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Curation at the point of measurement and traceability of measurement workflows

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Measurement: Sensors 2022年 23卷

作者： Thomas, Spencer A. Brochu, Frederic Data Science National Physical Laboratory United Kingdom Department of Computer Science University of Surrey United Kingdom

In this paper we introduce a method that can digitally capture machine actionable metadata, tag them to the associated measurement data, and upload to a curated database. Our method is packaged as a tool to enable scientists to capture and store curated data at the point of measurement. By ‘data’ we include the primary measurement and any associated information such as calibration data, processing/analysis scripts, multi-modal data, etc. Combining the associated data together enhances re-usability through metadata and confidence though calibration data. We extend this process by adding new data at each stage of the data capture and analysis workflow to develop a completely traceable data processing pipeline. We achieve this by cumulatively updating the ‘data’ at each stage and by using versioning in our database for complete generality. Here each version is a self-contained curated container of all relevant data and codes providing a reproducible ‘snapshot’ in a traceable analytical pipeline. Within each ‘snapshot’ we store the outputs from the relevant data analysis (figures, models, hypothesis tests, etc), the raw data, and each step (codes, converters, etc) between them, resulting in a fully transparent and reproducible workflow. The ‘snapshots’ are updated along the analytical pipeline and we demonstrate this with several steps including: at the point of measurement;conversion to an open format;pre-processing (feature selection, noise reduction, etc);and data analysis. We demonstrate our method with a large cohort of mass spectrometry imaging experiments as an exemplar case study. © 2022 The Authors

关键词： Metadata

An Incremental Multi-Level, Multi-Scale Approach to Assessment of Multifidelity HPC Systems

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arXiv 2025年

作者： Shilpika Lusch, Bethany Vishwanath, Venkatram Papka, Michael E. Argonne Leadership Computing Facility Argonne National Laboratory United States Argonne Leadership Computing Facility Argonne National Laboratory Department of Computer Science University of Illinois Chicago United States

With the growing complexity in architecture and the size of large-scale computing systems, monitoring and analyzing system behavior and events has become daunting. Monitoring data amounting to terabytes per day are collected by sensors housed in these massive systems at multiple fidelity levels and varying temporal resolutions. In this work, we develop an incremental version of multiresolution dynamic mode decomposition (mrDMD), which converts high-dimensional data to spatial-temporal patterns at varied frequency ranges. Our incremental implementation of the mrDMD algorithm (I-mrDMD) promptly reveals valuable information in the massive environment log dataset, which is then visually aligned with the processed hardware and job log datasets through our generalizable rack visualization using D3 visualization integrated into the Jupyter Notebook interface. We demonstrate the efficacy of our approach with two use scenarios on a real-world dataset from a Cray XC40 supercomputer, Theta. © 2025, CC BY.

关键词： Large scale systems

Powerformer: A Transformer with Weighted Causal Attention for Time-series Forecasting

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arXiv 2025年

作者： Hegazy, Kareem Mahoney, Michael W. Erichson, N. Benjamin Department of Statistics University of California Berkeley United States International Computer Science Institute Berkeley United States Lawrence Berkeley National Laboratory United States

Transformers have recently shown strong performance in time-series forecasting, but their all-to-all attention mechanism overlooks the (temporal) causal and often (temporally) local nature of data We introduce Powerformer, a novel Transformer variant that replaces noncausal attention weights with causal weights that are reweighted according to a smooth heavy-tailed decay. This simple yet effective modification endows the model with an inductive bias favoring temporally local dependencies, while still allowing sufficient flexibility to learn the unique correlation structure of each dataset. Our empirical results demonstrate that Powerformer not only achieves state-of-the-art accuracy on public time-series benchmarks, but also that it offers improved interpretability of attention patterns. Our analyses show that the model’s locality bias is amplified during training, demonstrating an interplay between time-series data and power-law-based attention. These findings highlight the importance of domain-specific modifications to the Transformer architecture for time-series forecasting, and they establish Powerformer as a strong, efficient, and principled baseline for future research and real-world applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Time series analysis

Time-Synchronized Safety-Critical Attitude Control for Spacecraft with Hardware-in-Loop Validation

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Time-Synchronized Safety-Critical Attitude Control for Space...

