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arXiv 2025年

作者： Alene, Muluneh Vansteelandt, Stijn Van Lancker, Kelly Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Public Health Debre Markos University Debre Markos Ethiopia

Augmented inverse probability weighting (AIPW) and G-computation with canonical generalized linear models have become increasingly popular for estimating the average treatment effect in randomized experiments. These estimators leverage outcome prediction models to adjust for imbalances in baseline covariates across treatment arms, improving statistical power compared to unadjusted analyses, while maintaining control over Type I error rates, even when the models are misspecified. Practical application of such estimators often overlooks the clustering present in multi-center clinical trials. Even when prediction models account for center effects, this neglect can degrade the coverage of confidence intervals, reduce the efficiency of the estimators, and complicate the interpretation of the corresponding estimands. These issues are particularly pronounced for estimators of counterfactual means, though less severe for those of the average treatment effect, as demonstrated through Monte Carlo simulations and supported by theoretical insights. To address these challenges, we develop efficient estimators of counterfactual means and the average treatment effect in a random center. These extract information from baseline covariates by relying on outcome prediction models, but remain unbiased in large samples when these models are misspecified. We also introduce an accompanying inference framework inspired by random-effects meta-analysis and relevant for settings where data from many small centers are being analyzed. Adjusting for center effects yields substantial gains in efficiency, especially when treatment effect heterogeneity across centers is large. Monte Carlo simulations and application to the WASH Benefits Bangladesh study demonstrate adequate performance of the proposed methods. © 2025, CC BY.

关键词： Inverse problems

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Investigating the Impact of Language-Adaptive Fine-Tuning on Sentiment Analysis in Hausa Language Using AfriBERTa

arXiv

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arXiv 2025年

作者： Sani, Sani Abdullahi Muhammad, Shamsuddeen Hassan Jarvis, Devon School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa Department of Computing Imperial College London United Kingdom

Sentiment analysis (SA) plays a vital role in Natural Language Processing (NLP) by identifying sentiments expressed in text. Although significant advances have been made in SA for widely spoken languages, low-resource languages such as Hausa face unique challenges, primarily due to a lack of digital resources. This study investigates the effectiveness of Language-Adaptive Fine-Tuning (LAFT) to improve SA performance in Hausa. We first curate a diverse, unlabeled corpus to expand the model’s linguistic capabilities, followed by applying LAFT to adapt AfriBERTa specifically to the nuances of the Hausa language. The adapted model is then fine-tuned on the labeled NaijaSenti sentiment dataset to evaluate its performance. Our findings demonstrate that LAFT gives modest improvements, which may be attributed to the use of formal Hausa text rather than informal social media data. Nevertheless, the pre-trained AfriBERTa model significantly outperformed models not specifically trained on Hausa, highlighting the importance of using pre-trained models in low-resource contexts. This research emphasizes the necessity for diverse data sources to advance NLP applications for low-resource African languages. We published the code and the dataset to encourage further research and facilitate reproducibility in low-resource NLP here SA for LowRes Language Copyright © 2025, The Authors. All rights reserved.

关键词： Natural language processing systems

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Exploring Exploration in Bayesian Optimization

arXiv

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arXiv 2025年

作者： Papenmeier, Leonard Cheng, Nuojin Becker, Stephen Nardi, Luigi Department of Computer Science Lund University Lund Sweden Department of Applied Mathematics University of Colorado Boulder BoulderCO United States DBtune Sweden

A well-balanced exploration-exploitation trade-off is crucial for successful acquisition functions in Bayesian optimization. However, there is a lack of quantitative measures for exploration, making it difficult to analyze and compare different acquisition functions. This work introduces two novel approaches – observation traveling salesman distance and observation entropy – to quantify the exploration characteristics of acquisition functions based on their selected observations. Using these measures, we examine the explorative nature of several well-known acquisition functions across a diverse set of black-box problems, uncover links between exploration and empirical performance, and reveal new relationships among existing acquisition functions. Beyond enabling a deeper understanding of acquisition functions, these measures also provide a foundation for guiding their design in a more principled and systematic manner. © 2025, CC BY.

关键词： Markov processes

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Word2Minecraft: Generating 3D Game Levels through Large Language Models

arXiv

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arXiv 2025年

作者： Huang, Shuo Nasir, Muhammad Umair James, Steven Togelius, Julian Department of Computer Science and Engineering Tandon School of Engineering New York University New York10012 United States School of Computer Science and Applied Mathematics University of the Witwatersrand South Africa

We present Word2Minecraft, a system that leverages large language models to generate playable game levels in Minecraft based on structured stories. The system transforms narrative elements—such as protagonist goals, antagonist challenges, and environmental settings—into game levels with both spatial and gameplay constraints. We introduce a flexible framework that allows for the customization of story complexity, enabling dynamic level generation. The system employs a scaling algorithm to maintain spatial consistency while adapting key game elements. We evaluate Word2Minecraft using both metric-based and human-based methods. Our results show that GPT-4Turbo outperforms GPT-4o-Mini in most areas, including story coherence and objective enjoyment, while the latter excels in aesthetic appeal. We also demonstrate the system’s ability to generate levels with high map enjoyment, offering a promising step forward in the intersection of story generation and game design. We open-source the code at https://***/JMZkk/Word2Minecraft/tree/word2mc_v0 Copyright © 2025, The Authors. All rights reserved.

