pwc-archive/papers-with-abstracts huggingface dataset:
Identification with possibly invalid IVs
Authors: Christophe Bruneel-Zupanc, Jad Beyhum
Date: 2024-01-08 00:00:00
Tasks: Additive models, valid

This paper proposes a novel identification strategy relying on quasi-instrumental variables (quasi-IVs). A quasi-IV is a relevant but possibly invalid IV because it is not exogenous or not excluded. We show that a variety of models with discrete or continuous endogenous treatment which are usually identified with an IV - quantile models with rank invariance, additive models with homogenous treatment effects, and local average treatment effect models - can be identified under the joint relevance of two complementary quasi-IVs instead. To achieve identification, we complement one excluded but possibly endogenous quasi-IV (e.g., "relevant proxies" such as lagged treatment choice) with one exogenous (conditional on the excluded quasi-IV) but possibly included quasi-IV (e.g., random assignment or exogenous market shocks). Our approach also holds if any of the two quasi-IVs turns out to be a valid IV. In practice, being able to address endogeneity with complementary quasi-IVs instead of IVs is convenient since there are many applications where quasi-IVs are more readily available. Difference-in-differences is a notable example: time is an exogenous quasi-IV while the group assignment acts as a complementary excluded quasi-IV.
Counterfactuals in factor models
Authors: Jad Beyhum
Date: 2024-01-06 00:00:00
Tasks: Time Series

We study a new model where the potential outcomes, corresponding to the values of a (possibly continuous) treatment, are linked through common factors. The factors can be estimated using a panel of regressors. We propose a procedure to estimate time-specific and unit-specific average marginal effects in this context. Our approach can be used either with high-dimensional time series or with large panels. It allows for treatment effects heterogenous across time and units and is straightforward to implement since it only relies on principal components analysis and elementary computations. We derive the asymptotic distribution of our estimator of the average marginal effect and highlight its solid finite sample performance through a simulation exercise. The approach can also be used to estimate average counterfactuals or adapted to an instrumental variables setting and we discuss these extensions. Finally, we illustrate our novel methodology through an empirical application on income inequality.
Income inequality and the oil resource curse
Authors: Osiris Jorge Parcero, Elissaios Papyrakis
Date: 2024-01-08 00:00:00

Surprisingly, there has been little research conducted about the cross-country relationship between oil dependence/abundance and income inequality. At the same time, there is some tentative evidence suggesting that oil rich nations tend to under-report data on income inequality, which can potentially influence the estimated empirical relationships between oil richness and income inequality. In this paper we contribute to the literature in a twofold manner. First, we explore in depth the empirical relationship between oil and income inequality by making use of the Standardized World Income Inequality Database; the most comprehensive dataset on income inequality providing comparable data for the broadest set of country-year observations. Second, this is the first study to our knowledge that adopts an empirical framework to examine whether oil rich nations tend to under-report data on income inequality and the possible implications thereof. We make use of Heckman selection models to validate the tendency of oil rich countries to under-report and correct for the bias that might arise as a result of this; we find that oil is associated with lower income inequality with the exception of the very oil-rich economies.
Saving for sunny days: The impact of climate (change) on consumer prices in the euro area
Authors: Paulo M. M. Rodrigues, Mirjam Salish, Nazarii Salish
Date: 2024-01-08 00:00:00
Tasks: Time Series

Climate (change) affects the prices of goods and services in different countries or regions differently. Simply relying on aggregate measures or summary statistics, such as the impact of average country temperature changes on HICP headline inflation, conceals a large heterogeneity across (sub-)sectors of the economy. Additionally, the impact of a weather anomaly on consumer prices depends not only on its sign and magnitude, but also on its location and the size of the area affected by the shock. This is especially true for larger countries or regions with diverse climate zones, since the geographical distribution of climatic effects plays a role in shaping economic outcomes. Using time series data of geolocations, we demonstrate that relying solely on country averages fails to adequately capture and explain the influence of weather on consumer prices in the euro area. We conclude that the information content hidden in rich and complex surface data can provide valuable insights into the role of weather and climate variables for price stability, and more generally may help to inform economic policy.
Spiritual Intelligence's Role in Reducing Technostress through Ethical Work Climates
Authors: Saleh Ghobbeh, Armita Atrian
Date: 2024-01-08 00:00:00
Tasks: Ethics

