pwc-archive/papers-with-abstracts huggingface dataset:
Langevin Diffusion Approximation to Same Marginal Schrödinger Bridge
Authors: Medha Agarwal, Zaid Harchaoui, Garrett Mulcahy, Soumik Pal
Date: 2025-05-12 00:00:00

We introduce a novel approximation to the same marginal Schr\"{o}dinger bridge using the Langevin diffusion. As $\varepsilon \downarrow 0$, it is known that the barycentric projection (also known as the entropic Brenier map) of the Schr\"{o}dinger bridge converges to the Brenier map, which is the identity. Our diffusion approximation is leveraged to show that, under suitable assumptions, the difference between the two is $\varepsilon$ times the gradient of the marginal log density (i.e., the score function), in $\mathbf{L}^2$. More generally, we show that the family of Markov operators, indexed by $\varepsilon > 0$, derived from integrating test functions against the conditional density of the static Schr\"{o}dinger bridge at temperature $\varepsilon$, admits a derivative at $\varepsilon=0$ given by the generator of the Langevin semigroup. Hence, these operators satisfy an approximate semigroup property at low temperatures.
Modelling higher education dropouts using sparse and interpretable post-clustering logistic regression
Authors: Andrea Nigri, Massimo Bilancia, Barbara Cafarelli, Samuele Magro
Date: 2025-05-12 00:00:00

Higher education dropout constitutes a critical challenge for tertiary education systems worldwide. While machine learning techniques can achieve high predictive accuracy on selected datasets, their adoption by policymakers remains limited and unsatisfactory, particularly when the objective is the unsupervised identification and characterization of student subgroups at elevated risk of dropout. The model introduced in this paper is a specialized form of logistic regression, specifically adapted to the context of university dropout analysis. Logistic regression continues to serve as a foundational tool among reliable statistical models, primarily due to the ease with which its parameters can be interpreted in terms of odds ratios. Our approach significantly extends this framework by incorporating heterogeneity within the student population. This is achieved through the application of a preliminary clustering algorithm that identifies latent subgroups, each characterized by distinct dropout propensities, which are then modeled via cluster-specific effects. We provide a detailed interpretation of the model parameters within this extended framework and enhance interpretability by imposing sparsity through a tailored variant of the LASSO algorithm. To demonstrate the practical applicability of the proposed methodology, we present an extensive case study based on the Italian university system, in which all the developed tools are systematically applied
Causal mediation analysis with one or multiple mediators: a comparative study
Authors: Judith Abécassis, Houssam Zenati, Sami Boumaïza, Julie Josse, Bertrand Thirion
Date: 2025-05-12 00:00:00

Mediation analysis breaks down the causal effect of a treatment on an outcome into an indirect effect, acting through a third group of variables called mediators, and a direct effect, operating through other mechanisms. Mediation analysis is hard because confounders between treatment, mediators, and outcome blur effect estimates in observational studies. Many estimators have been proposed to adjust on those confounders and provide accurate causal estimates. We consider parametric and non-parametric implementations of classical estimators and provide a thorough evaluation for the estimation of the direct and indirect effects in the context of causal mediation analysis for binary, continuous, and multi-dimensional mediators. We assess several approaches in a comprehensive benchmark on simulated data. Our results show that advanced statistical approaches such as the multiply robust and the double machine learning estimators achieve good performances in most of the simulated settings and on real data. As an example of application, we propose a thorough analysis of factors known to influence cognitive functions to assess if the mechanism involves modifications in brain morphology using the UK Biobank brain imaging cohort. This analysis shows that for several physiological factors, such as hypertension and obesity, a substantial part of the effect is mediated by changes in the brain structure. This work provides guidance to the practitioner from the formulation of a valid causal mediation problem, including the verification of the identification assumptions, to the choice of an adequate estimator.
Adaptive Learning-based Surrogate Method for Stochastic Programs with Implicitly Decision-dependent Uncertainty
Authors: Boyang Shen, Junyi Liu
Date: 2025-05-12 00:00:00

