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arXiv 2022年

作者： Bengs, Viktor Hüllermeier, Eyke Waegeman, Willem Germany Munich Center for Machine Learning Germany Department of Data Analysis and Mathematical Modeling Ghent University Belgium

Uncertainty quantification has received increasing attention in machine learning in the recent past. In particular, a distinction between aleatoric and epistemic uncertainty has been found useful in this regard. The latter refers to the learner's (lack of) knowledge and appears to be especially difficult to measure and quantify. In this paper, we analyse a recent proposal based on the idea of a second-order learner, which yields predictions in the form of distributions over probability distributions. While standard (first-order) learners can be trained to predict accurate probabilities, namely by minimising suitable loss functions on sample data, we show that loss minimisation does not work for second-order predictors: The loss functions proposed for inducing such predictors do not incentivise the learner to represent its epistemic uncertainty in a faithful *** Codes 68T37 (Primary) 68T30 (Secondary) Copyright © 2022, The Authors. All rights reserved.

关键词： Probability distributions

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Whole-slide Classification of H&E-stained Cervix Uteri Tissue using Deep Neural Networks 14

Whole-slide Classification of H&E-stained Cervix Uteri Tissu...

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14th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2022 as part of 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2022

作者： Idlahcen, Ferdaous Mboukou, Pierjos Francis Colere Zerouaoui, Hasnae Idri, Ali Modeling Simulation & Data Analysis - MSDA Mohammed VI Polytechnic University - UM6P Ben Guerir43150 Morocco Software Project Management Research Team ENSIAS Mohammed V University - UM5 Rabat10000 Morocco

ISBN: (纸本)9789897586149

Cervical cancer (CxCa) is heavily swerved toward low- and middle-income countries (LMICs). Without prompt actions, the burden is anticipated to worsen by 50% from 2020 to 2040 - nearly 90% of deaths to occur in sub-Saharan Africa (SSA). Yet, uterine cervix neoplasms are readily avoidable due to a protracted latent cancer period. As it stands, deep learning (DL) is a potent solution for enhancing the early detection of cervical cancer. This work assesses and compares the performance of seven end-to-end learning architectures to automatically recognize cervical lesions and carcinoma histotypes upon hematoxylin and eosin (H&E)stained pathology images. Pre-trained VGG16, VGG19, InceptionV3, ResNet50, MobileNetV2, InceptionResNetV2, and DenseNet201 were the implemented deep convolutional neural networks (dCNNs) throughout the present empirical analysis. Experiments are conducted on two datasets: (i) Mendeley liquid-based cytology (LBC) and (ii) The Cancer Genome Atlas (TCGA) Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma diagnostic slides. All tests were validated under a 5-fold cross-validation, with four key metrics, Scott-Knott (SK), and Borda count schemes. Both pathology data appear to promote InceptionV3 and DenseNet201. Yet, while VGG16 is a weak-performing approach for liquid-based cytology, it evinces promise in histopathology yielding 99.33% accuracy, 98.85% precision, 99.83% recall, and 99.34% F-measure. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Pathology

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Split-and-Merge Based Simultaneous Input and State Filter for Nonlinear Dynamic Systems

Split-and-Merge Based Simultaneous Input and State Filter fo...

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Chinese Automation Congress (CAC)

作者： Sanfeng Hu Liping Guo Mengjiao Tang Yao Rong Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University China College of Mathematics Sichuan University China

This paper investigates the problem of simultaneous input and state estimation (SISE) for nonlinear dynamic systems. By the augmented state approach, we convert this problem into a standard filtering problem. Then, the split-and-merge technique is utilized for the augmented state estimation. Based on this, a novel split-and-merge based simultaneous input and state filter is developed in order to enhance the ability of dealing with highly nonlinear systems. Simulations demonstrate the effectiveness and efficiency of the proposed filter.

关键词： Automation Filtering Computational modeling Nonlinear dynamical systems Computational efficiency Kalman filters State estimation

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Qualitative analysis of a Diffusive Predator-prey Model with Nonlcoal Fear Effect

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数学理论与应用 2024年第3期44卷 67-82页

作者： Shen Zhongyuan Zhang Xuebing Li Shunjie Center for Applied Mathematics of Jiangsu Province Jiangsu International Joint Laboratory on System Modeling and Data AnalysisNanjing 210044China

In this paper,we establish a delayed predator-prey model with nonlocal fear ***,the existence,uniqueness,and persistence of solutions of the model are ***,the local stability,Turing bifurcation,and Hopf bifurcation of the constant equilibrium state are analyzed by examining the characteristic *** global asymptotic stability of the positive equilibrium point is investigated using the Lyapunov function ***,the correctness of the theoretical analysis results is verified through numerical simulations.