This study explores the impact of spiritual intelligence (SI) on technostress, with a focus on the mediating role of the ethical environment. In an era where technological advancements continually reshape our work and personal lives, understanding the interplay between human intelligence, well-being, and ethics within organizations is increasingly significant. Spiritual intelligence, transcending traditional cognitive and emotional intelligences, emphasizes understanding personal meaning and values. This paper investigates how higher levels of SI enable individuals to integrate technology into their lives without undue stress, and how a robust ethical environment within organizations supports and amplifies these benefits. Through a comprehensive review of literature, empirical research, and detailed analysis, the study highlights the protective role of SI against technostress and the significant influence of an ethical climate in enhancing this effect. The findings offer valuable insights for organizational strategies aimed at promoting a harmonious, stress-free workplace environment.
The Effects of COVID-19 and the Russia-Ukraine War on Inward Foreign Direct Investment
Authors: MS Hosen, SM Hossain, MN Mia, MR Chowdhury
Date: 2024-01-06 00:00:00

Inward Foreign Direct Investment (IFDI) into Europe and Asian developing countries like Bangladesh is experimentally examined in this study. IFDI in emerging markets has been boosted by global investment and inflow influenced by resource availability and public policy. The economic policy uncertainty on IFDI in 13 countries is explored at a time when the crisis between Russia and Ukraine war is having a global impact. Microeconomic factors affected Gross Domestic Product (GDP) growth, inflation, interest rates, and the currency rate fluctuated with IFDI, which mostly shocked during COVID-19 and the Russia-Ukraine war. With data from the World Bank and the United Nations Conference on Trade and Development (UNCTAD) database, we compile a panel dataset covering 2018-2022. The researchers used a mixture of panel and linear regression analysis using a random effect model. Our findings show that the impact of global rates hurts IFDI in 13 selected countries. There is a correlation between a country's ability to enforce contracts and the amount of Inward FDI it receives. Using the top 13 hosts of incoming FDI flows COVID-19 and Russia-Ukraine wartime series analysis gives valuable information for policymakers in the remaining countries chosen to attract IFDI inflows.
Anonymous and Strategy-Proof Voting under Subjective Expected Utility Preferences
Authors: Eric Bahel
Date: 2024-01-08 00:00:00

We study three axioms in the model of constrained social choice under uncertainty where (i) agents have subjective expected utility preferences over acts and (ii) different states of nature have (possibly) different sets of available outcomes. Anonymity says that agents' names or labels should never play a role in the mechanism used to select the social act. Strategy-proofness requires that reporting one's true preferences be a (weakly) dominant strategy for each agent in the associated direct revelation game. Range unanimity essentially says that a feasible act must be selected by society whenever it is reported as every voter's favorite act within the range of the mechanism. We first show that every social choice function satisfying these three axioms can be factored as a product of voting rules that are either constant or binary (always yielding one of two pre-specified outcomes in each state). We describe four basic types of binary factors: three of these types are novel to this literature and exploit the voters' subjective beliefs. Our characterization result then states that a social choice function is anonymous, strategy-proof and range-unanimous if and only if every binary factor (in its canonical factorization) is of one of these four basic types.
The Priced Survey Methodology: Theory
Authors: Avner Seror
Date: 2024-01-08 00:00:00
Tasks: Attribute, Survey