We consider a class of stochastic programming problems where the implicitly decision-dependent random variable follows a nonparametric regression model with heteroscedastic error. The Clarke subdifferential and surrogate functions are not readily obtainable due to the latent decision dependency. To deal with such a computational difficulty, we develop an adaptive learning-based surrogate method that integrates the simulation scheme and statistical estimates to construct estimation-based surrogate functions in a way that the simulation process is adaptively guided by the algorithmic procedure. We establish the non-asymptotic convergence rate analysis in terms of $(\nu, \delta)$-near stationarity in expectation under variable proximal parameters and batch sizes, which exhibits the superior convergence performance and enhanced stability in both theory and practice. We provide numerical results with both synthetic and real data which illustrate the benefits of the proposed algorithm in terms of algorithmic stability and efficiency.
FCPCA: Fuzzy clustering of high-dimensional time series based on common principal component analysis
Authors: Ziling Ma, Ángel López-Oriona, Hernando Ombao, Ying Sun
Date: 2025-05-12 00:00:00
Tasks: Clustering, Clustering Multivariate Time Series, Time Series

Clustering multivariate time series data is a crucial task in many domains, as it enables the identification of meaningful patterns and groups in time-evolving data. Traditional approaches, such as crisp clustering, rely on the assumption that clusters are sufficiently separated with little overlap. However, real-world data often defy this assumption, exhibiting overlapping distributions or overlapping clouds of points and blurred boundaries between clusters. Fuzzy clustering offers a compelling alternative by allowing partial membership in multiple clusters, making it well-suited for these ambiguous scenarios. Despite its advantages, current fuzzy clustering methods primarily focus on univariate time series, and for multivariate cases, even datasets of moderate dimensionality become computationally prohibitive. This challenge is further exacerbated when dealing with time series of varying lengths, leaving a clear gap in addressing the complexities of modern datasets. This work introduces a novel fuzzy clustering approach based on common principal component analysis to address the aforementioned shortcomings. Our method has the advantage of efficiently handling high-dimensional multivariate time series by reducing dimensionality while preserving critical temporal features. Extensive numerical results show that our proposed clustering method outperforms several existing approaches in the literature. An interesting application involving brain signals from different drivers recorded from a simulated driving experiment illustrates the potential of the approach.
Kernel Dynamic Mode Decomposition For Sparse Reconstruction of Closable Koopman Operators
Authors: Nishant Panda, Himanshu Singh, J. Nathan Kutz
Date: 2025-05-11 00:00:00

Spatial temporal reconstruction of dynamical system is indeed a crucial problem with diverse applications ranging from climate modeling to numerous chaotic and physical processes. These reconstructions are based on the harmonious relationship between the Koopman operators and the choice of dictionary, determined implicitly by a kernel function. This leads to the approximation of the Koopman operators in a reproducing kernel Hilbert space (RKHS) associated with that kernel function. Data-driven analysis of Koopman operators demands that Koopman operators be closable over the underlying RKHS, which still remains an unsettled, unexplored, and critical operator-theoretic challenge. We aim to address this challenge by investigating the embedding of the Laplacian kernel in the measure-theoretic sense, giving rise to a rich enough RKHS to settle the closability of the Koopman operators. We leverage Kernel Extended Dynamic Mode Decomposition with the Laplacian kernel to reconstruct the dominant spatial temporal modes of various diverse dynamical systems. After empirical demonstration, we concrete such results by providing the theoretical justification leveraging the closability of the Koopman operators on the RKHS generated by the Laplacian kernel on the avenues of Koopman mode decomposition and the Koopman spectral measure. Such results were explored from both grounds of operator theory and data-driven science, thus making the Laplacian kernel a robust choice for spatial-temporal reconstruction.
Classifying Inconsistency in AHP Pairwise Comparison Matrices Using Machine Learning
Authors: Amarnath Bose
Date: 2025-05-07 00:00:00

