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arXiv 2025年

作者： Alber, Maximilian Tietz, Stephan Dippel, Jonas Milbich, Timo Lesort, Timothée Korfiatis, Panos Krügener, Moritz Cancer, Beatriz Perez Shah, Neelay Möllers, Alexander Seegerer, Philipp Carpen-Amarie, Alexandra Standvoss, Kai Dernbach, Gabriel de Jong, Edwin Schallenberg, Simon Kunft, Andreas von Ankershoffen, Helmut Hoffer Schaeferle, Gavin Duffy, Patrick Redlon, Matt Jurmeister, Philipp Horst, David Ruff, Lukas Müller, Klaus-Robert Klauschen, Frederick Norgan, Andrew Aignostics Germany Department of Laboratory Medicine and Pathology Mayo Clinic RochesterMN United States Department of Radiology Mayo Clinic RochesterMN United States Department of Information Technology Mayo Clinic RochesterMN United States Systems Quality Office Mayo Clinic RochesterMN United States Machine Learning Group Technische Universität Berlin Germany BIFOLD Berlin Institute for the Foundations of Learning and Data Germany Department of Artificial Intelligence Korea University Korea Republic of Max-Planck Institute for Informatics Germany Berlin & Munich Partner Sites Germany Institute of Pathology Ludwig-Maximilians-Universität München Germany Institute of Pathology Charité – Universitätsmedizin Berlin Germany Germany

Recent advances in digital pathology have demonstrated the effectiveness of foundation models across diverse applications. In this report, we present Atlas, a novel vision foundation model based on the RudolfV approach. Our model was trained on a dataset comprising 1.2 million histopathology whole slide images, collected from two medical institutions: Mayo Clinic and Charité - Universtätsmedizin Berlin. Comprehensive evaluations show that Atlas achieves state-of-the-art performance across twenty-one public benchmark datasets, even though it is neither the largest model by parameter count nor by training dataset size. © 2025, CC BY-NC-ND.

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A new nano-scale authentication architecture for improving the security of human-computer interaction systems based on quantum computing

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Analog Integrated Circuits and Signal Processing 2025年第1期124卷 1-15页

作者： Ahmadpour, Seyed-Sajad Zohaib, Muhammad Navimipour, Nima Jafari Misra, Neeraj Kumar Rasmi, Hadi Salahov, Huseyn Avval, Danial Bakhshayeshi Navin, Ahmad Habibizad Hosseinzadeh, Mehdi Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Turkiye Istanbul Turkey Department of Electrical and Electronics Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravathi522237 India Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Systems Engineering Sakarya University Türkiye Sakarya Turkey Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz*** Iran Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam Korea Republic of

Human-Computer Interaction (HCI) is an interdisciplinary area of study focusing on the interaction of users and computers by scheming interactive computer interfaces. In addition, HCI systems need security to confirm user authentication, which is a crucial issue in these systems. Hence, user authentication is vital, allowing only authorized users to access data. Authentication is critical to the digital world since it provides security and safety for digital data. Moreover, a digital signature is an authentication method to confirm the accuracy and reliability of digital documents or communications. In addition, designing the circuit based on the complementary metal-oxide semiconductor (CMOS) technology can affect the security and safety of digital data due to the excessive heat dissipation of circuits. On the other hand, quantum-dot cellular automata (QCA) and reversible logic as alternative technologies to CMOS address these problems. Since QCA and reversible logic circuits have minimal energy dissipation, which is considered nearly zero, approaching these technologies proves extremely difficult for any hacker. This work presents an effective structure for the authenticator and human-computer interaction using QCA and IBM quantum computing with Qiskit simulations. The proposed structure has outperformed current circuits in terms of area, cell count, and latency. The paper demonstrates the QCA reversible logic layout of the proposed HCI authenticator and integrates IBM quantum computing simulations using Qiskit for validation. The implementation and testing of results are performed utilizing QCADesigner-2.0.3 and Qiskit simulation tools. The accuracy and efficiency of the proposed design are validated through simulation-derived comparison values, and energy dissipation simulations prove that the suggested circuit dissipates minimal energy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Qubits

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Stochastic variance reduction for nonconvex optimization 33

Stochastic variance reduction for nonconvex optimization

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33rd International Conference on machine learning, ICML 2016

作者： Reddi, Sashank J. Hefny, Ahmed Sra, Suvrit Póczós, Barnabás Smola, Alex Machine Learning Department School of Computer Science Carnegie Mellon University United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology United States

ISBN: (纸本)9781510829008

We study nonconvex finite-sum problems and analyze stochastic variance reduced gradient (SVRG) methods for them. SVRG and related methods have recently surged into prominence for convex optimization given their edge over stochastic gradient descent (SGD);but their theoretical analysis almost exclusively assumes convexity. In contrast, we obtain non-asymptotic rates of convergence of SVRG for nonconvex optimization, showing that it is provably faster than SGD and gradient descent. We also analyze a subclass of nonconvex problems on which svrg attains linear convergence to the global optimum. We extend our analysis to mini-batch variants, showing (theoretical) linear speedup due to minibatching in parallel settings.