In this paper, I introduce the Priced Survey Methodology (PSM), a tool designed to overcome the limitations of traditional survey methods in analyzing social preferences. The PSM's design draws inspiration from consumption choice experiments, as respondents fill out the same survey multiple times under different choice sets. I generalize Afriat's theorem and show that the Generalized Axiom of Revealed Preferences is necessary and sufficient for the existence of a concave, continuous, and single-peaked utility function rationalizing answers to the PSM. This result has two major implications. First, it is possible to measure a respondent's ideal answer to a survey using only ordinal relations between possible answers. Second, the PSM captures aspects of social preferences often overlooked in standard surveys, such as the relative importance that respondents attribute to different survey questions. I deploy a PSM measuring altruistic preferences in a sample of online participants, recover respondents' single-peaked preferences, and draw several implications.
Learning about a changing state
Authors: Benjamin Davies
Date: 2024-01-07 00:00:00
Tasks: Gaussian Processes, Informativeness

A long-lived Bayesian agent observes costly signals of a time-varying state. He chooses the signals' precisions sequentially, balancing their costs and marginal informativeness. I compare the optimal myopic and forward-looking precisions when the state follows a Brownian motion. I also compare the myopic precisions induced by other Gaussian processes.
Incontestable Assignments
Authors: Benoit Decerf, Guillaume Haeringer, Martin Van der Linden
Date: 2024-01-07 00:00:00
Tasks: valid

In school districts where assignments are exclusively determined by a clearinghouse students can only appeal their assignment with a valid reason. An assignment is incontestable if it is appeal-proof. We study incontestability when students do not observe the other students' preferences and assignments. Incontestability is shown to be equivalent to individual rationality, non-wastefulness, and respect for top-priority sets (a weakening of justified envy). Stable mechanisms and those Pareto dominating them are incontestable, as well as the Top-Trading Cycle mechanism (but Boston is not). Under a mild consistency property, incontestable mechanisms are i-indinstiguishable (Li, 2017), and share similar incentive properties.
Optimal risk sharing, equilibria, and welfare with empirically realistic risk attitudes
Authors: Jean-Gabriel Lauzier, Liyuan Lin, Peter Wakker, Ruodu Wang
Date: 2024-01-06 00:00:00

This paper examines optimal risk sharing for empirically realistic risk attitudes, providing results on Pareto optimality, competitive equilibria, utility frontiers, and the first and second theorems of welfare. Empirical studies suggest, contrary to classical assumptions, that risk seeking is prevalent in particular subdomains, and is even the majority finding for losses, underlying for instance the disposition effect. We first analyze cases of expected utility agents, some of whom may be risk seeking. Yet more empirical realism is obtained by allowing agents to be risk averse in some subdomains but risk seeking in others, which requires generalizing expected utility. Here we provide first results, pleading for future research. Our main new tool for analyzing generalized risk attitudes is a counter-monotonic improvement theorem.
Geodesic interpolation of frame-wise speaker embeddings for the diarization of meeting scenarios
Authors: Tobias Cord-Landwehr, Christoph Boeddeker, Cătălin Zorilă, Rama Doddipatla, Reinhold Haeb-Umbach
Date: 2024-01-08 00:00:00
Tasks: Clustering

We propose a modified teacher-student training for the extraction of frame-wise speaker embeddings that allows for an effective diarization of meeting scenarios containing partially overlapping speech. To this end, a geodesic distance loss is used that enforces the embeddings computed from regions with two active speakers to lie on the shortest path on a sphere between the points given by the d-vectors of each of the active speakers. Using those frame-wise speaker embeddings in clustering-based diarization outperforms segment-level clustering-based diarization systems such as VBx and Spectral Clustering. By extending our approach to a mixture-model-based diarization, the performance can be further improved, approaching the diarization error rates of diarization systems that use a dedicated overlap detection, and outperforming these systems when also employing an additional overlap detection.
A Video Coding Method Based on Neural Network for CLIC2024
Authors: Zhengang Li, Jingchi Zhang, Yonghua Wang, Xing Zeng, Zhen Zhang, Yunlin Long, Menghu Jia, Ning Wang
Date: 2024-01-08 00:00:00
Tasks: Deep Learning, Quantization