2024 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2024

作者： Tong, Shang Yang, Haoyang Zhang, Rongchuan Hu, Qinglei Sam Ge, Shuzhi Li, Dongyu Tianmushan Laboratory Hangzhou China School of Cyber Science and Technology Beihang University Beijing China School of Engineering University of Warwick Coventry United Kingdom Institute Beihang University Hangzhou International Innovation Hangzhou China School of Automation Science and Electrical Engineering Beihang University Beijing China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9798331504755

A time-synchronized controller is proposed to solve the time-varying constrained reorientation problem. Time-Synchronized Stability (TSS) that considers the interrelationships among different state errors plays an important role in our method, enabling synchronized convergence of individual error components while ensuring compliance with state constraints. Compared to traditional sliding mode controllers, the efficiency of our safety-critical attitude controller is imporoved without adding computational burden, as evidenced by improved control energy consumption and reduced convergence time. A hardware-in-the-loop (HiL) experiment confirms its practical deployment capabilities. © 2024 IEEE.

关键词： Sliding mode control

Adaptive energy-preserving algorithms for guiding center system

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Plasma science and Technology 2023年第4期25卷 11-22页

作者：朱贝贝刘健张嘉炜祝爱卿唐贻发 Department of Applied Mathematics School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083Peopleʼs Republic of China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026Peopleʼs Republic of China Advanced algorithm Joint Lab Shandong Computer Science CenterQilu University of TechnologyJinan 250014Peopleʼs Republic of China The State Key Laboratory of Scientific and Engineering Computing The Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190Peopleʼs Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049Peopleʼs Republic of China

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

关键词： guiding center system energy-preserving methods adaptive energy conservation

Switching Controller Synthesis for Hybrid Systems Against STL Formulas

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arXiv 2024年

作者： Su, Han Feng, Shenghua Zhan, Sinong Zhan, Naijun Institute of Software CAS University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Department of Electrical and Computer Engineering Northwestern University United States School of Computer Science Peking University Beijing China

Switching controllers play a pivotal role in directing hybrid systems (HSs) towards the desired objective, embodying a "correct-by-construction" approach to HS design. Identifying these objectives is thus crucial for the synthesis of effective switching controllers. While most of existing works focus on safety and liveness, few of them consider timing constraints. In this paper, we delves into the synthesis of switching controllers for HSs that meet system objectives given by a fragment of STL, which essentially corresponds to a reach-avoid problem with timing constraints. Our approach involves iteratively computing the state sets that can be driven to satisfy the reach-avoid specification with timing constraints. This technique supports to create switching controllers for both constant and non-constant HSs. We validate our method’s soundness, and confirm its relative completeness for a certain subclass of HSs. Experiment results affirms the efficacy of our approach. © 2024, CC BY.

关键词： Hybrid systems

DeepCell: Multiview Representation Learning for Post-Mapping Netlists

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arXiv 2025年

作者： Shi, Zhengyuan Ma, Chengyu Zheng, Ziyang Zhou, Lingfeng Pan, Hongyang Jiang, Wentao Yang, Fan Yang, Xiaoyan Chu, Zhufei Xu, Qiang Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China School of Computer Science Hangzhou Dianzi University Hangzhou China School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University Shanghai China National Center of Technology Innovation for EDA Nanjing China

Representation learning for post-mapping (PM) netlists is a critical challenge in Electronic Design Automation (EDA), driven by the diverse and complex nature of modern circuit designs. Existing approaches focus on intermediate representations like And-Inverter Graphs (AIGs), limiting their applicability to post-synthesis stages. We introduce DeepCell, a multiview representation learning framework that integrates structural and functional insights from both PM netlists and AIGs to learn rich, generalizable embeddings. At its core, DeepCell employs the novel Mask Circuit Modeling (MCM) mechanism, which refines PM netlist representations in a self-supervised manner using pretrained AIG encoders. DeepCell sets a new benchmark in PM netlist representation, outperforming existing methods in predictive accuracy and reconstruction fidelity. To validate its efficacy, we apply DeepCell to functional Engineering Change Orders (ECO), achieving significant reductions in patch generation costs and runtime while improving patch quality. Copyright © 2025, The Authors. All rights reserved.