关键词： Convex optimization

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Pipe localization by apex detection

Pipe localization by apex detection

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IET International Conference on Radar systems (Radar 2012)

作者： R. Janning T. Horváth A. Busche L. Schmidt-Thieme Information Systems and Machine Learning Laboratory University of Hildesheim Hildesheim Germany

Ground penetrating radar is used to scan the shallow subsurface for detecting buried objects like pipes without corrupting the road surface. Buried objects are represented by hyperbola branches on GPR radargram images. As the manually interpretation of such radargrams is expensive and time consuming, an important goal in this field is to automatize the pipe localization process. A novel approach which is able to automatically estimate the number of buried objects, their horizontal position and their depth from a radargram is presented in this paper. Additionally, this approach delivers the reflection patterns of the buried objects, which may help to estimate their material. The core of our method is a specialised clustering algorithm which detects apexes with appendant hyperbola branch shaped clusters and ignores noise. A hyperbola fitting algorithm delivering hyperbola parameters is applied to this clusters. The pipe locations are estimated by means of the clusters found and the apexes of the fitted hyperbola branches.

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A new evaluation measure for learning from imbalanced data

A new evaluation measure for learning from imbalanced data

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International Joint Conference on Neural Networks (IJCNN)

作者： Nguyen Thai-Nghe Zeno Gantner Lars Schmidt-Thieme Information Systems and Machine Learning Laboratory University of Hildesheim Hildesheim Germany

Recently, researchers have shown that the Area Under the ROC Curve (AUC) has a serious deficiency since it implicitly uses different misclassification cost distributions for different classifiers. Thus, using the AUC can be compared to using different metrics to evaluate different classifiers [1]. To overcome this incoherence, the H measure was proposed, which uses a symmetric Beta distribution to replace the implicit cost weight distribution in the AUC. When learning from imbalanced data, misclassifying a minority class example is much more serious than misclassifying a majority class example. To take different misclassification costs into account, we propose using an asymmetric Beta distribution (B42) instead of a symmetric one. Experimental results on 36 imbalanced data sets using SVMs and logistic regression show that B42 is a good choice for evaluating on imbalanced data sets because it puts more weight on the minority class. We also show that balanced random undersampling does not work for large and highly imbalanced data sets, although it has been reported to be effective for small data sets.

关键词： Measurement Accuracy Support vector machines Degradation Terrorism information systems Educational institutions

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Cost-sensitive learning methods for imbalanced data

Cost-sensitive learning methods for imbalanced data

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International Joint Conference on Neural Networks (IJCNN)

作者： Nguyen Thai-Nghe Zeno Gantner Lars Schmidt-Thieme Information Systems and Machine Learning Laboratory University of Hildesheim Hildesheim Germany

Class imbalance is one of the challenging problems for machine learning algorithms. When learning from highly imbalanced data, most classifiers are overwhelmed by the majority class examples, so the false negative rate is always high. Although researchers have introduced many methods to deal with this problem, including resampling techniques and cost-sensitive learning (CSL), most of them focus on either of these techniques. This study presents two empirical methods that deal with class imbalance using both resampling and CSL. The first method combines and compares several sampling techniques with CSL using support vector machines (SVM). The second method proposes using CSL by optimizing the cost ratio (cost matrix) locally. Our experimental results on 18 imbalanced datasets from the UCI repository show that the first method can reduce the misclassification costs, and the second method can improve the classifier performance.

