pwc-archive/papers-with-abstracts huggingface dataset:
Online Multivariate Regularized Distributional Regression for High-dimensional Probabilistic Electricity Price Forecasting
Authors: Simon Hirsch
Date: 2025-04-03 00:00:00
Tasks: regression

Probabilistic electricity price forecasting (PEPF) is a key task for market participants in short-term electricity markets. The increasing availability of high-frequency data and the need for real-time decision-making in energy markets require online estimation methods for efficient model updating. We present an online, multivariate, regularized distributional regression model, allowing for the modeling of all distribution parameters conditional on explanatory variables. Our approach is based on the combination of the multivariate distributional regression and an efficient online learning algorithm based on online coordinate descent for LASSO-type regularization. Additionally, we propose to regularize the estimation along a path of increasingly complex dependence structures of the multivariate distribution, allowing for parsimonious estimation and early stopping. We validate our approach through one of the first forecasting studies focusing on multivariate probabilistic forecasting in the German day-ahead electricity market while using only online estimation methods. We compare our approach to online LASSO-ARX-models with adaptive marginal distribution and to online univariate distributional models combined with an adaptive Copula. We show that the multivariate distributional regression, which allows modeling all distribution parameters - including the mean and the dependence structure - conditional on explanatory variables such as renewable in-feed or past prices provide superior forecasting performance compared to modeling of the marginals only and keeping a static/unconditional dependence structure. Additionally, online estimation yields a speed-up by a factor of 80 to over 400 times compared to batch fitting.
Estimation of the complier causal hazard ratio under dependent censoring
Authors: Gilles Crommen, Jad Beyhum, Ingrid Van Keilegom
Date: 2025-04-02 00:00:00

In this work, we are interested in studying the causal effect of an endogenous binary treatment on a dependently censored duration outcome. By dependent censoring, it is meant that the duration time ($T$) and right censoring time ($C$) are not statistically independent of each other, even after conditioning on the measured covariates. The endogeneity issue is handled by making use of a binary instrumental variable for the treatment. To deal with the dependent censoring problem, it is assumed that on the stratum of compliers: (i) $T$ follows a semiparametric proportional hazards model; (ii) $C$ follows a fully parametric model; and (iii) the relation between $T$ and $C$ is modeled by a parametric copula, such that the association parameter can be left unspecified. In this framework, the treatment effect of interest is the complier causal hazard ratio (CCHR). We devise an estimation procedure that is based on a weighted maximum likelihood approach, where the weights are the probabilities of an observation coming from a complier. The weights are estimated non-parametrically in a first stage, followed by the estimation of the CCHR. Novel conditions under which the model is identifiable are given, a two-step estimation procedure is proposed and some important asymptotic properties are established. Simulations are used to assess the validity and finite-sample performance of the estimation procedure. Finally, we apply the approach to estimate the CCHR of both job training programs on unemployment duration and periodic screening examinations on time until death from breast cancer. The data come from the National Job Training Partnership Act study and the Health Insurance Plan of Greater New York experiment respectively.
How Election Shocks Impact Markets: Evidence from Sectoral Stock Prices
Authors: Aaron J. Amburgey
Date: 2025-04-03 00:00:00

This paper examines the effects of U.S. presidential election cycles on sectoral stock markets. Using a high-frequency identification approach, I construct a novel "election shock'' series, which captures exogenous surprises in election probabilities. Aside from election outcomes, the largest shocks are associated with events that are orthogonal to innovations in the macroeconomy, e.g., scandals and debates. These shocks have immediate effects on asset prices in sectors that are differentially impacted by the policy platforms of the two major U.S. political parties. In particular, shocks favoring Republican (Democratic) candidates increase (decrease) the asset prices in the energy and defense sectors, while decreasing (increasing) prices in the clean energy sector. These effects persist overtime.
Impact of a Blockchain-based Universal Basic Income Pilot: The case of Circles UBI currency
Authors: Alessandro Longo, Teodoro Criscione, Julio Linares, Sowelu Avanzo
Date: 2025-04-03 00:00:00

