concerns, and to test if it matters whether the decision is described in neutral terms or as a market situation. When evaluating an action, morally motivated individuals consider what their own payoff would be if -- hypothetically -- the roles were reversed and the other subject chose the same action (universalization). We vary the salience of role uncertainty, thus varying the ease for participants to envisage the role-reversal scenario.
Evaluating Offshore Electricity Market Design Considering Endogenous Infrastructure Investments: Zonal or Nodal?
Authors: Michiel Kenis, Vladimir Dvorkin, Tim Schittekatte, Kenneth Bruninx, Erik Delarue, Audun Botterud
Date: 2024-05-21 00:00:00

Policy makers are formulating offshore energy infrastructure plans, including wind turbines, electrolyzers, and HVDC transmission lines. An effective market design is crucial to guide cost-efficient investments and dispatch decisions. This paper jointly studies the impact of offshore market design choices on the investment in offshore electrolyzers and HVDC transmission capacity. We present a bilevel model that incorporates investments in offshore energy infrastructure, day-ahead market dispatch, and potential redispatch actions near real-time to ensure transmission constraints are respected. Our findings demonstrate that full nodal pricing, i.e., nodal pricing both onshore and offshore, outperforms the onshore zonal combined with offshore nodal pricing or offshore zonal layouts. While combining onshore zonal with offshore nodal pricing can be considered as a second-best option, it generally diminishes the profitability of offshore wind farms. However, if investment costs of offshore electrolyzers are relatively low, they can serve as catalysts to increase the revenues of the offshore wind farms. This study contributes to the understanding of market designs for highly interconnected offshore power systems, offering insights into the impact of congestion pricing methodologies on investment decisions. Besides, it is useful towards understanding the interaction of offshore loads like electrolyzers with financial support mechanisms for offshore wind farms.
Valores extremos de inflación en Costa Rica
Authors: Daniel Aguilar, Breyner Chacón
Date: 2024-05-16 00:00:00

Maintaining low, non-negative and stable inflation levels is a necessary condition for the stability of the economy as a whole, because the monetary authorities of most industrialized countries, including the Central Bank of Costa Rica since 2005, they have oriented their monetary policy precisely to that task. Still Thus, both in Costa Rica and internationally, most of the statistical modeling of inflation has been limited to modeling their expectancy conditional on different covariates using linear models. This implies a lack of knowledge of the dynamics of the extreme values of the inflation rate and how these are related with other macroeconomic variables. In Costa Rica this is of particular importance since in several periods Negative quarter-on-quarter inflation rates have recently been experienced, which can be problematic if this becomes a recurring phenomenon. Therefore, in this work we propose to answer what is the relationship between the gap of GDP, inflation expectations, imported inflation rate, and the extreme values of the inflation rate in Costa Rica. That is, the main objective is to determine the relationship between the extreme values of the the inflation rate, GDP gap, inflation expectations and imported inflation.
Comparisons of Sequential Experiments for Additively Separable Problems
Authors: Mark Whitmeyer, Cole Williams
Date: 2024-05-22 00:00:00

For three natural classes of dynamic decision problems; 1. additively separable problems, 2. discounted problems, and 3. discounted problems for a fixed discount factor; we provide necessary and sufficient conditions for one sequential experiment to dominate another in the sense that the dominant experiment is preferred to the other for any decision problem in the specified class. We use these results to study the timing of information arrival in additively separable problems.
Continued Pretraining for Domain Adaptation of Wav2vec2.0 in Automatic Speech Recognition for Elementary Math Classroom Settings
Authors: Ahmed Adel Attia, Dorottya Demszky, Tolulope Ogunremi, Jing Liu, Carol Espy-Wilson
Date: 2024-05-15 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Domain Adaptation, Math, speech-recognition, Speech Recognition

Creating Automatic Speech Recognition (ASR) systems that are robust and resilient to classroom conditions is paramount to the development of AI tools to aid teachers and students. In this work, we study the efficacy of continued pretraining (CPT) in adapting Wav2vec2.0 to the classroom domain. We show that CPT is a powerful tool in that regard and reduces the Word Error Rate (WER) of Wav2vec2.0-based models by upwards of 10%. More specifically, CPT improves the model's robustness to different noises, microphones, classroom conditions as well as classroom demographics. Our CPT models show improved ability to generalize to different demographics unseen in the labeled finetuning data.
Physics-informed Score-based Diffusion Model for Limited-angle Reconstruction of Cardiac Computed Tomography
Authors: Shuo Han, Yongshun Xu, Dayang Wang, Bahareh Morovati, Li Zhou, Jonathan S. Maltz, Ge Wang, Hengyong Yu
Date: 2024-05-23 00:00:00
Tasks: Computed Tomography (CT), Diagnostic

Cardiac computed tomography (CT) has emerged as a major imaging modality for the diagnosis and monitoring of cardiovascular diseases. High temporal resolution is essential to ensure diagnostic accuracy. Limited-angle data acquisition can reduce scan time and improve temporal resolution, but typically leads to severe image degradation and motivates for improved reconstruction techniques. In this paper, we propose a novel physics-informed score-based diffusion model (PSDM) for limited-angle reconstruction of cardiac CT. At the sampling time, we combine a data prior from a diffusion model and a model prior obtained via an iterative algorithm and Fourier fusion to further enhance the image quality. Specifically, our approach integrates the primal-dual hybrid gradient (PDHG) algorithm with score-based diffusion models, thereby enabling us to reconstruct high-quality cardiac CT images from limited-angle data. The numerical simulations and real data experiments confirm the effectiveness of our proposed approach.
HemSeg-200: A Voxel-Annotated Dataset for Intracerebral Hemorrhages Segmentation in Brain CT Scans
Authors: Changwei Song, Qing Zhao, Jianqiang Li, Xin Yue, Ruoyun Gao, Zhaoxuan Wang, An Gao, Guanghui Fu
Date: 2024-05-23 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Acute intracerebral hemorrhage is a life-threatening condition that demands immediate medical intervention. Intraparenchymal hemorrhage (IPH) and intraventricular hemorrhage (IVH) are critical subtypes of this condition. Clinically, when such hemorrhages are suspected, immediate CT scanning is essential to assess the extent of the bleeding and to facilitate the formulation of a targeted treatment plan. While current research in deep learning has largely focused on qualitative analyses, such as identifying subtypes of cerebral hemorrhages, there remains a significant gap in quantitative analysis crucial for enhancing clinical treatments. Addressing this gap, our paper introduces a dataset comprising 222 CT annotations, sourced from the RSNA 2019 Brain CT Hemorrhage Challenge and meticulously annotated at the voxel level for precise IPH and IVH segmentation. This dataset was utilized to train and evaluate seven advanced medical image segmentation algorithms, with the goal of refining the accuracy of segmentation for these hemorrhages. Our findings demonstrate that this dataset not only furthers the development of sophisticated segmentation algorithms but also substantially aids scientific research and clinical practice by improving the diagnosis and management of these severe hemorrhages. Our dataset and codes are available at \url{https://github.com/songchangwei/3DCT-SD-IVH-ICH}.