Assessing consistency in Pairwise Comparison Matrices (PCMs) within the Analytical Hierarchy Process (AHP) poses significant challenges when using the traditional Consistency Ratio (CR) method. This study introduces a novel alternative that leverages triadic preference reversals (PR) to provide a more robust and interpretable assessment of consistency. Triadic preference reversals capture inconsistencies between a pair of elements by comparing the direction of preference derived from the global eigenvector with that from a 3x3 submatrix (triad) containing the same pair, highlighting local-global preference conflicts. This method detects a reversal when one eigen ratio exceeds one while another falls below one, signaling inconsistency. We identify two key features: the proportion of preference reversals and the maximum reversal, which mediate the impact of a PCM's order on its consistency. Using these features simulated PCMs are clustered into consistent and inconsistent classes through k-means clustering, followed by training a logistic classifier for consistency evaluation. The PR method achieves 97\% accuracy, significantly surpassing the Consistency Ratio (CR) method's 50%, with a false negative rate of only 2.6\% compared to 5.5\%. These findings demonstrate the PR method's superior accuracy in assessing AHP consistency, thereby enabling more reliable decision-making. The proposed triadic preference reversal (PR) approach is implemented in the R package AHPtools publicly available on the Comprehensive R Archive Network (CRAN).
Edge-Optimized Deep Learning & Pattern Recognition Techniques for Non-Intrusive Load Monitoring of Energy Time Series
Authors: Sotirios Athanasoulias
Date: 2025-05-07 00:00:00
Tasks: Model Compression, Non-Intrusive Load Monitoring, Time Series

The growing global energy demand and the urgent need for sustainability call for innovative ways to boost energy efficiency. While advanced energy-saving systems exist, they often fall short without user engagement. Providing feedback on energy consumption behavior is key to promoting sustainable practices. Non-Intrusive Load Monitoring (NILM) offers a promising solution by disaggregating total household energy usage, recorded by a central smart meter, into appliance-level data. This empowers users to optimize consumption. Advances in AI, IoT, and smart meter adoption have further enhanced NILM's potential. Despite this promise, real-world NILM deployment faces major challenges. First, existing datasets mainly represent regions like the USA and UK, leaving places like the Mediterranean underrepresented. This limits understanding of regional consumption patterns, such as heavy use of air conditioners and electric water heaters. Second, deep learning models used in NILM require high computational power, often relying on cloud services. This increases costs, raises privacy concerns, and limits scalability, especially for households with poor connectivity. This thesis tackles these issues with key contributions. It presents an interoperable data collection framework and introduces the Plegma Dataset, focused on underrepresented Mediterranean energy patterns. It also explores advanced deep neural networks and model compression techniques for efficient edge deployment. By bridging theoretical advances with practical needs, this work aims to make NILM scalable, efficient, and adaptable for global energy sustainability.
IIKL: Isometric Immersion Kernel Learning with Riemannian Manifold for Geometric Preservation
Authors: Zihao Chen, Wenyong Wang, Jiachen Yang, Yu Xiang
Date: 2025-05-07 00:00:00
Tasks: Representation Learning

Geometric representation learning in preserving the intrinsic geometric and topological properties for discrete non-Euclidean data is crucial in scientific applications. Previous research generally mapped non-Euclidean discrete data into Euclidean space during representation learning, which may lead to the loss of some critical geometric information. In this paper, we propose a novel Isometric Immersion Kernel Learning (IIKL) method to build Riemannian manifold and isometrically induce Riemannian metric from discrete non-Euclidean data. We prove that Isometric immersion is equivalent to the kernel function in the tangent bundle on the manifold, which explicitly guarantees the invariance of the inner product between vectors in the arbitrary tangent space throughout the learning process, thus maintaining the geometric structure of the original data. Moreover, a novel parameterized learning model based on IIKL is introduced, and an alternating training method for this model is derived using Maximum Likelihood Estimation (MLE), ensuring efficient convergence. Experimental results proved that using the learned Riemannian manifold and its metric, our model preserved the intrinsic geometric representation of data in both 3D and high-dimensional datasets successfully, and significantly improved the accuracy of downstream tasks, such as data reconstruction and classification. It is showed that our method could reduce the inner product invariant loss by more than 90% compared to state-of-the-art (SOTA) methods, also achieved an average 40% improvement in downstream reconstruction accuracy and a 90% reduction in error for geometric metrics involving isometric and conformal.
Soft causal learning for generalized molecule property prediction: An environment perspective
Authors: Limin Li, Kuo Yang, Wenjie Du, Pengkun Wang, Zhengyang Zhou, Yang Wang
Date: 2025-05-07 00:00:00
Tasks: Graph Learning, Property Prediction, scientific discovery

