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Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weighting

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arXiv 2025年

作者： Feldman, Shai Bates, Stephen Romano, Yaniv Department of Computer Science Technion Israel Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Departments of Electrical and Computer Engineering and of Computer Science Technion Israel

We introduce a framework for robust uncertainty quantification in situations where labeled training data are corrupted, through noisy or missing labels. We build on conformal prediction, a statistical tool for generating prediction sets that cover the test label with a pre-specified probability. The validity of conformal prediction, however, holds under the i.i.d assumption, which does not hold in our setting due to the corruptions in the data. To account for this distribution shift, the privileged conformal prediction (PCP) method proposed leveraging privileged information (PI)—additional features available only during training—to re-weight the data distribution, yielding valid prediction sets under the assumption that the weights are accurate. In this work, we analyze the robustness of PCP to inaccuracies in the weights. Our analysis indicates that PCP can still yield valid uncertainty estimates even when the weights are poorly estimated. Furthermore, we introduce uncertain imputation (UI), a new conformal method that does not rely on weight estimation. Instead, we impute corrupted labels in a way that preserves their uncertainty. Our approach is supported by theoretical guarantees and validated empirically on both synthetic and real benchmarks. Finally, we show that these techniques can be integrated into a triply robust framework, ensuring statistically valid predictions as long as at least one underlying method is valid. © 2025, CC BY-NC-SA.

关键词： Uncertainty analysis

SISP: A Benchmark Dataset for Fine-grained Ship Instance Segmentation in Panchromatic Satellite Images

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arXiv 2024年

作者： Feng, Pengming Xie, Mingjie Liu, Hongning Zhao, Xuanjia He, Guangjun Zhang, Xueliang Guan, Jian The State Key Laboratory of Space-Ground Integrated Information Technology CAST Beijing100095 China College of Computer Science and Technology Harbin Engineering University Harbin150001 China The College of Electrical and Mechanical Engineering Dalian Minzu University Dalian116600 China The School of Geography and Ocean Science Nanjing University Nanjing210023 China

Fine-grained ship instance segmentation in satellite images holds considerable significance for monitoring maritime activities at sea. However, existing datasets often suffer from the scarcity of fine-grained information or pixel-wise localization annotations, as well as the insufficient image diversity and variations, thus limiting the research of this task. To this end, we propose a benchmark dataset for fine-grained Ship Instance Segmentation in Panchromatic satellite images, namely SISP, which contains 56,693 well-annotated ship instances with four fine-grained categories across 10,000 sliced images, and all the images are collected from SuperView-1 satellite with the resolution of 0.5m. Targets in the proposed SISP dataset have characteristics that are consistent with real satellite scenes, such as high class imbalance, various scenes, large variations in target densities and scales, and high inter-class similarity and intra-class diversity, all of which make the SISP dataset more suitable for real-world applications. In addition, we introduce a Dynamic Feature Refinement-assist Instance segmentation network, namely DFRInst, as the benchmark method for ship instance segmentation in satellite images, which can fortify the explicit representation of crucial features, thus improving the performance of ship instance segmentation. Experiments and analysis are performed on the proposed SISP dataset to evaluate the benchmark method and several state-of-the-art methods to establish baselines for facilitating future research. The proposed dataset and source codes will be available at: https://***/Justlovesmile/SISP. Copyright © 2024, The Authors. All rights reserved.

关键词： Ships

Hardware-in-the-loop Simulation of ocean Current Turbines for Grid Integration

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Hardware-in-the-loop Simulation of Ocean Current Turbines fo...

IEEE General Meeting Power& Energy Society

作者： Sasha Fung Carter Nichols Elisa Weinberg Yufei Tang James VanZwieten Gabriel Alsenas Department of Electrical Engineering and Computer Science Florida Atlantic University Boca Raton FL USA Department of Ocean and Mechanical Engineering Florida Atlantic University Boca Raton FL USA Southeast National Marine Renewable Energy Center Florida Atlantic University Boca Raton FL USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

The escalating interest in renewable energy sources has spurred a demand for innovative approaches to electricity generation. Notably, marine hydrokinetic turbines have emerged as a promising solution, capable of providing cost-effective power to locations isolated from traditional grids. Among these turbines, ocean current turbines (OCTs) stand out for their ability to harness the vast energy potential stored within open ocean currents. This research paper delves into the realm of marine energy, focusing on the modeling and simulation of OCTs using a hardware-in-loop (HIL) simulation approach with the aid of an OPAL-RT environment. The experiment recreates realistic operating conditions to evaluate the turbine’s feedback performance in the real-time simulator. The research progresses to explore a grid-integrated scenario where the OCT system’s response to water flow speed changes is evaluated as a blade pitch controller is tested. Power generation profiles are recorded from the simulated grid to demonstrate grid integration capabilities. The results validate the effectiveness of the OPAL-RT platform for power HIL emulation of marine energy systems and contribute to advancing the technology required to make ocean current turbines a viable renewable energy source with the potential to sustainably meet the world’s growing power demand.