This paper presents a video coding scheme that combines traditional optimization methods with deep learning methods based on the Enhanced Compression Model (ECM). In this paper, the traditional optimization methods adaptively adjust the quantization parameter (QP). The key frame QP offset is set according to the video content characteristics, and the coding tree unit (CTU) level QP of all frames is also adjusted according to the spatial-temporal perception information. Block importance mapping technology (BIM) is also introduced, which adjusts the QP according to the block importance. Meanwhile, the deep learning methods propose a convolutional neural network-based loop filter (CNNLF), which is turned on/off based on the rate-distortion optimization at the CTU and frame level. Besides, intra-prediction using neural networks (NN-intra) is proposed to further improve compression quality, where 8 neural networks are used for predicting blocks of different sizes. The experimental results show that compared with ECM-3.0, the proposed traditional methods and adding deep learning methods improve the PSNR by 0.54 dB and 1 dB at 0.05Mbps, respectively; 0.38 dB and 0.71dB at 0.5 Mbps, respectively, which proves the superiority of our method.
O-PRESS: Boosting OCT axial resolution with Prior guidance, Recurrence, and Equivariant Self-Supervision
Authors: Kaiyan Li, Jingyuan Yang, Wenxuan Liang, Xingde Li, Chenxi Zhang, Lulu Chen, Chan Wu, Xiao Zhang, Zhiyan Xu, Yuelin Wang, Lihui Meng, Yue Zhang, Youxin Chen, S. Kevin Zhou
Date: 2024-01-06 00:00:00

Optical coherence tomography (OCT) is a noninvasive technology that enables real-time imaging of tissue microanatomies. The axial resolution of OCT is intrinsically constrained by the spectral bandwidth of the employed light source while maintaining a fixed center wavelength for a specific application. Physically extending this bandwidth faces strong limitations and requires a substantial cost. We present a novel computational approach, called as O-PRESS, for boosting the axial resolution of OCT with Prior Guidance, a Recurrent mechanism, and Equivariant Self-Supervision. Diverging from conventional superresolution methods that rely on physical models or data-driven techniques, our method seamlessly integrates OCT modeling and deep learning, enabling us to achieve real-time axial-resolution enhancement exclusively from measurements without a need for paired images. Our approach solves two primary tasks of resolution enhancement and noise reduction with one treatment. Both tasks are executed in a self-supervised manner, with equivariance imaging and free space priors guiding their respective processes. Experimental evaluations, encompassing both quantitative metrics and visual assessments, consistently verify the efficacy and superiority of our approach, which exhibits performance on par with fully supervised methods. Importantly, the robustness of our model is affirmed, showcasing its dual capability to enhance axial resolution while concurrently improving the signal-to-noise ratio.
Prompt-driven Latent Domain Generalization for Medical Image Classification
Authors: Siyuan Yan, Chi Liu, Zhen Yu, Lie Ju, Dwarikanath Mahapatra, Brigid Betz-Stablein, Victoria Mar, Monika Janda, Peter Soyer, ZongYuan Ge
Date: 2024-01-05 00:00:00
Tasks: Domain Generalization, image-classification, Image Classification, Medical Image Analysis, Medical Image Classification, Prompt Learning

Deep learning models for medical image analysis easily suffer from distribution shifts caused by dataset artifacts bias, camera variations, differences in the imaging station, etc., leading to unreliable diagnoses in real-world clinical settings. Domain generalization (DG) methods, which aim to train models on multiple domains to perform well on unseen domains, offer a promising direction to solve the problem. However, existing DG methods assume domain labels of each image are available and accurate, which is typically feasible for only a limited number of medical datasets. To address these challenges, we propose a novel DG framework for medical image classification without relying on domain labels, called Prompt-driven Latent Domain Generalization (PLDG). PLDG consists of unsupervised domain discovery and prompt learning. This framework first discovers pseudo domain labels by clustering the bias-associated style features, then leverages collaborative domain prompts to guide a Vision Transformer to learn knowledge from discovered diverse domains. To facilitate cross-domain knowledge learning between different prompts, we introduce a domain prompt generator that enables knowledge sharing between domain prompts and a shared prompt. A domain mixup strategy is additionally employed for more flexible decision margins and mitigates the risk of incorrect domain assignments. Extensive experiments on three medical image classification tasks and one debiasing task demonstrate that our method can achieve comparable or even superior performance than conventional DG algorithms without relying on domain labels. Our code will be publicly available upon the paper is accepted.
Hyperspectral Image Denoising via Spatial-Spectral Recurrent Transformer
Authors: Guanyiman Fu, Fengchao Xiong, Jianfeng Lu, Jun Zhou, Jiantao Zhou, Yuntao Qian
Date: 2023-12-31 00:00:00
Tasks: Denoising, Hyperspectral Image Denoising, Image Denoising