Learning on molecule graphs has become an increasingly important topic in AI for science, which takes full advantage of AI to facilitate scientific discovery. Existing solutions on modeling molecules utilize Graph Neural Networks (GNNs) to achieve representations but they mostly fail to adapt models to out-of-distribution (OOD) samples. Although recent advances on OOD-oriented graph learning have discovered the invariant rationale on graphs, they still ignore three important issues, i.e., 1) the expanding atom patterns regarding environments on graphs lead to failures of invariant rationale based models, 2) the associations between discovered molecular subgraphs and corresponding properties are complex where causal substructures cannot fully interpret the labels. 3) the interactions between environments and invariances can influence with each other thus are challenging to be modeled. To this end, we propose a soft causal learning framework, to tackle the unresolved OOD challenge in molecular science, from the perspective of fully modeling the molecule environments and bypassing the invariant subgraphs. Specifically, we first incorporate chemistry theories into our graph growth generator to imitate expaned environments, and then devise an GIB-based objective to disentangle environment from whole graphs and finally introduce a cross-attention based soft causal interaction, which allows dynamic interactions between environments and invariances. We perform experiments on seven datasets by imitating different kinds of OOD generalization scenarios. Extensive comparison, ablation experiments as well as visualized case studies demonstrate well generalization ability of our proposal.
A Data-Driven Probabilistic Framework for Cascading Urban Risk Analysis Using Bayesian Networks
Authors: Chunduru Rohith Kumar, PHD Surya Shanmuk, Prabhala Naga Srinivas, Sri Venkatesh Lankalapalli, Debasis Dwibedy
Date: 2025-05-07 00:00:00

The increasing complexity of cascading risks in urban systems necessitates robust, data-driven frameworks to model interdependencies across multiple domains. This study presents a foundational Bayesian network-based approach for analyzing cross-domain risk propagation across key urban domains, including air, water, electricity, agriculture, health, infrastructure, weather, and climate. Directed Acyclic Graphs (DAGs) are constructed using Bayesian Belief Networks (BBNs), with structure learning guided by Hill-Climbing search optimized through Bayesian Information Criterion (BIC) and K2 scoring. The framework is trained on a hybrid dataset that combines real-world urban indicators with synthetically generated data from Generative Adversarial Networks (GANs), and is further balanced using the Synthetic Minority Over-sampling Technique (SMOTE). Conditional Probability Tables (CPTs) derived from the learned structures enable interpretable probabilistic reasoning and quantify the likelihood of cascading failures. The results identify key intra- and inter-domain risk factors and demonstrate the framework's utility for proactive urban resilience planning. This work establishes a scalable, interpretable foundation for cascading risk assessment and serves as a basis for future empirical research in this emerging interdisciplinary field.
Interpretable Learning Dynamics in Unsupervised Reinforcement Learning
Authors: Shashwat Pandey
Date: 2025-05-06 00:00:00
Tasks: Diagnostic, Diversity, reinforcement-learning, Reinforcement Learning, Representation Learning, Unsupervised Reinforcement Learning

We present an interpretability framework for unsupervised reinforcement learning (URL) agents, aimed at understanding how intrinsic motivation shapes attention, behavior, and representation learning. We analyze five agents DQN, RND, ICM, PPO, and a Transformer-RND variant trained on procedurally generated environments, using Grad-CAM, Layer-wise Relevance Propagation (LRP), exploration metrics, and latent space clustering. To capture how agents perceive and adapt over time, we introduce two metrics: attention diversity, which measures the spatial breadth of focus, and attention change rate, which quantifies temporal shifts in attention. Our findings show that curiosity-driven agents display broader, more dynamic attention and exploratory behavior than their extrinsically motivated counterparts. Among them, TransformerRND combines wide attention, high exploration coverage, and compact, structured latent representations. Our results highlight the influence of architectural inductive biases and training signals on internal agent dynamics. Beyond reward-centric evaluation, the proposed framework offers diagnostic tools to probe perception and abstraction in RL agents, enabling more interpretable and generalizable behavior.
Reproducibility, Replicability, and Insights into Visual Document Retrieval with Late Interaction
Authors: Jingfen Qiao, Jia-Huei Ju, Xinyu Ma, Evangelos Kanoulas, Andrew Yates
Date: 2025-05-12 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Patch Matching, Retrieval, Text Retrieval