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High Capacity Reversible Data Hiding in Encrypted 3D Mesh Models Based on Dynamic Prediction and Virtual Connection

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2996-3010页

作者： Wang, Ke Yao, Ye Shen, Yanzhao Xiao, Fengjun Ren, Yizhi Meng, Weizhi Hangzhou Dianzi University School of Cyberspace Hangzhou310018 China Shandong Institute of Blockchain Jinan250102 China Hangzhou Dianzi University Zhejiang Informatization Development Institute Hangzhou310018 China Lancaster University School of Computing and Communications LancasterLA1 4YW United Kingdom Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark

In recent years, reversible data hiding in encrypted domain (RDH-ED) has garnered considerable interest among researchers, resulting in the development of high-performance methods based on various carriers. However, the challenge of enhancing the data embedding capacity while ensuring reversibility becomes increasingly pronounced when the carrier is a three-dimensional (3D) model. In this paper, a high capacity RDH-ED method based on dynamic prediction and virtual connection for 3D models is proposed. Unlike existing methods that partition the vertices in the model into embeddable and prediction sets, where each vertex can only serve one function, the proposed dynamic prediction mechanism constructs a data embedding order set by leveraging the connectivity relationships between vertices. This allows each vertex within the set to both embed data and provide predictions, significantly increasing the proportion of embeddable vertices. Moreover, the proposed method is the first work to consider independent vertices within the model and integrates a novel virtual connection approach with the dynamic prediction process, enabling all independent vertices to participate in data embedding and prediction, thereby further enhancing the data embedding capacity. Experimental results demonstrated that the proposed method significantly outperforms other state-of-the-art methods in terms of data embedding capacity while ensuring reversibility. © 2004-2012 IEEE.

关键词： Prediction models

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Quantum Perfect Matchings

arXiv

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arXiv 2025年

作者： Cui, David Mančinska, Laura Nezhadi, Seyed Sajjad Roberson, David E. Department of Mathematics Massachusetts Institute of Technology United States Department of Mathematical Sciences University of Copenhagen Denmark University of Maryland United States Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark

We investigate quantum and nonsignaling generalizations of perfect matchings in graphs using nonlocal games. Specifically, we introduce nonlocal games that test for L-perfect matchings in bipartite graphs, perfect matchings in general graphs and hypergraphs, and fractional perfect matchings. Our definitions come from the fact that these games are classical property tests for the corresponding matching conditions. We use the existence of perfect quantum and nonsignaling strategies for these games to define quantum and nonsignaling versions of perfect matchings. Finally, we provide characterizations of when graphs exhibit these extended properties: • For nonsignaling matchings, we give a complete combinatorial characterizations. In particular, a graph has a nonsignaling perfect matching if and only if it admits a fractional perfect matching that has bounded value on triangles. • In bipartite graphs, the nonsignaling L-perfect matching property is achieved exactly when the left component of the graph can be split into two disjoint subgraphs: one with a classical L-perfect matching and another with left-degree 2. • In the quantum setting, we show that complete graphs Kn with odd n ≥ 7 have quantum perfect matchings. We prove that a graph has a quantum perfect matching if and only if the quantum independence number of its line graph is maximal, extending a classical relationship between perfect matchings and line graph independence numbers. • For bipartite graphs, we establish that the L-perfect matching game does not exhibit quantum pseudotelepathy, but we characterize the quantum advantage for complete bipartite graphs Kn,2. • Additionally, we prove that deciding quantum perfect matchings in hypergraphs is undecidable and leave open the question of its complexity in graphs. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph theory

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Numerically robust Gaussian state estimation with singular observation noise

arXiv

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arXiv 2025年

作者： Krämer, Nicholas Tronarp, Filip Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark Centre for Mathematical Sciences Lund University Lund Sweden

This article proposes numerically robust algorithms for Gaussian state estimation with singular observation noise. Our approach combines a series of basis changes with Bayes’ rule, transforming the singular estimation problem into a nonsingular one with reduced state dimension. In addition to ensuring low runtime and numerical stability, our proposal facilitates marginal-likelihood computations and Gauss–Markov representations of the posterior process. We analyse the proposed method’s computational savings and numerical robustness and validate our findings in a series of simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Markov processes

