Miracle-dz/UniFAR-FTUs huggingface dataset:
Moral Preferences in Bargaining Games

We report on a large, pre-registered, experiment testing the role of moral preferences in the ultimatum and the impunity games, which vary the veto power of responders. We measure moral preferences in two ways: through a decision problem with real economic consequences (the trade-off game), and through a self-reported scale (the moral foundations questionnaire). Both measures lead to the same results: (i) moral preferences are positively associated with proposers’ offers in the impunity game, but not in the ultimatum game; (ii) moral preferences are positively associated with responders’ minimum acceptable offers in both games. Additional analysis highlights the relevance of the individualizing and the binding moral dimensions in determining offers in the impunity game. Minimum acceptable offers in the ultimatum game are primarily driven by the individualizing moral dimensions, whereas minimum acceptable offers in the impunity game are primarily driven by the binding dimensions.
Gains from Free Trade Agreements: A Theoretical Analysis

Empirical estimates from various studies on impact assessment of free trade agreements show that there are limited economic gains from concluding such arrangements. It has been argued by trade negotiators of many countries that while some partners gain more from an agreement, others gain less or, even suffer from a rise in their current account deficits and overall economic losses. Even the Indian scenario is not an outlier in such a case. This question about unequal gains from an FTA has raised various policy concerns. We attempt to provide an answer to this debate by incorporating the role of the type of commodities that countries trade with each other. In an imperfectly competitive setup with three countries and two types of commodities viz. a final good and an intermediate input, our findings reveal that bilateral free trade in final goods is more welfare-enhancing for the member countries vis-à-vis bilateral free trade in intermediates. However, the former possibility is feasible only for a very small range of parametric values given the pre-requisites for ensuring the formation of an effective FTA. More specifically, we find that a horizontal FTA covering final goods becomes feasible only when the degree of market size asymmetry between the two partners is very less. On the contrary, when we emphasise on the role of vertical trade, i.e., where one of the FTA members exports intermediate inputs to the other, and imports the final good in return, we find that FTA is feasible only when the larger partner is an exporter of final goods and an importer of intermediate inputs, vis-à-vis the smaller partner. In such a case, the larger partner accrues higher gains from such a bilateral engagement. While capturing the role of tradable intermediates, we also show that in the presence of well-connected GVCs, RTAs actually become a less attractive option for enhancing trade and welfare of an economy.
A Factor Model for Cryptocurrency Returns

We investigate the dynamics of daily realised returns and risk premiums for a large cross-section of cryptocurrency pairs through the lens of an Instrumented Principal Component Analysis (IPCA) (see Kelly et al. 2019). We show that a model with three latent factors and time-varying factor loadings significantly outperforms a benchmark model with observable risk factors: the total (predictive) R
The Unintended Benefits of Increased Disclosure Frequency: Evidence from the Brokerage House Industry

Using a plausibly exogenous shock to performance disclosure frequency by listed brokerage houses in China, we examine the impact of disclosure frequency on the quality of information production. Difference-in-difference estimation shows that analysts affiliated with listed brokerage houses exhibit lower forecast optimism in the post-regulation period, compared to their unlisted peers. Cross-sectional analyses suggest that the effect is more pronounced when the listed brokerage houses’ business relies less on their clients, when analysts have greater career concern, and when the covered firms exhibit lower valuation uncertainty/difficulty. Additional analysis indicates that forecast revisions by analysts affiliated with a listed brokerage house are more informative after the shock. Moreover, we find that analysts affiliated with listed brokerage houses issue more accurate and frequent forecasts and cover more companies than their peers do, which suggests that information production improves as a result of increased competition. Overall, our evidence suggests that increased performance disclosure by information intermediaries has positive externalities.
Digital Platforms and the Transactions Cost Approach to Competition Law

In recent years, several competition authorities around the world have announced major investigations into potential anti-competitive behaviour by digital platforms. Not all of that behaviour harms downstream consumers. This has contributed to a growing ‘existential crisis’ at the foundation of competition law: What is the harm that competition law is designed to address? How, exactly, does competition law promote economic welfare? Separately, the literature on ecosystems emphasises the central role of non-generic complementary investments as a defining feature of ecosystems, and the power this can give to ecosystem orchestrators over their associated complementors. We draw these strands together to show how a recently-proposed economic foundation for competition law, drawing on the transactions cost literature, provides a consistent, compelling, and economically-sound foundation for competition policy towards digital platforms going forward.
Would Households Understand Average Inflation Targeting?

