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Modification speed alters stability of ecological higher-order interaction networks

Physical Review E 2025年第1期111卷 014309-014309页

作者： Thomas Van Giel Aisling J. Daly Jan M. Baetens Bernard De Baets BionamiX Department of Data Analysis and Mathematical Modelling Ghent University 9000 Ghent Belgium KERMIT Department of Data Analysis and Mathematical Modelling Ghent University 9000 Ghent Belgium

Higher-order interactions (HOIs) have the potential to greatly increase our understanding of ecological interaction networks beyond what is possible with established models that usually consider only pairwise interactions between organisms. While equilibrium values of such HOI-based models have been studied, the dynamics of these models and the stability of their equilibria remain underexplored. Here we present a novel investigation on the effect of the onset speed of a higher-order interaction. In particular, we study the stability of the equilibrium of all configurations of a three-species interaction network, including transitive as well as intransitive ones. We show that the HOI onset speed has a dramatic effect on the evolution and stability of the ecological network, with significant structural changes compared to commonly used HOI extensions or pairwise networks. Changes in the HOI onset speed from fast to slow can reverse the stability of the interaction network. The evolution of the system also affects the equilibrium that will be reached, influenced by the HOI onset speed. This implies that the HOI onset speed is an important determinant in the dynamics of ecological systems, and including it in models of ecological networks can improve our understanding thereof.

关键词： Abiotic

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Two Prominent Examples of Penalty-Based Aggregation of Circular data 20th

Two Prominent Examples of Penalty-Based Aggregation of Cir...

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20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2024

作者： Pérez-Fernández, Raúl De Baets, Bernard Departamento de Estadística e I.O. y D.M. Universidad de Oviedo Oviedo Spain KERMIT Department of Data Analysis and Mathematical Modelling Ghent University Ghent Belgium

ISBN: (纸本)9783031739996

Aggregation processes appear naturally in many fields of application. The formalization of such processes has been a core topic for researchers in the fuzzy set community for decades, mostly focusing on the aggregation of elements of a bounded poset (typically a bounded real interval). Recent work by the present authors has aimed at further generalizing aggregation theory so it can accommodate aggregation processes on more general structures such as multivariate data, ranking data and string data. In this work, the aggregation of circular data is explored and, in particular, two prominent examples of aggregation functions for circular data (namely the circular mean and the circular median) are presented within this revisited aggregation theory framework. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Set theory

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FLAIR: Federated Learning for Augmented Industrial Retrieval

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IEEE Internet of Things Journal 2025年

作者： Chiaro, Diletta Qi, Pian Mele, Valeria Piccialli, Francesco University of Naples Federico II M.O.D.A.L. -Mathematical mOdelling and Data AnaLysis reserch group Department of Mathematics and Applications "R. Caccioppoli" Naples Italy

Deep learning (DL) has significantly advanced Industry 4.0 by leveraging data from the Industrial Internet of Things (IIoT) to enable smart manufacturing, predictive maintenance, and data-driven product marketing. However, multimodal industrial data presents challenges for traditional frameworks, including scalability, data privacy, and integration efficiency. This paper introduces an efficient product retrieval framework for e-commerce systems, addressing privacy and performance challenges through federated learning (FL). Specifically, we propose FLAIR (Federated Learning for Augmented Industrial Retrieval), a novel part retrieval system where distributed warehouses collaboratively train a multimodal foundation model, CLIP (Contrastive Language-Image Pre-Training), by fine-tuning only the Adapter module via FL, ensuring data privacy and efficiency. To address the limited availability of multimodal industrial data, our framework incorporates effective data augmentation strategies to enhance the diversity and quality of the training dataset. Comprehensive experiments on the Industrial Language-Image dataset (ILID) highlight that FLAIR holds effective privacy safeguards and strong retrieval capabilities. Additionally, an advanced e-commerce recommendation system built on FLAIR showcases its practical effectiveness. FLAIR represents the first application of FL for industrial product retrieval, optimizing part searches, inventory management, and customer experience while maintaining data security. © 2014 IEEE.