Visual Document Retrieval (VDR) is an emerging research area that focuses on encoding and retrieving document images directly, bypassing the dependence on Optical Character Recognition (OCR) for document search. A recent advance in VDR was introduced by ColPali, which significantly improved retrieval effectiveness through a late interaction mechanism. ColPali's approach demonstrated substantial performance gains over existing baselines that do not use late interaction on an established benchmark. In this study, we investigate the reproducibility and replicability of VDR methods with and without late interaction mechanisms by systematically evaluating their performance across multiple pre-trained vision-language models. Our findings confirm that late interaction yields considerable improvements in retrieval effectiveness; however, it also introduces computational inefficiencies during inference. Additionally, we examine the adaptability of VDR models to textual inputs and assess their robustness across text-intensive datasets within the proposed benchmark, particularly when scaling the indexing mechanism. Furthermore, our research investigates the specific contributions of late interaction by looking into query-patch matching in the context of visual document retrieval. We find that although query tokens cannot explicitly match image patches as in the text retrieval scenario, they tend to match the patch contains visually similar tokens or their surrounding patches.
KAQG: A Knowledge-Graph-Enhanced RAG for Difficulty-Controlled Question Generation
Authors: Ching Han Chen, Ming Fang Shiu
Date: 2025-05-12 00:00:00
Tasks: Knowledge Graphs, Question Generation, Question-Generation, RAG, Retrieval, Retrieval-augmented Generation

KAQG introduces a decisive breakthrough for Retrieval-Augmented Generation (RAG) by explicitly tackling the two chronic weaknesses of current pipelines: transparent multi-step reasoning and fine-grained cognitive difficulty control. This transforms RAG from a passive retriever into an accountable generator of calibrated exam items. Technically, the framework fuses knowledge graphs, RAG retrieval, and educational assessment theory into a single pipeline. Domain passages are parsed into a structured graph; graph-aware retrieval feeds fact chains to an LLM; and an assessment layer governed by Bloom's Taxonomy levels and Item Response Theory (IRT) transforms those chains into psychometrically sound questions. This cross-disciplinary marriage yields two scholarly contributions: it shows how semantic graph contexts guide LLM reasoning paths, and it operationalizes difficulty metrics within the generation process, producing items whose IRT parameters match expert benchmarks. Every module, from KG construction scripts to the multi-agent reasoning scheduler and the automatic IRT validator, is openly released on GitHub. This enables peer laboratories to replicate experiments, benchmark against baselines, and extend individual components without licensing barriers. Its reproducible design paves the way for rigorous ablation studies, cross-domain transfer experiments, and shared leaderboards on multi-step reasoning benchmarks.
From raw affiliations to organization identifiers
Authors: Myrto Kallipoliti, Serafeim Chatzopoulos, Miriam Baglioni, Eleni Adamidi, Paris Koloveas, Thanasis Vergoulis
Date: 2025-05-12 00:00:00
Tasks: Benchmarking, Metadata quality

Accurate affiliation matching, which links affiliation strings to standardized organization identifiers, is critical for improving research metadata quality, facilitating comprehensive bibliometric analyses, and supporting data interoperability across scholarly knowledge bases. Existing approaches fail to handle the complexity of affiliation strings that often include mentions of multiple organizations or extraneous information. In this paper, we present AffRo, a novel approach designed to address these challenges, leveraging advanced parsing and disambiguation techniques. We also introduce AffRoDB, an expert-curated dataset to systematically evaluate affiliation matching algorithms, ensuring robust benchmarking. Results demonstrate the effectiveness of AffRp in accurately identifying organizations from complex affiliation strings.