Circles UBI is a blockchain-based Community Currency System (CCS) that has been active in Berlin (Germany) since October 2021. The Circles Coop, which launched the project in 2021, was shut down in December 2023. In this paper, we show the results of a survey carried out between October and November 2023. The respondents were twenty-five individuals involved in various ways in the Circles' network. The main emerging narrative points out how their participation was deeply motivated by their identification with the values and ideals of the Circles community. Among them, we selected five profiles that stood for their difference in type and degree of involvement. Finally, we report some stories of economic linkages that suggest a positive externality in adopting a local community currency. To our knowledge, this is the first qualitative study of a universal basic income designed as a community currency and adopting blockchain technology. This pilot project was a remarkable experiment for its adopted advanced technological and social innovations. In fact, as far as we know, the integration of basic income and local currency features has been experimented with only in two other cases (Maric\'a, Brazil and Barcelona, Spain) and none of them adopted a decentralized ledger system. In this work, we try to outline strengths and weaknesses that emerged after about two years of activity. For this reason, future researchers and activists interested in this field will find valuable information.
A Combinatorial Auction Design for Formulary Positions
Authors: Lawrence W. Abrams
Date: 2025-04-03 00:00:00
Tasks: Position

The purpose of this paper is to apply the economics field of market design to develop a simple algebraic and graphic model of a combinatorial auction for formulary position assignments.
The Tension between Trust and Oversight in Long-term Relationships
Authors: Peter Achim, Jan Knoepfle
Date: 2025-04-03 00:00:00

A principal continually decides whether to approve resource allocations to an agent, who exerts private effort to remain eligible. The principal must perform costly inspections to determine the agent's eligibility. We characterize Markov Perfect Equilibria and analyze the paths of trust and oversight that emerge from the dynamic interplay of effort and oversight. At high trust levels, effort is an intertemporal substitute to oversight, which leads to unique interior effort choices and random inspections. At low trust levels, effort is an intertemporal complement to oversight, which may create a coordination problem, leading to equilibrium multiplicity. Voluntary disclosure can mitigate this coordination issue.
Imprecision Attenuates Updating
Authors: Martin Vaeth
Date: 2025-04-03 00:00:00

This paper studies how imprecision in noisy signals attenuates Bayesian updating toward the prior. This phenomenon is well-known under a normal prior and normal noise, where less precise signals yield posterior means closer to the prior mean. We show this effect extends to any symmetric, log-concave prior and any symmetric, quasi-concave location experiment, using a newly introduced precision order. Our main result is that for any such prior and any signal realization, the posterior mean under location experiment S is closer to the prior mean than is the posterior mean under S', if and only if S is less precise than S'. We discuss applications to cognitive imprecision, prior precision, and overconfidence.
Latency Advantages in Common-Value Auctions
Authors: Ciamac C. Moallemi, Mallesh M. Pai, Dan Robinson
Date: 2025-04-02 00:00:00

In financial applications, latency advantages - the ability to make decisions later than others, even without the ability to see what others have done - can provide individual participants with an edge by allowing them to gather additional relevant information. For example, a trader who is able to act even milliseconds after another trader may receive information about changing prices on other exchanges that lets them make a profit at the expense of the latter. To better understand the economics of latency advantages, we consider a common-value auction with a reserve price in which some bidders may have more information about the value of the item than others, e.g., by bidding later. We provide a characterization of the equilibrium strategies and study the welfare and auctioneer revenue implications of the last-mover advantage. We show that the auction does not degenerate completely and that the seller is still able to capture some value. We study comparative statics of the equilibrium under different assumptions about the nature of the latency advantage. Under the assumptions of the Black-Scholes model, we derive formulas for the last mover's expected profit, as well as for the sensitivity of that profit to their timing advantage. We apply our results to the design of blockchain protocols that aim to run auctions for financial assets on-chain, where incentives to increase timing advantages can put pressure on the decentralization of the system.
VoiceCraft-Dub: Automated Video Dubbing with Neural Codec Language Models
Authors: Kim Sung-Bin, Jeongsoo Choi, Puyuan Peng, Joon Son Chung, Tae-Hyun Oh, David Harwath
Date: 2025-04-03 00:00:00
Tasks: Speech Synthesis