关键词： Marketplaces

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Pre-trained Maldi Transformers improve MALDI-TOF MS-based prediction

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Computers in Biology and Medicine 2025年 186卷 109695-109695页

作者： De Waele, Gaetan Menschaert, Gerben Vandamme, Peter Waegeman, Willem Department of Data Analysis and Mathematical Modelling Ghent University Coupure Links 653 Ghent9000 Belgium Laboratory of Microbiology Ghent University K. L. Ledeganckstraat 35 Ghent9000 Belgium

For the last decade, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been the reference method for species identification in clinical microbiology. Hampered by a historical lack of open data, machine learning research towards models specifically adapted to MALDI-TOF MS remains in its infancy. Given the growing complexity of available datasets (such as large-scale antimicrobial resistance prediction), a need for models that (1) are specifically designed for MALDI-TOF MS data, and (2) have high representational capacity, presents itself. Here, we introduce Maldi Transformer, an adaptation of the state-of-the-art transformer architecture to the MALDI-TOF mass spectral domain. We propose the first self-supervised pre-training technique specifically designed for mass spectra. The technique is based on shuffling peaks across spectra, and pre-training the transformer as a peak discriminator. Extensive benchmarks confirm the efficacy of this novel design. The final result is a model exhibiting state-of-the-art (or competitive) performance on downstream prediction tasks. In addition, we show that Maldi Transformer's identification of noisy spectra may be leveraged towards higher predictive performance. All code supporting this study is distributed on PyPI and is packaged under: https://***/gdewael/maldi-nn. © 2025

关键词： Distribution transformers

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Conformal Prediction in Hierarchical Classification

arXiv

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arXiv 2025年

作者： Mortier, Thomas Javanmardi, Alireza Sale, Yusuf Hüllermeier, Eyke Waegeman, Willem Department of Environment Ghent University Ghent Belgium Department of Data Analysis and Mathematical Modelling Ghent University Ghent Belgium Institute of Informatics LMU Munich Munich Germany Munich Center for Machine Learning Munich Germany

Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In this work, we extend the split conformal prediction framework to hierarchical classification, where prediction sets are commonly restricted to internal nodes of a predefined hierarchy, and propose two computationally efficient inference algorithms. The first algorithm returns internal nodes as prediction sets, while the second relaxes this restriction, using the notion of representation complexity, yielding a more general and combinatorial inference problem, but smaller set sizes. Empirical evaluations on several benchmark datasets demonstrate the effectiveness of the proposed algorithms in achieving nominal coverage. © 2025, CC BY.

关键词： Prediction models

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Consistent multi-animal pose estimation in cattle using dynamic Kalman filter based tracking

arXiv

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arXiv 2025年

作者： Perneel, Maarten Adriaens, Ines Aernouts, Ben Verwaeren, Jan Department of Biosystems Division of Animal and Human Health Engineering KU Leuven Campus Geel Kleinhoefstraat 4 Geel2440 Belgium Department of Data Analysis and Mathematical Modelling Faculty of Bioscience Engineering Ghent University Coupure Links 653 Ghent9000 Belgium

Over the past decade, studying animal behaviour with the help of computer vision has become more popular. Replacing human observers by computer vision lowers the cost of data collection and therefore allows to collect more extensive datasets. However, the majority of available computer vision algorithms to study animal behaviour is highly tailored towards a single research objective, limiting possibilities for data reuse. In this perspective, pose-estimation in combination with animal tracking offers opportunities to yield a higher level representation capturing both the spatial and temporal component of animal behaviour. Such a higher level representation allows to answer a wide variety of research questions simultaneously, without the need to develop repeatedly tailored computer vision algorithms. In this paper, we therefore first cope with several weaknesses of current pose-estimation algorithms and thereafter introduce KeySORT (Keypoint Simple and Online Realtime Tracking). KeySORT deploys an adaptive Kalman filter to construct tracklets in a bounding-box free manner, significantly improving the temporal consistency of detected keypoints. In this paper, we focus on pose estimation in cattle, but our methodology can easily be generalised to any other animal species. Our test results indicate our algorithm is able to detect up to 80% of the ground truth keypoints with high accuracy, with only a limited drop in performance when daylight recordings are compared to nightvision recordings. Moreover, by using KeySORT to construct skeletons, the temporal consistency of generated keypoint coordinates was largely improved, offering opportunities with regard to automated behaviour monitoring of animals. © 2025, CC BY-SA.

