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arXiv 2022年

作者： Dong, Hexin Chen, Zifan Yuan, Mingze Xie, Yutong Zhao, Jie Yu, Fei Dong, Bin Zhang, Li Center for Data Science Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China Center for Machine Learning Research Peking University Beijing China Peking University Beijing China

As one of the most challenging and practical segmentation tasks, open-world semantic segmentation requires the model to segment the anomaly regions in the images and incrementally learn to segment out-of-distribution (OOD) objects, especially under a few-shot condition. The current state-of-the-art (SOTA) method, Deep Metric learning Network (DMLNet), relies on pixel-level metric learning, with which the identification of similar regions having different semantics is difficult. Therefore, we propose a method called region-aware metric learning (RAML), which first separates the regions of the images and generates region-aware features for further metric learning. RAML improves the integrity of the segmented anomaly regions. Moreover, we propose a novel meta-channel aggregation (MCA) module to further separate anomaly regions, forming high-quality sub-region candidates and thereby improving the model performance for OOD objects. To evaluate the proposed RAML, we have conducted extensive experiments and ablation studies on Lost And Found and Road Anomaly datasets for anomaly segmentation and the CityScapes dataset for incremental few-shot learning. The results show that the proposed RAML achieves SOTA performance in both stages of open world segmentation. Our code and appendix are available at https://***/czifan/RAML. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Accelerating crystal structure search through active learning with neural networks for rapid relaxations

arXiv

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arXiv 2024年

作者： Hessmann, Stefaan S.P. Schütt, Kristof T. Gebauer, Niklas W.A. Gastegger, Michael Oguchi, Tamio Yamashita, Tomoki Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany BASLEARN TU Berlin BASF Joint Lab for Machine Learning Technische Universität Berlin Berlin10587 Germany Center for Spintronics Research Network Osaka University 1-3 Machikaneyama Toyonaka Osaka560-8531 Japan Department of Electrical Electronics and Information Engineering Nagaoka University of Technology 1603-1 Kamitomioka-machi Nagaoka Niigata940-2188 Japan

Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space. The specific physical properties linked to a three-dimensional atomic arrangement make this an essential task in the development of new materials. We present a method that efficiently uses active learning of neural network force fields for structure relaxation, minimizing the required number of steps in the process. This is achieved by neural network force fields equipped with uncertainty estimation, which iteratively guide a pool of randomly generated candidates towards their respective local minima. Using this approach, we are able to effectively identify the most promising candidates for further evaluation using density functional theory (DFT). Our method not only reliably reduces computational costs by up to two orders of magnitude across the benchmark systems Si16, Na8Cl8, Ga8As8 and Al4O6, but also excels in finding the most stable minimum for the unseen, more complex systems Si46 and Al16O24. Moreover, we demonstrate at the example of Si16 that our method can find multiple relevant local minima while only adding minor computational effort. Copyright © 2024, The Authors. All rights reserved.

关键词： Density functional theory

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Design and Implementation of a data Governance Framework and Platform: A Case Study of a National research Organization of Thailand

Design and Implementation of a Data Governance Framework and...

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International Joint Conference on Computer Science and Software Engineering (JCSSE)

作者： Sapa Chanyachatchawan Krich Nasingkun Patipat Tumsangthong Porntiwa Chata Marut Buranarach Monsak Socharoentum Leveraging Technology Solutions Section National Electronics and Computer Technology Center Bangkok Thailand Strategic Analytics Networks with Machine Learning and AI Research National Electronics and Computer Technology Center Bangkok Thailand Data Science and Analytics Research Group National Electronics and Computer Technology Center Bangkok Thailand Digital Government Development Agency Bangkok Thailand

In the current era of extensive data usage across industries, data collection, preservation, utilization, and organization has become more challenging and nuanced because it is necessary to consider critical concerns such as data security, privacy, and legal issues, apart from efficiency issues. As a result, Thai government initiated the idea and effort to implement data governance throughout the government agency. This paper showcases the implementation of data governance in a governmental research organization with highly diverse structured and unstructured data. The implementation follows international standards and the guidelines of the Digital Government Development Agency (DGA). The executives set up the working body, including the data Governance Council and data Stewards, responsible for setting up and deploying policies and regulations. Creating awareness and the necessary infrastructure are the main focuses in the first-year phase. The metadata was designed to extend DGA's version and match the organization's unique requirements. A data catalog platform was developed accordingly. We organized activities to boost employee awareness and participation, including advertising and data catalog platform training. By the end of the first year of implementation, every organization unit had registered at least one data record into the data catalog.