We present VoiceCraft-Dub, a novel approach for automated video dubbing that synthesizes high-quality speech from text and facial cues. This task has broad applications in filmmaking, multimedia creation, and assisting voice-impaired individuals. Building on the success of Neural Codec Language Models (NCLMs) for speech synthesis, our method extends their capabilities by incorporating video features, ensuring that synthesized speech is time-synchronized and expressively aligned with facial movements while preserving natural prosody. To inject visual cues, we design adapters to align facial features with the NCLM token space and introduce audio-visual fusion layers to merge audio-visual information within the NCLM framework. Additionally, we curate CelebV-Dub, a new dataset of expressive, real-world videos specifically designed for automated video dubbing. Extensive experiments show that our model achieves high-quality, intelligible, and natural speech synthesis with accurate lip synchronization, outperforming existing methods in human perception and performing favorably in objective evaluations. We also adapt VoiceCraft-Dub for the video-to-speech task, demonstrating its versatility for various applications.
STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection
Authors: Divya Velayudhan, Abdelfatah Ahmed, Mohamad Alansari, Neha Gour, Abderaouf Behouch, Taimur Hassan, Syed Talal Wasim, Nabil Maalej, Muzammal Naseer, Juergen Gall, Mohammed Bennamoun, Ernesto Damiani, Naoufel Werghi
Date: 2025-04-03 00:00:00
Proceeding: CVPR 2025 1
Tasks: Instruction Following, Language Modeling, Language Modelling, Question Answering, Visual Grounding, Visual Question Answering, Visual Question Answering (VQA)

Advancements in Computer-Aided Screening (CAS) systems are essential for improving the detection of security threats in X-ray baggage scans. However, current datasets are limited in representing real-world, sophisticated threats and concealment tactics, and existing approaches are constrained by a closed-set paradigm with predefined labels. To address these challenges, we introduce STCray, the first multimodal X-ray baggage security dataset, comprising 46,642 image-caption paired scans across 21 threat categories, generated using an X-ray scanner for airport security. STCray is meticulously developed with our specialized protocol that ensures domain-aware, coherent captions, that lead to the multi-modal instruction following data in X-ray baggage security. This allows us to train a domain-aware visual AI assistant named STING-BEE that supports a range of vision-language tasks, including scene comprehension, referring threat localization, visual grounding, and visual question answering (VQA), establishing novel baselines for multi-modal learning in X-ray baggage security. Further, STING-BEE shows state-of-the-art generalization in cross-domain settings. Code, data, and models are available at https://divs1159.github.io/STING-BEE/.
GMR-Conv: An Efficient Rotation and Reflection Equivariant Convolution Kernel Using Gaussian Mixture Rings
Authors: Yuexi Du, Jiazhen Zhang, Nicha C. Dvornek, John A. Onofrey
Date: 2025-04-03 00:00:00