关键词： Kalman filters

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An OWA analysis of the VSTOXX Volatility Index

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International Journal of Information Technology and Decision Making 2025年

作者： Gambarelli, Luca Muzzioli, Silvia De Baets, Bernard Marco Biagi Department of Economics University of Modena and Reggio Emilia Viale Berengario 51 Modena41121 Italy Kermit Department of Data Analysis and Mathematical Modelling Ghent University Coupure Links 653 GhentB-9000 Belgium

In this paper, we analyze the information value of the VSTOXX (volatility) index as a measure of risk for the European stock market. Based upon daily data from 2007 to 2023, the properties of the VSTOXX index are inspected and contrasted under various market conditions and in high- and low-volatility periods. Moreover, to evaluate the contribution of each country-specific index to the VSTOXX index, we employ the Ordered Weighted Averaging (OWA) operator as an analysis tool. We obtain a number of useful insights. Only for France and Germany the correlation between the country-specific volatility index and the VSTOXX index is high during the entire period. In addition, the VSTOXX index acts more like a maximum than as a minimum of volatility for the European stock markets and acts as an average only during periods of extreme volatility. Our findings offer important implications for both investors and policymakers. © 2025 World Scientific Publishing Company.

关键词： Marketplaces

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Transfer learning and single-polarized SAR image preprocessing for oil spill detection

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ISPRS Open Journal of Photogrammetry and Remote Sensing 2025年 15卷

作者： Kussul, Nataliia Salii, Yevhenii Kuzin, Volodymyr Yailymov, Bohdan Shelestov, Andrii National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Department of Mathematical Modelling and Data Analysis Kyiv Ukraine Space Research Institute NAS Ukraine and SSA Ukraine Department of Space Information Technologies and Systems Kyiv Ukraine University of Maryland Department of Geographical Sciences College Park United States

This study addresses the challenge of oil spill detection using Synthetic Aperture Radar (SAR) satellite imagery, employing deep learning techniques to improve accuracy and efficiency. We investigated the effectiveness of various neural network architectures and encoders for this task, focusing on scenarios with limited training data. The research problem centered on enhancing feature extraction from single-channel SAR data to improve oil spill detection performance. Our methodology involved developing a novel preprocessing pipeline that converts single-channel SAR data into a three-channel RGB representation. The preprocessing technique normalizes SAR intensity values and encodes extracted features into RGB channels. Through an experiment, we have shown that a combination of the LinkNet with an EfficientNet-B4 is superior to pairs of other well-known architectures and encoders. Quantitative evaluation revealed a significant improvement in F1-score of 0.064 compared to traditional dB-scale preprocessing methods. Qualitative assessment on independent SAR scenes from the Mediterranean Sea demonstrated better detection capabilities, albeit with increased sensitivity to look-alike. We conclude that our proposed preprocessing technique shows promise for enhancing automatic oil spill segmentation from SAR imagery. The study contributes to advancing oil spill detection methods, with potential implications for environmental monitoring and marine ecosystem protection. © 2024 The Authors

关键词： Deep learning Image preprocessing Oil spill detection Synthetic aperture radar (SAR) Transfer learning

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Multifractal behaviors on exchange rate of world prominent economic countries correspond to Malaysia Ringgit

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Physica A: Statistical Mechanics and its Applications 2025年 671卷

作者： Masseran, Nurulkamal Safari, Muhammad Aslam Mohd Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi 43600 UKM Malaysia Center for Modelling and Data Analysis (DELTA) Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi 43600 UKM Malaysia Department of Mathematics and Statistics Faculty of Science Universiti Putra Malaysia Selangor Serdang 43400 UPM Malaysia Institute for Mathematical Research (INSPEM) Universiti Putra Malaysia Selangor Serdang 43400 UPM Malaysia