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GRAMA: ADAPTIVE GRAPH AUTOREGRESSIVE MOVING AVERAGE MODELS

arXiv

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arXiv 2025年

作者： Eliasof, Moshe Gravina, Alessio Ceni, Andrea Gallicchio, Claudio Bacciu, Davide Schönlieb, Carola-Bibiane Department of Applied Mathematics University of Cambridge Cambridge United Kingdom Department of Computer Science University of Pisa Pisa Italy University of Cambridge Cambridge United Kingdom

Graph State Space Models (SSMs) have recently been introduced to enhance Graph Neural Networks (GNNs) in modeling long-range interactions. Despite their success, existing methods either compromise on permutation equivariance or limit their focus to pairwise interactions rather than sequences. Building on the connection between Autoregressive Moving Average (ARMA) and SSM, in this paper, we introduce GRAMA, a Graph Adaptive method based on a learnable Autoregressive Moving Average (ARMA) framework that addresses these limitations. By transforming from static to sequential graph data, GRAMA leverages the strengths of the ARMA framework, while preserving permutation equivariance. Moreover, GRAMA incorporates a selective attention mechanism for dynamic learning of ARMA coefficients, enabling efficient and flexible long-range information propagation. We also establish theoretical connections between GRAMA and Selective SSMs, providing insights into its ability to capture long-range dependencies. Extensive experiments on 14 synthetic and real-world datasets demonstrate that GRAMA consistently outperforms backbone models and performs competitively with state-of-the-art methods. © 2025, CC BY.

关键词： Contrastive Learning

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COLLECT, COMMIT, EXPAND: EFFICIENT CPQR-BASED COLUMN SELECTION FOR EXTREMELY WIDE MATRICES

arXiv

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arXiv 2025年

作者： Armstrong, Robin Damle, Anil Cornell University Center for Applied Mathematics IthacaNY United States Cornell University Department of Computer Science IthacaNY United States

Column-pivoted QR (CPQR) factorization is a computational primitive used in numerous applications that require selecting a small set of "representative" columns from a much larger matrix. These include applications in spectral clustering, model-order reduction, low-rank approximation, and computational quantum chemistry, where the matrix being factorized has a moderate number of rows but an extremely large number of columns. We describe a modification of the Golub-Businger algorithm which, for many matrices of this type, can perform CPQR-based column selection much more efficiently. This algorithm, which we call CCEQR, is based on a three-step "collect, commit, expand" strategy that limits the number of columns being manipulated, while also transferring more computational effort from level-2 BLAS to level-3. Unlike most CPQR algorithms that exploit level-3 BLAS, CCEQR is deterministic, and provably recovers a column permutation equivalent to the one computed by the Golub-Businger algorithm. Tests on spectral clustering and Wannier basis localization problems demonstrate that on appropriately structured problems, CCEQR can significantly outperform GEQP3. Copyright © 2025, The Authors. All rights reserved.

关键词： Density functional theory

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A Novel Methodology to Predict Flood and Landslide Disasters using Learning Assisted Artificial Intelligence (AI) Logic 3

A Novel Methodology to Predict Flood and Landslide Disasters...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Raj, G. Bhupal Patan, Muzeeb Khan Gupta, Ashwani Kumar Lakshmi, C. Gopinath, D. Yadav, Ajay Singh Sr University School of Agriculture Warangal India University of Technology and Applied Sciences College of Engineering and Technology Department of Eee Muscat Oman Chandigarh University Faculty of Business and Commerce Uttar Pradesh Unnao India Saveetha Engineering College Chennai India Department of Computer Science Karnataka Bengaluru India Srm Institute of Science and Technology Department of Mathematics Delhi-NCR Campus U.P Ghaziabad India

ISBN: (纸本)9798331509675

Comprehending the dynamics of flood and landslide prediction in current and adjacent regions is crucial for the formulation and advancement of effective predictive models, conservation, and management techniques. Conventional forecasting techniques, typically dependent on statistical models and empirical data, may prove inadequate because of the intrinsic complexities and variability of environmental elements. The complex interaction of climatic conditions, hydrological processes, geological attributes, and topographical aspects complicates precise forecasting. The constraints of traditional methods highlight the pressing necessity for more advanced and efficient predictive instruments. Artificial intelligence, especially machine learning (ML) and deep learning (DL), presents a robust paradigm that utilizes sophisticated algorithms to examine extensive datasets, discern intricate patterns, and produce more precise and prompt predictions. This proposed ViT-DL model prediction ability can greatly boost catastrophe preparedness, mitigation strategies, and ultimately, preserve lives and reduce economic damages. This study will comprehensively examine contemporary research trends, emphasizing the methodology used, model efficacy, related problems, and potential future research avenues in AI-based flood and landslide prediction. The experimental results of our proposed model produces 96.6% of Accuracy and 96.31% of Precision, comparision shows our model outperforms the overall accuracy to conventions model techniques. © 2025 IEEE.

关键词： Landslides

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