Symmetry, where certain features remain invariant under geometric transformations, can often serve as a powerful prior in designing convolutional neural networks (CNNs). While conventional CNNs inherently support translational equivariance, extending this property to rotation and reflection has proven challenging, often forcing a compromise between equivariance, efficiency, and information loss. In this work, we introduce Gaussian Mixture Ring Convolution (GMR-Conv), an efficient convolution kernel that smooths radial symmetry using a mixture of Gaussian-weighted rings. This design mitigates discretization errors of circular kernels, thereby preserving robust rotation and reflection equivariance without incurring computational overhead. We further optimize both the space and speed efficiency of GMR-Conv via a novel parameterization and computation strategy, allowing larger kernels at an acceptable cost. Extensive experiments on eight classification and one segmentation datasets demonstrate that GMR-Conv not only matches conventional CNNs' performance but can also surpass it in applications with orientation-less data. GMR-Conv is also proven to be more robust and efficient than the state-of-the-art equivariant learning methods. Our work provides inspiring empirical evidence that carefully applied radial symmetry can alleviate the challenges of information loss, marking a promising advance in equivariant network architectures. The code is available at https://github.com/XYPB/GMR-Conv.
Learning Phase Distortion with Selective State Space Models for Video Turbulence Mitigation
Authors: Xingguang Zhang, Nicholas Chimitt, Xijun Wang, Yu Yuan, Stanley H. Chan
Date: 2025-04-03 00:00:00
Proceeding: CVPR 2025 1
Tasks: State Space Models

Atmospheric turbulence is a major source of image degradation in long-range imaging systems. Although numerous deep learning-based turbulence mitigation (TM) methods have been proposed, many are slow, memory-hungry, and do not generalize well. In the spatial domain, methods based on convolutional operators have a limited receptive field, so they cannot handle a large spatial dependency required by turbulence. In the temporal domain, methods relying on self-attention can, in theory, leverage the lucky effects of turbulence, but their quadratic complexity makes it difficult to scale to many frames. Traditional recurrent aggregation methods face parallelization challenges. In this paper, we present a new TM method based on two concepts: (1) A turbulence mitigation network based on the Selective State Space Model (MambaTM). MambaTM provides a global receptive field in each layer across spatial and temporal dimensions while maintaining linear computational complexity. (2) Learned Latent Phase Distortion (LPD). LPD guides the state space model. Unlike classical Zernike-based representations of phase distortion, the new LPD map uniquely captures the actual effects of turbulence, significantly improving the model's capability to estimate degradation by reducing the ill-posedness. Our proposed method exceeds current state-of-the-art networks on various synthetic and real-world TM benchmarks with significantly faster inference speed.
Two-Stage nnU-Net for Automatic Multi-class Bi-Atrial Segmentation from LGE-MRIs
Authors: Y. On, C. Galazis, C. Chiu, M. Varela
Date: 2025-04-03 00:00:00

Late gadolinium enhancement magnetic resonance imaging (LGE-MRI) is used to visualise atrial fibrosis and scars, providing important information for personalised atrial fibrillation (AF) treatments. Since manual analysis and delineations of these images can be both labour-intensive and subject to variability, we develop an automatic pipeline to perform segmentation of the left atrial (LA) cavity, the right atrial (RA) cavity, and the wall of both atria on LGE-MRI. Our method is based on a two-stage nnU-Net architecture, combining 2D and 3D convolutional networks, and incorporates adaptive histogram equalisation to improve tissue contrast in the input images and morphological operations on the output segmentation maps. We achieve Dice similarity coefficients of 0.92 +/- 0.03, 0.93 +/- 0.03, 0.71 +/- 0.05 and 95% Hausdorff distances of (3.89 +/- 6.67) mm, (4.42 +/- 1.66) mm and (3.94 +/- 1.83) mm for LA, RA, and wall, respectively. The accurate delineation of the LA, RA and the myocardial wall is the first step in analysing atrial structure in cardiovascular patients, especially those with AF. This can allow clinicians to provide adequate and personalised treatment plans in a timely manner.
Adaptive Frequency Enhancement Network for Remote Sensing Image Semantic Segmentation
Authors: Feng Gao, Miao Fu, Jingchao Cao, Junyu Dong, Qian Du
Date: 2025-04-03 00:00:00
Tasks: Semantic Segmentation