This study analyzes the multifractal properties of foreign exchange rates between the Malaysian Ringgit (MYR) and several major global economies, including the United States Dollar (USD), Chinese Yuan (RMB), Euro, Japanese Yen (JPY), Indian Rupee (INR), British Pound (GBP), Canadian Dollar (CAD), and South Korean Won (KRW). Using the methodologies of multifractal detrended fluctuation analysis (MFDFA) and multifractal detrended cross-correlation analysis (MFDCCA), this study examines both individual and paired exchange rate time series. This study also assesses power-law characteristics of fluctuation functions, complexity, persistence, and variability in these exchange rates. The results indicate that the exchange rates for MYR/USD, MYR/RMB, MYR/GBP, and MYR/CAD demonstrate scale-invariant characteristics, suggesting the presence of a multifractal nature within these temporal series. In contrast, a large fluctuation in the MYR/EUR, MYR/JPY, MYR/KRW, and MYR/INR exchange rates are found to behave like a non-multifractal behavior. Asymmetry measures reveal that MYR/KRW and MYR/INR are more susceptible to short-term fluctuations, while MYR/USD and MYR/CAD are predominantly influenced by longer-term trends. Generalized Hurst exponent (GHE) plots reveal varying cross-correlation patterns, with majority of the exchange rate pairs reflecting pronounced cross-multifractal behavior. Certain pairs, including MYR/USD–MYR/CAD, MYR/USD–MYR/GBP, MYR/RMB–MYR/GBP, MYR/RMB–MYR/CAD, and MYR/GBP–MYR/CAD indicate a weaker degree of multifractal properties in their cross-relations. This study contributes to the comprehension of exchange rate dynamics, offering valuable insights into the associated risks and volatilities of various currency pairs. © 2025 Elsevier B.V.

关键词： Cross-correlation Dynamic behaviors Exchange rate Multifractal

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Constraint-Guided Learning of data-driven Health Indicator Models: An Application on the Pronostia Bearing dataset

arXiv

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arXiv 2025年

作者： Tefera, Yonas Van Baelen, Quinten Meire, Maarten Luca, Stijn Karsmakers, Peter KU Leuven Dept. of Computer Science ADVISE-DTAI Kleinhoefstraat 4 GeelB-2440 Belgium Leuven.AI - KU Leuven Institute for AI Belgium Flanders Make @ KU Leuven Belgium Addis Ababa University School of Electrical and Computer Engineering Addis Ababa Ethiopia Ghent University Department of Data Analysis and Mathematical Modelling Coupure Links 653 Gent9000 Belgium

This paper presents a constraint-guided deep learning framework to develop physically consistent health indicators in bearing prognostics and health management. Conventional data-driven approaches often lack physical plausibility, while physics-based models are limited by incomplete knowledge of complex systems. To address this, we integrate domain knowledge into deep learning models via constraints, ensuring monotonicity, bounding output ranges between 1 and 0 (representing healthy to failed states respectively), and maintaining consistency between signal energy trends and health indicator estimates. This eliminates the need for complex loss term balancing to incorporate domain knowledge. We implement a constraint-guided gradient descent optimization within an autoencoder architecture, creating a constrained autoencoder. However, the framework is flexible and can be applied to other architectures as well. Using time-frequency representations of accelerometer signals from the Pronostia dataset, the constrained model generates more accurate and reliable representations of bearing health compared to conventional methods. It produces smoother degradation profiles that align with the expected physical behavior. Model performance is assessed using three metrics: trendability, robustness, and consistency. When compared to a conventional baseline model, the constrained model shows significant improvement in all three metrics. Another baseline incorporated the monotonicity behavior directly into the loss function using a soft-ranking approach. While this approach outperforms the constrained model in trendability, due to its explicit monotonicity enforcement, the constrained model performed better in robustness and consistency, providing stable and interpretable health indicator estimates over time. The results of the ablation study confirm that the monotonicity constraint enhances trendability, the boundary constraint ensures consistency, and the energy-health indicator con

关键词： Electronic health record

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