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Flow-Based Sampling for Entanglement Entropy and the machine learning of Defects

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Physical Review Letters 2025年第15期134卷 151601-151601页

作者： Andrea Bulgarelli Elia Cellini Karl Jansen Stefan Kühn Alessandro Nada Shinichi Nakajima Kim A. Nicoli Marco Panero Department of Physics University of Turin and INFN Turin unit Via Pietro Giuria 1 I-10125 Turin Italy Computation-Based Science and Technology Research Center The Cyprus Institute Nicosia Cyprus Deutsches Elektronen-Synchrotron DESY Zeuthen Germany Berlin Institute for the Foundations of Learning and Data (BIFOLD) Berlin Germany Machine Learning Group Technische Universität Berlin Berlin Germany RIKEN Center for AIP Tokyo Japan Transdisciplinary Research Area (TRA) Matter University of Bonn Germany Helmholtz Institute for Radiation and Nuclear Physics (HISKP) Bonn Germany Department of Physics and Helsinki Institute of Physics PL 64 FIN-00014 University of Helsinki Finland

We introduce a novel technique to numerically calculate Rényi entanglement entropies in lattice quantum field theory using generative models. We describe how flow-based approaches can be combined with the replica trick using a custom neural-network architecture around a lattice defect connecting two replicas. Numerical tests for the ϕ4 scalar field theory in two and three dimensions demonstrate that our technique outperforms state-of-the-art Monte Carlo calculations, and exhibit a promising scaling with the defect size.

关键词： Lattice theory

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On the Mathematics of RNA Velocity II: Algorithmic Aspects

arXiv

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arXiv 2023年

作者： Li, Tiejun Wang, Yizhuo Yang, Guoguo Zhou, Peijie LMAM School of Mathematical Sciences Peking University Beijing100871 China Center for Machine Learning Research Peking University Beijing100871 China Center for Data Science Peking University Beijing100871 China Department of Mathematics University of California Irvine IrvineCA92697 United States

In a previous paper [CSIAM Trans. Appl. Math. 2 (2021), 1-55], the authors proposed a theoretical framework for the analysis of RNA velocity, which is a promising concept in scRNA-seq data analysis to reveal the cell state-transition dynamical processes underlying snapshot data. The current paper is devoted to the algorithmic study of some key components in RNA velocity workflow. Four important points are addressed in this paper: (1) We construct a rational time-scale fixation method which can determine the global gene-shared latent time for cells. (2) We present an uncertainty quantification strategy for the inferred parameters obtained through the EM algorithm. (3) We establish the optimal criterion for the choice of velocity kernel bandwidth with respect to the sample size in the downstream analysis and discuss its implications. (4) We propose a temporal distance estimation approach between two cell clusters along the cellular development path. Some illustrative numerical tests are also carried out to verify our analysis. These results are intended to provide tools and insights in further development of RNA velocity type methods in the future. © 2023, CC BY-NC-SA.

关键词： RNA

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The Conditional Cauchy-Schwarz Divergence With Applications to Time-Series data and Sequential Decision Making

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IEEE Transactions on Pattern analysis and machine Intelligence 2025年第7期47卷 5901-5917页

作者： Shujian Yu Hongming Li Sigurd Løkse Robert Jenssen José C. Príncipe Machine Learning Group UiT - The Arctic University of Norway Tromsø Norway Quantitative Data Analytics Group Vrije Universiteit Amsterdam Amsterdam The Netherlands Department of Electrical and Computer Engineering University of Florida Gainesville FL USA Drones and Autonomous Systems Group NORCE Norwegian Research Centre Tromsø Norway Pioneer AI Centre Copenhagen University København Denmark Norwegian Computing Center Oslo Norway

The Cauchy-Schwarz (CS) divergence was developed by Príncipe et al. in 2000. In this paper, we extend the classic CS divergence to quantify the closeness between two conditional distributions and show that the developed conditional CS divergence can be elegantly estimated by a kernel density estimator from given samples. We illustrate the advantages (e.g., rigorous faithfulness guarantee, lower computational complexity, higher statistical power, and much more flexibility in a wide range of applications) of our conditional CS divergence over previous proposals, such as the conditional Kullback-Leibler divergence and the conditional maximum mean discrepancy. We also demonstrate the compelling performance of conditional CS divergence in two machine learning tasks related to time series data and sequential inference, namely time series clustering and uncertainty-guided exploration for sequential decision making.

关键词： Covariance matrices Kernel machine learning Time series analysis Vectors Symmetric matrices Q measurement Matrix decomposition Estimation Decision making

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HmBERT: Historical Multilingual Language Models for Named Entity Recognition

arXiv

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arXiv 2022年

作者： Schweter, Stefan März, Luisa Schmid, Katharina Çano, Erion Bayerische Staatsbibliothek München Digital Library/ Munich Digitization Center Munich Germany Digital Philology Research Group Data Mining and Machine Learning University of Vienna Austria Natural Language Processing Expert Center Data:Lab Volkswagen AG Munich Germany