关键词： Renewable energy sources Supply and demand oceans Hardware-in-the-loop simulation Blades Microgrids Real-time systems Turbines Power generation Testing

Interpretable Machine Learning for Discovery: Statistical Challenges & Opportunities

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arXiv 2023年

作者： Allen, Genevera I. Gan, Luqin Zheng, Lili Departments of Electrical and Computer Engineering and Computer Science Rice University HoustonTX77005 United States Neurological Research Institute Baylor College of Medicine HoustonTX77030 United States Department of Statistics Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States

New technologies have led to vast troves of large and complex datasets across many scientific domains and industries. People routinely use machine learning techniques to not only process, visualize, and make predictions from this big data, but also to make data-driven discoveries. These discoveries are often made using Interpretable Machine Learning, or machine learning models and techniques that yield human understandable insights. In this paper, we discuss and review the field of interpretable machine learning, focusing especially on the techniques as they are often employed to generate new knowledge or make discoveries from large data sets. We outline the types of discoveries that can be made using Interpretable Machine Learning in both supervised and unsupervised settings. Additionally, we focus on the grand challenge of how to validate these discoveries in a data-driven manner, which promotes trust in machine learning systems and reproducibility in science. We discuss validation from both a practical perspective, reviewing approaches based on data-splitting and stability, as well as from a theoretical perspective, reviewing statistical results on model selection consistency and uncertainty quantification via statistical inference. Finally, we conclude by highlighting open challenges in using interpretable machine learning techniques to make discoveries, including gaps between theory and practice for validating data-driven-discoveries. © 2023, CC BY.

关键词： Machine learning

Deep Learning for Breast MRI Style Transfer with Limited Training Data

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arXiv 2023年

作者： Cao, Shixing Konz, Nicholas Duncan, James Mazurowski, Maciej A. Department of Electrical and Computer Engineering Duke University DurhamNC27704 United States Department of Biomedical Engineering Yale University New HavenCT06520 United States Departments of Computer Science Radiology and Biostatistics & Bioinformatics Duke University DurhamNC27704 United States

Purpose: In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data. This is made possible by training our model on unlimited possibilities of simulated random medical imaging styles on the training set, making our work more computationally efficient when compared with other style transfer methods. Moreover, our method enables arbitrary style transfer: transferring images to styles unseen in training. This is useful for medical imaging, where images are acquired using different protocols and different scanner models, resulting in a variety of styles that data may need to be transferred between. Methods: Our model disentangles image content from style and can modify an image’s style by simply replacing the style encoding with one extracted from a single image of the target style, with no additional optimization required. This also allows the model to distinguish between different styles of images, including among those that were unseen in training. We propose a formal description of the proposed model. Results: Experimental results on breast magnetic resonance images indicate the effectiveness of our method for style transfer. Conclusion: Our style transfer method allows for the alignment of medical images taken with different scanners into a single unified style dataset, allowing for the training of other downstream tasks on such a dataset for tasks such as classification, object detection and others. © 2023, CC BY.

关键词： Medical imaging

Improved batch code lower bounds

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arXiv 2021年

作者： Li, Ray Wootters, Mary Department of Computer Science Stanford University Departments of Computer Science and Electrical Engineering Stanford University

Batch codes are a useful notion of locality for error correcting codes, originally introduced in the context of distributed storage and cryptography. Many constructions of batch codes have been given, but few lower bound (limitation) results are known, leaving gaps between the best known constructions and best known lower bounds. Towards determining the optimal redundancy of batch codes, we prove a new lower bound on the redundancy of batch codes. Specifically, we study (primitive, multiset) linear batch codes that systematically encode n information symbols into N codeword symbols, with the requirement that any multiset of k symbol requests can be obtained in disjoint ways. We show that such batch codes need ω (vNk) symbols of redundancy, improving on the previous best lower bounds of (vN +k) at all k = nϵ with ϵ ∈ (0, 1). Our proof follows from analyzing the dimension of the order-O(k) tensor of the batch code's dual code. Copyright © 2021, The Authors. All rights reserved.