关键词： Delay Nonlocal fear effect Global stability Hopf bifurcation

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GottBERT: a pure German Language Model

GottBERT: a pure German Language Model

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Scheible, Raphael Frei, Johann Thomczyk, Fabian He, Henry Tippmann, Patric Knaus, Jochen Jaravine, Victor Kramer, Frank Boeker, Martin Institute for AI and Informatics in Medicine University Hospital rechts der Isar Technical University Munich Germany IT-Infrastructure for Translational Medical Research Faculty of Applied Computer Science University of Augsburg Germany Data Integration Center Faculty of Medicine University of Freiburg Germany School of Computation Information and Technology Technical University Munich Germany Institute of Medical Biometry and Statistics Medical Center Faculty of Medicine University of Freiburg Germany Freiburg Center for Data Analysis and Modeling University of Freiburg Germany Hengrui Europe Biosciences Zurich Switzerland

ISBN: (纸本)9798891761643

Pre-trained language models have significantly advanced natural language processing (NLP), especially with the introduction of BERT and its optimized version, RoBERTa. While initial research focused on English, single-language models can be advantageous compared to multilingual ones in terms of pre-training effort, overall resource efficiency or downstream task performance. Despite the growing popularity of prompt-based LLMs, more compute-efficient BERT-like models remain highly relevant. In this work, we present the first German single-language RoBERTa model, GottBERT, pre-trained exclusively on the German portion of the OSCAR dataset. Additionally, we investigated the impact of filtering the OSCAR corpus. GottBERT was pre-trained using fairseq and standard hyperparameters. We evaluated its performance on two Named Entity Recognition (NER) tasks (Conll 2003 and GermEval 2014) and three text classification tasks (GermEval 2018 fine and coarse, and 10kGNAD) against existing German BERT models and two multilingual models. Performance was measured using the F1 score and accuracy. The GottBERT base and large models showed competitive performance, with GottBERT leading among the base models in 4 of 6 tasks. Contrary to our expectation, the applied filtering did not significantly affect the results. To support the German NLP research community, we are releasing the GottBERT models under the MIT license. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A CFL condition for the finite cell method

arXiv

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arXiv 2025年

作者： Bürchner, Tim Radtke, Lars Kopp, Philipp Computational Modeling and Simulation Technische Universität München Germany Institute for Ship Structural Design and Analysis Technische Universität Hamburg Germany Chair of Data Science in Civil Engineering Bauhaus-Universität Weimar Germany

Immersed boundary finite element methods allow the user to bypass the potentially troublesome task of boundary-conforming mesh generation. However, they suffer from the influence of cut elements, i.e., elements that are intersected by the physical domain boundaries. When combined with explicit time integration, poorly cut elements with little support in the physical domain have a detrimental effect on the critical time step size, thereby hampering the application of immersed boundary methods to wave propagation simulations. In this paper, we investigate the stabilizing effect of the finite cell method concerning explicit time integration. Starting with an analytical solution of an example with one degree of freedom, we systematically study the influence of α-stabilization on the maximum eigenvalue and thus on the critical time step size. The analysis is then complemented by a numerical study of an example with one element and an increasing polynomial degree. We demonstrate that the critical time step size does not decrease below a certain limit, even when further reducing the cut fraction of the element. This minimum critical time step size is controlled by the chosen α value and becomes less severe for higher dimensions. Increasing the polynomial degree has little effect on the degradation of the minimum critical time step size. Finally, we provide an estimate of the minimum critical time step size depending on the chosen stabilization parameter α and the dimension of the problem. Based on this estimate, we propose a modified CFL condition for the finite cell method, the validity of which we demonstrate on a numerical example of a perforated plate. © 2025, CC BY.

关键词： Boundary element method

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Machine Learning Models and Technology for Classification of Forest on Satellite data

Machine Learning Models and Technology for Classification of...

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Eurocon

作者： Yevhenii Salii Volodymyr Kuzin Anton Hohol Nataliia Kussul Hanna Yailymova Department of Mathematical Modeling and Data Analysis National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine Department of Informatics National University of “Kyiv-Mohyla Academy” Kyiv Ukraine Department of Space Information Technologies and Systems Space Research Institute NASU-SSAU Department of Mathematical Modeling and Data Analysis National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine

The paper deals with the problem of semantic segmentation of satellite imagery to deliver forest type map with high resolution. To solve the problem, we propose 4 machine learning models. Two of them are based on Random Forest (RF) and other two - on Convolutional Neural Network (CNN) - U-Net. As an input we use 2 images of Sentinel-2 (one for summer and one for winter, 4 spectral bands from each). As an output (labels) we use the Copernicus Forest Type dataset for 2018 year. Our models showed promising results on validation data. Of all models the one based on U-Net ended up being the most efficient in forest classification with overall accuracy 91.7%. At the same time the best RF model scored with 86.5%. After comparing the results, in order to check our model transferability we created and compared forest map of northern part of Kyiv region of 2018 and 2022. The experiment confirmed the robustness of the model and it's scalability. The developed models have been implemented in the cloud platform specialized on satellite data - CREODIAS. The developed map can provide valuable data for foresters, biologists, or other researchers to make decisions about forest management and conservation, as well as to ensure that Europe's forests are managed in an ecologically sustainable way.

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The Conquest of Quantum Genetic Algorithms: The Adventure to Cross the Valley of Death

arXiv

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arXiv 2023年

作者： Lahoz-Beltra, Rafael Faculty of Biological Sciences Complutense University of Madrid Madrid28040 Spain Modeling Data Analysis and Computational Tools for Biology Research Group Complutense University of Madrid Madrid28040 Spain

In recent years, the emergence of the first quantum computers at a time when AI is undergoing a fruitful era has led many AI researchers to be tempted into adapting their algorithms to run on a quantum computer. However, in many cases the initial enthusiasm has ended in frustration, since the features and principles underlying quantum computing are very different from traditional computers. In this paper, we present a discussion of the difficulties arising when designing a quantum version of an evolutionary algorithm based on Darwin's evolutionary mechanism, the so-called genetic algorithms. The paper includes the code in both Python and QISKIT of the quantum version of one of these evolutionary algorithms allowing the reader to experience the setbacks arising when translating a classical algorithm to its quantum version. The algorithm studied in this paper, termed RQGA (Reduced Quantum Genetic Algorithm), has been chosen as an example that clearly shows these difficulties, which are common to other AI algorithms. © 2023, CC BY.

关键词： Genetic algorithms

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Iteration problem for several chaos in non-autonomous discrete system

arXiv

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arXiv 2024年

作者： Zeng, Hongbo Huang, Chuangxia Liu, Bingwen School of Mathematics and Statistics Changsha University of Science and Technology Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering Hunan Changsha410114 China College of Data Science Jiaxing University Zhejiang Jiaxing314001 China

In this paper we investigate the iteration problem for several chaos in non-autonomous discrete system (X, f1,∞). Firstly, we prove that the Li-Yorke chaos of a non-autonomous discrete dynamical system is preserved under iterations when f1,∞ converges to f, which weakens the condition in the literature that f1,∞ uniformly converges to f . Besides, we prove that both DC2’ and Kato’s chaos of a non-autonomous discrete dynamical system are iteration invariants. Additionally, we give a sufficient condition for non-autonomous discrete dynamical system to be Li-Yorke chaos. Finally, we give an example to show that the DC3 of a non-autonomous discrete dynamical system is not inherited under iterations, which partly answers an open question proposed by Wu and Zhu(Chaos in a class of non-autonomous discrete systems, ***. 2013,26:431-436). Copyright © 2024, The Authors. All rights reserved.

关键词： Dynamical systems

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Statistical inference for smoothed quantile regression with streaming data

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Journal of Econometrics 2025年 249卷

作者： Xie, Jinhan Yan, Xiaodong Jiang, Bei Kong, Linglong Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming650091 China Department of Mathematical and Statistical Sciences University of Alberta Edmonton AlbertaT6G 2G1 Canada School of Mathematics and Statistics Xi'an Jiaotong University China

In this paper, we tackle the problem of conducting valid statistical inference for quantile regression with streaming data. The main difficulties are that the quantile regression loss function is non-smooth and it is often infeasible to store the entire dataset in memory, rendering traditional methodologies ineffective. We introduce a fully online updating method for statistical inference in smoothed quantile regression with streaming data to overcome these issues. Our main contributions are twofold. First, for low-dimensional data, we present an incremental updating algorithm to obtain the smoothed quantile regression estimator with the streaming data set. The proposed estimator allows us to construct asymptotically exact statistical inference procedures. Second, within the realm of high-dimensional data, we develop an online debiased lasso procedure to accommodate the special sparse structure of streaming data. The proposed online debiased approach is updated with only the current data and summary statistics of historical data and corrects an approximation error term from online updating with streaming data. Furthermore, theoretical results such as estimation consistency and asymptotic normality are established to justify its validity in both settings. Our findings are supported by simulation studies and illustrated through applications to Seoul's bike-sharing demand data and index fund data. © 2024

关键词： Logistic regression

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