Semantic segmentation of high-resolution remote sensing images plays a crucial role in land-use monitoring and urban planning. Recent remarkable progress in deep learning-based methods makes it possible to generate satisfactory segmentation results. However, existing methods still face challenges in adapting network parameters to various land cover distributions and enhancing the interaction between spatial and frequency domain features. To address these challenges, we propose the Adaptive Frequency Enhancement Network (AFENet), which integrates two key components: the Adaptive Frequency and Spatial feature Interaction Module (AFSIM) and the Selective feature Fusion Module (SFM). AFSIM dynamically separates and modulates high- and low-frequency features according to the content of the input image. It adaptively generates two masks to separate high- and low-frequency components, therefore providing optimal details and contextual supplementary information for ground object feature representation. SFM selectively fuses global context and local detailed features to enhance the network's representation capability. Hence, the interactions between frequency and spatial features are further enhanced. Extensive experiments on three publicly available datasets demonstrate that the proposed AFENet outperforms state-of-the-art methods. In addition, we also validate the effectiveness of AFSIM and SFM in managing diverse land cover types and complex scenarios. Our codes are available at https://github.com/oucailab/AFENet.
Towards Computation- and Communication-efficient Computational Pathology
Authors: Chu Han, Bingchao Zhao, Jiatai Lin, Shanshan Lyu, Longfei Wang, Tianpeng Deng, Cheng Lu, Changhong Liang, Hannah Y. Wen, Xiaojing Guo, Zhenwei Shi, Zaiyi Liu
Date: 2025-04-03 00:00:00
Tasks: Diagnostic, Self-Supervised Learning

Despite the impressive performance across a wide range of applications, current computational pathology models face significant diagnostic efficiency challenges due to their reliance on high-magnification whole-slide image analysis. This limitation severely compromises their clinical utility, especially in time-sensitive diagnostic scenarios and situations requiring efficient data transfer. To address these issues, we present a novel computation- and communication-efficient framework called Magnification-Aligned Global-Local Transformer (MAG-GLTrans). Our approach significantly reduces computational time, file transfer requirements, and storage overhead by enabling effective analysis using low-magnification inputs rather than high-magnification ones. The key innovation lies in our proposed magnification alignment (MAG) mechanism, which employs self-supervised learning to bridge the information gap between low and high magnification levels by effectively aligning their feature representations. Through extensive evaluation across various fundamental CPath tasks, MAG-GLTrans demonstrates state-of-the-art classification performance while achieving remarkable efficiency gains: up to 10.7 times reduction in computational time and over 20 times reduction in file transfer and storage requirements. Furthermore, we highlight the versatility of our MAG framework through two significant extensions: (1) its applicability as a feature extractor to enhance the efficiency of any CPath architecture, and (2) its compatibility with existing foundation models and histopathology-specific encoders, enabling them to process low-magnification inputs with minimal information loss. These advancements position MAG-GLTrans as a particularly promising solution for time-sensitive applications, especially in the context of intraoperative frozen section diagnosis where both accuracy and efficiency are paramount.
Bridging the Gap between Gaussian Diffusion Models and Universal Quantization for Image Compression
Authors: Lucas Relic, Roberto Azevedo, Yang Zhang, Markus Gross, Christopher Schroers
Date: 2025-04-03 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Compression, Quantization

Generative neural image compression supports data representation at extremely low bitrate, synthesizing details at the client and consistently producing highly realistic images. By leveraging the similarities between quantization error and additive noise, diffusion-based generative image compression codecs can be built using a latent diffusion model to "denoise" the artifacts introduced by quantization. However, we identify three critical gaps in previous approaches following this paradigm (namely, the noise level, noise type, and discretization gaps) that result in the quantized data falling out of the data distribution known by the diffusion model. In this work, we propose a novel quantization-based forward diffusion process with theoretical foundations that tackles all three aforementioned gaps. We achieve this through universal quantization with a carefully tailored quantization schedule and a diffusion model trained with uniform noise. Compared to previous work, our proposal produces consistently realistic and detailed reconstructions, even at very low bitrates. In such a regime, we achieve the best rate-distortion-realism performance, outperforming previous related works.