Yes, they would. We collect medium- and longer-term inflation expectations from 9,000 individuals in Germany. Unconditionally, the average probability mass is well-centered around the ECB’s inflation aim of 2%. In a randomized control trial, we subject groups of respondents with information about a hypothetical alternative ECB monetary policy regime akin to the Federal Reserve’s flexible average inflation targeting (AIT). Inflation expectations significantly increase for the treated individuals. When provided with additional assumptions about current inflation, individuals update their expected inflation path in line with the central banks’ intentions. Individuals with a high trust in the ECB’s ability to achieve price stability adjust their inflation expectations particularly strongly. We assess the economic significance of our findings by comparing two model economies under different monetary policy strategies, calibrated to match the difference in medium-term inflation expectations from our survey results. Inflation is six times less volatile and the frequency of hitting the lower bound of interest rates substantially reduced under AIT.
Risks and Global Supply Chains: What We Know and What We Need to Know

Recent supply disruptions catapulted the issue of risk in global supply chains (GSCs) to the top of policy agendas and created the impression that shortages would have been less severe if GSCs had been either shorter and more domestic or more diversified. But is this right? We start our answer by reviewing studies that look at risks to and from GSCs and at how GSCs have recovered from past shocks. We then look at whether GSCs are too risky, starting with business research on how firms approach the cost-resilience trade-off. We propose the risk-versus-reward framework from portfolio theory as a good way to evaluate whether anti-risk policy is justified. We then discuss how exposures to foreign shocks are measured and argue that exposure is higher than direct indicators imply. Finally, we consider the future of GSCs in light of current policy proposals and advancing technology before pointing to the rich menu of topics for future research on the risk-GSC nexus. Expected final online publication date for the Annual Review of Economics, Volume 14 is August 2022. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Monetary Policy in a Low Interest Rate Environment: Reversal Rate and Risk-Taking

This paper develops a simple analytical framework to study the impact of central bank policy-rate changes on banks’ credit supply and risk-taking incentives. Unobservable expost bank monitoring of loans creates an external-financing constraint, which determines bank leverage. Unobservable, costly ex-ante screening of borrowers determines the level of bank risk-taking. More risk-taking tightens the external-financing constraint. The policy rate affects the external-financing constraint because it affects both the return on outside investors’ alternative investments and loan rates. In a low rate environment, a policy-rate cut reduces bank funding costs less because of a zero lower bound (ZLB) on retail deposit rates. Bank risk-taking is a necessary but not sufficient for a policy-rate cut to become contractionary ("reversal"). Reversal can occur even though banks’ net-interest margins increase. Credit market competition plays an important role for the interplay of monetary policy and financing stability. When banks have market power, a policy-rate cut can increase lending and still lead to risk-taking. We use our analytical framework to discuss the literature on how monetary policy affects the credit supply of banks, with special emphasis on low and negative rates. JEL Classification: E44, E52, E58, G20, G21
Grasp-Oriented Fine-grained Cloth Segmentation without Real Supervision

Automatically detecting graspable regions from a single depth image is a key ingredient in cloth manipulation. The large variability of cloth deformations has motivated most of the current approaches to focus on identifying specific grasping points rather than semantic parts, as the appearance and depth variations of local regions are smaller and easier to model than the larger ones. However, tasks like cloth folding or assisted dressing require recognizing larger segments, such as semantic edges that carry more information than points. We thus first tackle the problem of fine-grained region detection in deformed clothes using only a depth image. We implement an approach for T-shirts, and define up to 6 semantic regions of varying extent, including edges on the neckline, sleeve cuffs, and hem, plus top and bottom grasping points. We introduce a U-Net based network to segment and label these parts. Our second contribution is concerned with the level of supervision required to train the proposed network. While most approaches learn to detect grasping points by combining real and synthetic annotations, in this work we propose a multilayered Domain Adaptation strategy that does not use any real annotations. We thoroughly evaluate our approach on real depth images of a T-shirt annotated with fine-grained labels, and show that training our network only with synthetic labels and our proposed DA approach yields results competitive with real data supervision.
An Online Scheduling Algorithm for a Community Energy Storage System