关键词： Mapping

Auto-Linear Phenomenon in Subsurface Imaging 41

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Auto-Linear Phenomenon in Subsurface Imaging

41st International Conference on Machine Learning, ICML 2024

作者： Feng, Yinan Chen, Yinpeng Jin, Peng Feng, Shihang Lin, Youzuo Department of Computer Science The University of North Carolina Chapel Hill United States Google Research United States College of Information Sciences and Technology The Pennsylvania State University United States Earth and Environmental Sciences Division Los Alamos National Laboratory United States School of Data Science and Society The University of North Carolina Chapel Hill United States

Subsurface imaging involves solving full waveform inversion (FWI) to predict geophysical properties from measurements. This problem can be reframed as an image-to-image translation, with the usual approach being to train an encoder-decoder network using paired data from two domains: geophysical property and measurement. A recent seminal work (InvLINT) demonstrates there is only a linear mapping between the latent spaces of the two domains, and the decoder requires paired data for training. This paper extends this direction by demonstrating that only linear mapping necessitates paired data, while both the encoder and decoder can be learned from their respective domains through self-supervised learning. This unveils an intriguing phenomenon (named Auto-Linear) where the self-learned features of two separate domains are automatically linearly correlated. Compared with existing methods, our Auto-Linear has four advantages: (a) solving both forward and inverse modeling simultaneously, (b) applicable to different subsurface imaging tasks and achieving markedly better results than previous methods, (c)enhanced performance, especially in scenarios with limited paired data and in the presence of noisy data, and (d) strong generalization ability of the trained encoder and decoder. Copyright 2024 by the author(s)

关键词： Subsalt imaging

SJA: Server-driven Joint Adaptation of Loss and Bitrate for Multi-Party Realtime Video Streaming 42

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SJA: Server-driven Joint Adaptation of Loss and Bitrate for ...

42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Shen, Kai Zhang, Dayou Zhu, Zi Zhang, Lei Wang, Fangxin Wang, Dan The Chinese University of HongKong The Future Network of Intelligence Institute Shenzhen China The Chinese University of HongKong School of Science and Engineering Shenzhen China Shenzhen University College of Computer Science and Software Engineering China Peng Cheng Laboratory China The Guangdong Provincial Key Laboratory of Future Networks of Intelligence China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350334142

The outbreak of COVID-19 has dramatically promoted the explosive proliferation of multi-party realtime video streaming (MRVS) services, represented by Zoom and Microsoft Teams. Different from Video-on-Demand (VoD) or live streaming, MRVS enables all-to-all realtime video communication, bringing significant challenges to service providing. First, unreliable network transmission can cause network loss, resulting in delay increase and visual quality degradation. Second, the transformation from two-party to multi-party communication makes resource scheduling much more difficult. Moreover, optimizing the overall QoE requires a global coordination, which is quite challenging given the various impact factors such as bitrate and *** this paper, we propose the SJA framework, which is, to our best knowledge, the first server-driven joint loss and bitrate adaptation framework in multi-party realtime video streaming services towards maximized QoE. We comprehensively design an appropriate QoE model for MRVS services to capture the interplay among perceptual quality, variations, bitrate mismatch, loss damage, and streaming delay. We mathematically formulate the QoE maximization problem in MRVS services. A Lyapunov-based relaxation and the SJA algorithm are further designed to address the optimization problem with close-to-optimal performance. Evaluations show that our framework can outperform the SOTA solutions by 18.4% ∼ 46.5%. © 2023 IEEE.

关键词： Quality control