关键词： Support vector machines Rain Measurement Nearest neighbor searches Kernel Noise Cancer

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IQ estimation for accurate time-series classification

IQ estimation for accurate time-series classification

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Krisztian Buza Alexandros Nanopoulos Lars Schmidt-Thieme Information Systems and Machine Learning Laboratory University of Hildesheim Hildesheim Germany

Due to its various applications, time-series classification is a prominent research topic in data mining and computational intelligence. The simple k-NN classifier using dynamic time warping (DTW) distance had been shown to be competitive to other state-of-the art time-series classifiers. In our research, however, we observed that a single fixed choice for the number of nearest neighbors k may lead to suboptimal performance. This is due to the complexity of time-series data, especially because the characteristic of the data may vary from region to region. Therefore, local adaptations of the classification algorithm is required. In order to address this problem in a principled way by, in this paper we introduce individual quality (IQ) estimation. This refers to estimating the expected classification accuracy for each time series and each k individually. Based on the IQ estimations we combine the classification results of several k-NN classifiers as final prediction. In our framework of IQ, we develop two time-series classification algorithms, IQ-MAX and IQ-WV. In our experiments on 35 commonly used benchmark data sets, we show that both IQ-MAX and IQ-WV outperform two baselines.

关键词： Estimation Time series analysis Predictive models Training Accuracy Computational modeling Training data

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Improving Academic Performance Prediction by Dealing with Class Imbalance

Improving Academic Performance Prediction by Dealing with Cl...

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International Conference on Intelligent systems Design and Applications (ISDA)

作者： Nguyen Thai-Nghe Andre Busche Lars Schmidt-Thieme Information Systems and Machine Learning Laboratory University of Hildesheim Hildesheim Germany

This paper introduces and compares some techniques used to predict the student performance at the university. Recently, researchers have focused on applying machine learning in higher education to support both the students and the instructors getting better in their performances. Some previous papers have introduced this problem but the prediction results were unsatisfactory because of the class imbalance problem, which causes the degradation of the classifiers. The purpose of this paper is to tackle the class imbalance for improving the prediction/classification results by over-sampling techniques as well as using cost-sensitive learning (CSL). The paper shows that the results have been improved when comparing with only using baseline classifiers such as Decision Tree (DT), Bayesian Networks (BN), and Support Vector machines (SVM) to the original datasets.

关键词： machine learning Degradation Support vector machines Support vector machine classification Data mining Nearest neighbor searches Intelligent systems learning systems information systems Classification tree analysis

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Time-Series Classification Based on Individualised Error Prediction

Time-Series Classification Based on Individualised Error Pre...

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IEEE International Conference on Computational Science and Engineering, CSE

作者： Krisztian Buza Alexandros Nanopoulos Lars Schmidt-Thieme Information Systems and Machine Learning Laboratory University of Hildesheim Hildesheim Germany

Time-series classification is an active research topic in machine learning, as it finds applications in numerous domains. The k-NN classifier, based on the discrete time warping (DTW) distance, had been shown to be competitive to many state-of-the art time-series classification methods. Nevertheless, due to the complexity of time-series data sets, our investigation demonstrates that a single, global choice for k (≥ 1) can become suboptimal, because each individual region of a data set may require a different k value. In this paper, we proposed a novel individualized error prediction (IEP) mechanism that considers a range of k-NN classifiers (for different k values) and uses secondary regression models that predict the error of each such classifier. This permits to perform k-NN time-series classification in a more fine grained fashion that adapts to the varying characteristics among different regions by avoiding the restriction of a single value of k. Our experimental evaluation, using a large collection of real timeseries data, indicates that the proposed method is more robust and compares favorably against two examined baselines by resulting in significant reduction in the classification error.

关键词： Time series analysis Predictive models Training Accuracy Nearest neighbor searches Data models Training data

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Active learning for aspect model in recommender systems

Active learning for aspect model in recommender systems

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： Rasoul Karimi Christoph Freudenthaler Alexandros Nanopoulos Lars Schmidt-Thieme Information Systems and Machine Learning Laboratory (ISMLL) University of Hildesheim Hildesheim Germany

Recommender systems help Web users to address information overload. Their performance, however, depends on the amount of information that users provide about their preferences. Users are not willing to provide information for a large amount of items, thus the quality of recommendations is affected specially for new users. Active learning has been proposed in the past, to acquire preference information from users. Based on an underlying prediction model, these approaches determine the most informative item for querying the new user to provide a rating. In this paper, we propose a new active learning method which is developed specially based on aspect model features. There is a difference between classic active learning and active learning for recommender system. In the recommender system context, each item has already been rated by training users while in classic active learning there is not training user. We take into account this difference and develop a new method which competes with a complicated bayesian approach in accuracy while results in drastically reduced (one order of magnitude) user waiting times, i.e., the time that the users wait before being asked a new query.

关键词： Recommender systems Bayesian methods Mathematical model Training Predictive models Accuracy Equations

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