Hyperspectral images (HSIs) often suffer from noise arising from both intra-imaging mechanisms and environmental factors. Leveraging domain knowledge specific to HSIs, such as global spectral correlation (GSC) and non-local spatial self-similarity (NSS), is crucial for effective denoising. Existing methods tend to independently utilize each of these knowledge components with multiple blocks, overlooking the inherent 3D nature of HSIs where domain knowledge is strongly interlinked, resulting in suboptimal performance. To address this challenge, this paper introduces a spatial-spectral recurrent transformer U-Net (SSRT-UNet) for HSI denoising. The proposed SSRT-UNet integrates NSS and GSC properties within a single SSRT block. This block consists of a spatial branch and a spectral branch. The spectral branch employs a combination of transformer and recurrent neural network to perform recurrent computations across bands, allowing for GSC exploitation beyond a fixed number of bands. Concurrently, the spatial branch encodes NSS for each band by sharing keys and values with the spectral branch under the guidance of GSC. This interaction between the two branches enables the joint utilization of NSS and GSC, avoiding their independent treatment. Experimental results demonstrate that our method outperforms several alternative approaches. The source code will be available at https://github.com/lronkitty/SSRT.
A Practical Beamforming Design for Active RIS-assisted MU-MISO Systems
Authors: Yun Yang, Zhiping Lu, Ming Li, Rang Liu, Qian Liu
Date: 2024-01-08 00:00:00

Reconfigurable Intelligent Surfaces (RIS) have been proposed as a revolutionary technology with the potential to address several critical requirements of 6G communication systems. Despite its powerful ability for radio environment reconfiguration, the ``double fading'' effect constricts the practical system performance enhancements due to the significant path loss. A new active RIS architecture has been recently proposed to overcome this challenge. However, existing active RIS studies rely on an ideal amplification model without considering the practical hardware limitation of amplifiers, which may cause performance degradation using such inaccurate active RIS modeling. Motivated by this fact, in this paper we first investigate the amplification principle of typical active RIS and propose a more accurate amplification model based on amplifier hardware characteristics. Then, based on the new amplification model, we propose a novel joint transmit beamforming and RIS reflection beamforming design considering the incident signal power on practical active RIS for multiuser multi-input single-output (MU-MISO) communication system. Fractional programming (FP), majorization minimization (MM) and block coordinate descent (BCD) methods are used to solve for the complex problem. Simulation results indicate the importance of the consideration of practical amplifier hardware characteristics in the joint beamforming designs and demonstrate the effectiveness of the proposed algorithm compared to other benchmarks.
Hybrid Vector Message Passing for Generalized Bilinear Factorization
Authors: Hao Jiang, Xiaojun Yuan, Qinghua Guo
Date: 2024-01-08 00:00:00

In this paper, we propose a new message passing algorithm that utilizes hybrid vector message passing (HVMP) to solve the generalized bilinear factorization (GBF) problem. The proposed GBF-HVMP algorithm integrates expectation propagation (EP) and variational message passing (VMP) via variational free energy minimization, yielding tractable Gaussian messages. Furthermore, GBF-HVMP enables vector/matrix variables rather than scalar ones in message passing, resulting in a loop-free Bayesian network that improves convergence. Numerical results show that GBF-HVMP significantly outperforms state-of-the-art methods in terms of NMSE performance and computational complexity.