关键词： Redundancy

Hilbert-Space Ergodicity in Driven Quantum Systems: Obstructions and Designs

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Physical Review X 2024年第4期14卷 041059-041059页

作者： Saúl Pilatowsky-Cameo Iman Marvian Soonwon Choi Wen Wei Ho Center for Theoretical Physics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Departments of Physics and Electrical and Computer Engineering Duke University Durham North Carolina 27708 USA Department of Physics National University of Singapore Singapore 117551 Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore 117543

Despite its long history, a canonical formulation of quantum ergodicity that applies to general classes of quantum dynamics, including driven systems, has not been fully established. Here we introduce and study a notion of quantum ergodicity for closed systems with time-dependent Hamiltonians, defined as statistical randomness exhibited in their longtime dynamics. Concretely, we consider the temporal ensemble of quantum states (time-evolution operators) generated by the evolution, and investigate the conditions necessary for them to be statistically indistinguishable from uniformly random states (operators) in the Hilbert space (space of unitaries). We find that the number of driving frequencies underlying the Hamiltonian needs to be sufficiently large for this to occur. Conversely, we show that statistical pseudo-randomness—indistinguishability up to some large but finite moment—can already be achieved by a quantum system driven with a single frequency, i.e., a Floquet system, as long as the driving period is sufficiently long. Our work relates the complexity of a time-dependent Hamiltonian and that of the resulting quantum dynamics, and offers a fresh perspective to the established topics of quantum ergodicity and chaos from the lens of quantum information.

关键词： Quantum information theory Quantum statistical mechanics Quantum many-body systems Ergodic theory

A New Denoising Method for Ship-Radiated Noise Based on Complete Ensemble Empirical Mode Decomposition

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A New Denoising Method for Ship-Radiated Noise Based on Comp...

IEEE Information Technology and Mechatronics engineering Conference (ITOEC)

作者： Zewen Zhuang Haoyu Chen Jianxun Tang Mingsong Chen Xingjian Hezhou School of Information and Communication Guilin University of Electronic Technology Guilin China School of Electric Power Engineering Guangxi Electrical Polytechnic Institute Nanning China School of Ocean Engineering Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

Noise reduction lays a foundation for feature extraction and identification of ship-radiated noise. To improve the signal-to-noise ratio (SNR) of ship-radiated noise, a new denoising scheme is proposed in this paper, based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved permutation entropy (IPE). The proposed approach initiates with the decomposition of ship-generated acoustic signals into multiple intrinsic mode functions (IMFs) through the CEEMDAN technique. Subsequently, these IMF components are categorized into three distinct groups -pure noise components, noise-biased components, and signal-dominant components - based on their respective IPE metrics. The processing strategy involves discarding the pure noise IMFs while applying least mean square adaptive filtering (LMSAF) to refine the noise-biased components. The final denoised output is reconstructed by integrating the processed noise-dominant IMFs with the unaltered signal-dominant IMFs. Experimental validation through both simulated and real-world acoustic datasets demonstrates the superior performance of this approach. Comparative analysis reveals that the proposed technique outperforms conventional methods, providing a more reliable preprocessing solution for underwater acoustic signal analysis and subsequent interpretation tasks.

关键词： Empirical mode decomposition Filtering Noise reduction Entropy Underwater acoustics Signal analysis Signal resolution Mutual information Signal integrity Signal to noise ratio

Enhancing Image Security With a Novel Chaotic System: A Focus on Multi-Face Image Encryption in Smart Applications

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IEEE Internet of Things Journal 2025年第12期12卷 20087-20098页

作者： Liu, Pengbo Teng, Lin Liu, Huipeng Iu, Herbert Ho-Ching Wang, Mingxu Yan, Xiaopeng Fu, Xianping Dalian Maritime University School of Information Science and Technology Dalian116026 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Key Laboratory of Ministry of Fisheries and Education Dalian Ocean University College of Information Engineering Dalian Key Laboratory of Smart Fishery Liaoning Key Laboratory of Marine Information Technology Dalian116023 China

To ensure stringent security strategies for image information involving personal privacy during conveyance and storage, we propose an innovative multi-face privacy protection scheme based on chaos theory. Compared to single-face encryption algorithms, the proposed scheme has broader potential applications in fields such as smart cities and smart transportation. Specifically, a new spatiotemporal chaotic system named the sine-cosine coupled mapping lattice system (SCCML) is designed. It features a larger parameter domain, complexity, and profound unpredictability, yet maintains a simpler construction aimed at providing potential benefits and implementations in the field of information security. In the proposed multi-face privacy protection scheme, multiple faces within an image are rapidly and accurately identified and then encrypted using the proposed SCCML-based digital separation loop encryption algorithm. The encryption algorithm exhibits a synchronous scrambling diffusion mechanism. Additionally, the introduction of mixed multi-base cascade diffusion offers multiple layers of security for facial data, prevents diffusion singularity, and enhances diversity, making it significantly more challenging to crack. Experimental verification on a real multi-face image dataset shows that the algorithm is superior, practical, safe, and efficient. © 2014 IEEE.

关键词： Differential privacy