Compared to standard Named Entity Recognition (NER), identifying persons, locations, and organizations in historical texts constitutes a big challenge. To obtain machine-readable corpora, the historical text is usually scanned and Optical Character Recognition (OCR) needs to be performed. As a result, the historical corpora contain errors. Also, entities like location or organization can change over time, which poses another challenge. Overall, historical texts come with several peculiarities that differ greatly from modern texts and large labeled corpora for training a neural tagger are hardly available for this domain. In this work, we tackle NER for historical German, English, French, Swedish, and Finnish by training large historical language models. We circumvent the need for large amounts of labeled data by using unlabeled data for pretraining a language model. We propose hmBert, a historical multilingual BERT-based language model, and release the model in several versions of different sizes. Furthermore, we evaluate the capability of hmBert by solving downstream NER as part of this year's HIPE-2022 shared task and provide detailed analysis and insights. For the Multilingual Classical Commentary coarse-grained NER challenge, our tagger HISTeria outperforms the other teams' models for two out of three languages. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical character recognition

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SLSL-QPSO: Quantum-Behaved Particle Swarm Optimization with Short-Lived Swarm Layers

SSRN

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SSRN 2023年

作者： Liang, Kang Xiukai, Zhang Krakhmalev, Oleg Engineering Training and Innovation Education Center Shanghai Polytechnic University Shanghai201209 China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai201209 China Department of Data Analysis and Machine Learning Financial University Under the Government of the Russian Federation 4th Veshnyakovsky Passage 4 Moscow109456 Russia

SLSL-QPSO is a software that can find the optimal value of a function. It improves over the QPSO algorithms by leveraging the concept of living and death as swarm layers, the Lévy mutation, the scoped contradiction-expansion coefficient, and the selection of effective layers. Experimental results demonstrate that SLSL-QPSO has superior performance than QPSO and several other variants thus providing a competitive solution to optimization problems. © 2023, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Can Gaussian sketching converge faster on a preconditioned landscape? 24

Can Gaussian sketching converge faster on a preconditioned l...

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Proceedings of the 41st International Conference on machine learning

作者： Yilong Wang Haishan Ye Guang Dai Ivor W. Tsang Center for Intelligent Decision-Making and Machine Learning School of Management Xi'an Jiaotong University China and SGIT AI Lab State Grid Corporation of China SGIT AI Lab State Grid Corporation of China CFAR and IHPC Agency for Science Technology and Research (A*STAR) Singapore and College of Computing and Data Science NTU Singapore

This paper focuses on the large-scale optimization which is very popular in the big data era. The gradient sketching is an important technique in the large-scale optimization. Specifically, the random coordinate descent algorithm is a kind of gradient sketching method with the random sampling matrix as the sketching matrix. In this paper, we propose a novel gradient sketching called GSGD (Gaussian Sketched Gradient Descent). Compared with the classical gradient sketching methods such as the random coordinate descent and SEGA (Hanzely et al., 2018), our GSGD does not require the importance sampling but can achieve a fast convergence rate matching the ones of these methods with importance sampling. Furthermore, if the objective function has a non-smooth regularization term, our GSGD can also exploit the implicit structure information of the regularization term to achieve a fast convergence rate. Finally, our experimental results substantiate the effectiveness and efficiency of our algorithm.

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Attention-Based Early Warning Framework for Abnormal Operating Conditions in Fluid Catalytic Cracking Units

SSRN

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SSRN 2023年

作者： Chenwei, Tang Huang, Jialiang Xu, Mao Liu, Xu Yang, Fan Feng, Wentao He, Zhenan Lv, Jiancheng College of Computer Science Sichuan University Chengdu610065 China Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education Chengdu610065 China Algorithm and Bigdata Center Chengdu610041 China Data Intelligence Lab New Hope Liuhe Co.Ltd Chengdu610000 China

As a critical processing technology in the oil refining industry, Fluid Catalytic Cracking Unit (FCCU) plays a significant role in energy efficiency and environmental protection. However, FCCU often experiences various abnormal operating conditions, which can lead to safety hazards, downtime, and reduced production efficiency. Early warning of abnormal conditions in such complex industrial processes is essential yet challenging, especially when faced with high noise, strong hysteresis, and class imbalance problems. To address these challenges, we propose a universal attention-based early warning framework for abnormal operating conditions, called AEW-AOC, with applications in FCCU. Specifically, we propose a Self-Correlation Denoiser (SCD) module to reduce noise by utilizing spatiotemporal data correlation. Then, we employ a Convolutional Long Short-Term Memory (Conv-LSTM) module to solve the problem of strong hysteresis by extracting the temporal variation features of process parameters. Finally, based on the clustering results of historical abnormal instances, we propose an Anomaly Pattern Attention (APA) module to enhance the distinguishability of abnormal operating conditions. Extensive experiments demonstrate the effectiveness and superiority of our proposed AEW-AOC framework, especially in practical applications. The proposed universal early warning framework has the potential to enhance the safety, efficiency, and sustainability of industrial processes. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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