In this paper, we consider a community energy storage (CES) system that is shared by various electricity consumers who want to charge and discharge the CES throughout a given time span. We study the problem facing the manager of such a CES who must schedule the charging, discharging, and capacity reservations for numerous users. Moreover, we consider the case where requests to charge/discharge the CES arrive in an online fashion and the CES manager must immediately allocate charging power and energy capacity to fulfill the request or reject the request altogether. The objective of the CES manager is to maximize the total value gained by all of the users of the CES while accounting for the operational constraints of the CES. We discuss an algorithm titled COmmunityEnergyScheduling that acts as a pricing mechanism based on online primal-dual optimization as a solution to the CES manager’s problem. The online algorithm estimates the dual variables (prices) in real-time to allow for requests to be allocated or rejected immediately as they arrive. Furthermore, the proposed method promotes charging and discharging cancellations to reduce the CES’s usage at popular times and is able to handle the inherent stochastic nature of the requests to charge/discharge stemming from randomness in users’ net load patterns and weather uncertainties. Additionally, we are able to show that the algorithm is able to handle any adversarially chosen request sequence and will always yield total welfare within a factor of $\frac {1}{\alpha }$ of the offline optimal welfare.
On compact K\"ahler manifold with strongly pseudo-effective tangent bundle

In the first part of this note, we discuss the compact K\"ahler manifold with a strongly pseudo-effective tangent bundle. In the second part, we give new proof of the fact that the only projective manifolds with the big tangent bundle are the projective spaces. In the third part, we give a characterisation of nef vector bundle.
Inverse Probability Weighting-based Mediation Analysis for Microbiome Data

Mediation analysis is an important tool to study causal associations in biomedical and other scientific areas and has recently gained attention in microbiome studies. Using a microbiome study of acute myeloid leukemia (AML) patients, we investigate whether the effect of induction chemotherapy intensity levels on the infection status is mediated by the microbial taxa abundance. The unique characteristics of the microbial mediators -- high-dimensionality, zero-inflation, and dependence -- call for new methodological developments in mediation analysis. The presence of an exposure-induced mediator-outcome confounder, antibiotic use, further requires a delicate treatment in the analysis. To address these unique challenges in our motivating AML microbiome study, we propose a novel nonparametric identification formula for the interventional indirect effect (IIE), a measure recently developed for studying mediation effects. We develop the corresponding estimation algorithm using the inverse probability weighting method. We also test the presence of mediation effects via constructing the standard normal bootstrap confidence intervals. Simulation studies show that the proposed method has good finite-sample performance in terms of the IIE estimation, and type-I error rate and power of the corresponding test. In the AML microbiome study, our findings suggest that the effect of induction chemotherapy intensity levels on infection is mainly mediated by patients' gut microbiome.
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer

Recently, Transformer has become a prevailing deep architecture for solving vehicle routing problems (VRPs). However, it is less effective in learning improvement models for VRP because its positional encoding (PE) method is not suitable in representing VRP solutions. This paper presents a novel Dual-Aspect Collaborative Transformer (DACT) to learn embeddings for the node and positional features separately, instead of fusing them together as done in existing ones, so as to avoid potential noises and incompatible correlations. Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry of VRP solutions (i.e., cyclic sequences). We train DACT using Proximal Policy Optimization and design a curriculum learning strategy for better sample efficiency. We apply DACT to solve the traveling salesman problem (TSP) and capacitated vehicle routing problem (CVRP). Results show that our DACT outperforms existing Transformer based improvement models, and exhibits much better generalization performance across different problem sizes on synthetic and benchmark instances, respectively.
ABC: Attention with Bounded-memory Control

Transformer architectures have achieved state- of-the-art results on a variety of natural language processing (NLP) tasks. However, their attention mechanism comes with a quadratic complexity in sequence lengths, making the computational overhead prohibitive, especially for long sequences. Attention context can be seen as a random-access memory with each token taking a slot. Under this perspective, the memory size grows linearly with the sequence length, and so does the overhead of reading from it. One way to improve the efficiency is to bound the memory size. We show that disparate approaches can be subsumed into one abstraction, attention with bounded-memory control (ABC), and they vary in their organization of the memory. ABC reveals new, unexplored possibilities. First, it connects several efficient attention variants that would otherwise seem apart. Second, this abstraction gives new insights—an established approach (Wang et al., 2020b) previously thought to not be applicable in causal attention, actually is. Last, we present a new instance of ABC, which draws inspiration from existing ABC approaches, but replaces their heuristic memory-organizing functions with a learned, contextualized one. Our experiments on language modeling, machine translation, and masked language model finetuning show that our approach outperforms previous efficient attention models; compared to the strong transformer baselines, it significantly improves the inference time and space efficiency with no or negligible accuracy loss.
DIGRAC: Digraph Clustering Based on Flow Imbalance

Node clustering is a powerful tool in the analysis of networks. We introduce a graph neural network framework, named DIGRAC, to obtain node embeddings for directed networks in a self-supervised manner, including a novel probabilistic imbalance loss, which can be used for network clustering. Here, we propose \textit{directed flow imbalance} measures, which are tightly related to directionality, to reveal clusters in the network even when there is no density difference between clusters. In contrast to standard approaches in the literature, in this paper, directionality is not treated as a nuisance, but rather contains the main signal. DIGRAC optimizes directed flow imbalance for clustering without requiring label supervision, unlike existing graph neural network methods, and can naturally incorporate node features, unlike existing spectral methods. Extensive experimental results on synthetic data, in the form of directed stochastic block models, and real-world data at different scales, demonstrate that our method, based on flow imbalance, attains state-of-the-art results on directed graph clustering when compared against 10 state-of-the-art methods from the literature, for a wide range of noise and sparsity levels, graph structures, and topologies, and even outperforms supervised methods.
COMPLETENESS OF THE GÖDEL-LÖB PROVABILITY LOGIC FOR THE FILTER SEQUENCE OF NORMAL MEASURES

Assuming the existence of suitable large cardinals, we show it is consistent that the Provability logic $\mathbf{GL}$ is complete with respect to the filter sequence of normal measures. This result answers a question of Andreas Blass from 1990 and a related question of Beklemishev and Joosten.
Diastereoselective Formation of Trans-HC(O)SH through Hydrogenation of OCS on Interstellar Dust Grains

With the presence of evermore complex S-bearing molecules being detected lately, studies of their chemical formation routes need to keep up the pace to rationalize observations, suggest new candidates for detection, and provide input for chemical evolution models. In this paper, we theoretically characterize the hydrogenation channels of OCS on top of amorphous solid water (ASW) as an interstellar dust grain analog in molecular clouds. Our results show that the significant reaction outcome is trans-HC(O)SH, a recently detected prebiotic molecule toward G+0.693. The reaction is diastereoselective, explaining the apparent absence of the cis isomer in astronomical observations. We found that the reaction proceeds through a highly localized radical intermediate (cis-OCSH), which could be essential in the formation of other sulfur-bearing complex organic molecules due to its slow isomerization dynamics on top of ASW.
Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations

Reinforcement learning (RL) is an effective framework for solving sequential decision-making tasks. However, applying RL methods in medical care settings is challenging in part due to heterogeneity in treatment response among patients. Some patients can be treated with standard protocols whereas others, such as those with chronic diseases, need personalized treatment planning. Traditional RL methods often fail to account for this heterogeneity, because they assume that all patients respond to the treatment in the same way (i.e., transition dynamics are shared). We introduce Compositional Fitted $Q$-iteration (CFQI), which uses a compositional task structure to represent heterogeneous treatment responses in medical care settings. A compositional task consists of several variations of the same task, each progressing in difficulty; solving simpler variants of the task can enable efficient solving of harder variants. CFQI uses a compositional $Q$-value function with separate modules for each task variant, allowing it to take advantage of shared knowledge while learning distinct policies for each variant. We validate CFQI's performance using a Cartpole environment and use CFQI to recommend electrolyte repletion for patients with and without renal disease. Our results demonstrate that CFQI is robust even in the presence of class imbalance, enabling effective information usage across patient sub-populations. CFQI exhibits great promise for clinical applications in scenarios characterized by known compositional structures.
Weak Novel Categories without Tears: A Survey on Weak-Shot Learning

—Deep learning is a data-hungry approach, which requires massive training data. However, it is time-consuming and labor-intensive to collect abundant fully-annotated training data for all categories. Assuming the existence of base categories with adequate fully-annotated training samples, different paradigms requiring fewer training samples or weaker annotations for novel categories have attracted growing research interest. Among them, zero-shot ( resp. , few-shot) learning explores using zero ( resp. , a few) training samples for novel categories, which lowers the quantity requirement for novel categories. Instead, weak-shot learning lowers the quality requirement for novel categories. Speciﬁcally, sufﬁcient training samples are collected for novel categories but they only have weak annotations. In different tasks, weak annotations are presented in different forms ( e.g. , noisy labels for image classiﬁcation, image labels for object detection, bounding boxes for segmentation), similar to the deﬁnitions in weakly supervised learning. Therefore, weak-shot learning can also be treated as weakly supervised learning with auxiliary fully supervised categories. In this paper, we discuss the existing weak- shot learning methodologies in different tasks and summarize the codes at https://github.com/bcmi/Awesome-Weak-Shot-Learning.