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arXiv 2025年

作者： Guo, Kehan Shen, Yili Gonzalez-Montiel, Gisela Abigail Huang, Yue Zhou, Yujun Surve, Mihir Guo, Zhichun Das, Prayel Chawla, Nitesh V. Wiest, Olaf Zhang, Xiangliang Department of Computer Science and Engineering University of Notre Dame United States Department of Chemistry and Biochemistry University of Notre Dame United States Institute for Protein Design University of Washington United States Trusted AI Department of IBM Thomas J Watson Research Center IBM United States

The rapid advent of machine learning (ML) and artificial intelligence (ai) has catalyzed major transformations in chemistry, yet the application of these methods to spectroscopic and spectrometric data–termed Spectroscopy Machine Learning (SpectraML)–remains relatively underexplored. Modern spectroscopic techniques (MS, NMR, IR, Raman, UV-Vis) generate an ever-growing volume of high-dimensional data, creating a pressing need for automated and intelligent analysis beyond traditional expert-based workflows. In this survey, we provide a unified review of SpectraML, systematically examining state-of-the-art approaches for both forward tasks (molecule-to-spectrum prediction) and inverse tasks (spectrum-to-molecule inference). We trace the historical evolution of ML in spectroscopy–from early pattern recognition to the latest foundation models capable of advanced reasoning–and offer a taxonomy of representative neural architectures, including graph-based and transformer-based methods. Addressing key challenges such as data quality, multimodal integration, and computational scalability, we highlight emerging directions like synthetic data generation, large-scale pretraining, and few- or zero-shot learning. To foster reproducible research. We also release an open-source repository listing recent papers and their corresponding curated datasets1. Our survey serves as a roadmap for researchers, guiding advancements at the intersection of spectroscopy and ai. © 2025, CC BY.

关键词： Zero-shot learning

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Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator

arXiv

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arXiv 2025年

作者： Kou, Wei-Bin Zhu, Guangxu Ye, Rongguang Wang, Shuai Tang, Ming Wu, Yik-Chung Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen International Center For Industrial And Applied Mathematics Shenzhen Research Institute of Big Data Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Learning-based street scene semantic understanding in autonomous driving (AD) has advanced significantly recently, but the performance of the AD model is heavily dependent on the quantity and quality of the annotated training data. However, traditional manual labeling involves high cost to annotate the vast amount of required data for training robust model. To mitigate this cost of manual labeling, we propose a Label Anything Model (denoted as LAM), serving as an interpretable, high-fidelity, and prompt-free data annotator. Specifically, we firstly incorporate a pretrained Vision Transformer (ViT) to extract the latent features. On top of ViT, we propose a semantic class adapter (SCA) and an optimization-oriented unrolling algorithm (OptOU), both with a quite small number of trainable parameters. SCA is proposed to fuse ViT-extracted features to consolidate the basis of the subsequent automatic annotation. OptOU consists of multiple cascading layers and each layer contains an optimization formulation to align its output with the ground truth as closely as possible, though which OptOU acts as being interpretable rather than learning-based blackbox nature. In addition, training SCA and OptOU requires only a single pre-annotated RGB seed image, owing to their small volume of learnable parameters. Extensive experiments clearly demonstrate that the proposed LAM can generate high-fidelity annotations (almost 100% in mIoU) for multiple real-world datasets (i.e., Camvid, Cityscapes, and Apolloscapes) and CARLA simulation dataset. © 2025, CC BY-NC-ND.

关键词： Adversarial machine learning

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ZuantuSet: A Collection of Historical Chinese Visualizations and Illustrations

arXiv

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arXiv 2025年

作者： Mei, Xiyao Zhang, Yu Yang, Chaofan Shi, Rui Yuan, Xiaoru National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Beijing China Department of Computer Science University of Oxford Oxford United Kingdom Center for Research on Ancient Chinese History Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Application Peking University Beijing China PKU-WUHAN Institute for Artificial Intelligence Wuhan China

Historical visualizations are a valuable resource for studying the history of visualization and inspecting the cultural context where they were created. When investigating historical visualizations, it is essential to consider contributions from different cultural frameworks to gain a comprehensive understanding. While there is extensive research on historical visualizations within the European cultural framework, this work shifts the focus to ancient China, a cultural context that remains underexplored by visualization researchers. To this aim, we propose a semi-automatic pipeline to collect, extract, and label historical Chinese visualizations. Through the pipeline, we curate ZuantuSet, a dataset with over 71K visualizations and 108K illustrations. We analyze distinctive design patterns of historical Chinese visualizations and their potential causes within the context of Chinese history and culture. We illustrate potential usage scenarios for this dataset, summarize the unique challenges and solutions associated with collecting historical Chinese visualizations, and outline future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： data collection

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Dose-finding design based on level set estimation in phase I cancer clinical trials

arXiv

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arXiv 2025年

作者： Seno, Keiichiro Matsui, Kota Iwazaki, Shogo Inatsu, Yu Takeno, Shion Matsui, Shigeyuki Department of Biostatistics Nagoya University Japan Department of Biostatistics Kyoto University Japan MI-6 Ltd. Department of Computer Science Nagoya Institute of Technology Japan Department of Mechanical Systems Engineering Nagoya University Japan Center for Advanced Intelligence Project RIKEN Japan Research Center for Medical and Health Data Science The Institute of Statistical Mathematics Japan

The primary objective of phase I cancer clinical trials is to evaluate the safety of a new experimental treatment and to find the maximum tolerated dose (MTD). We show that the MTD estimation problem can be regarded as a level set estimation (LSE) problem whose objective is to determine the regions where an unknown function value is above or below a given threshold. Then, we propose a novel dose-finding design in the framework of LSE. The proposed design determines the next dose on the basis of an acquisition function incorporating uncertainty in the posterior distribution of the dose-toxicity curve as well as overdose control. Simulation experiments show that the proposed LSE design achieves a higher accuracy in estimating the MTD and involves a lower risk of overdosing allocation compared to existing designs, thereby indicating that it provides an effective methodology for phase I cancer clinical trial design. © 2025, CC BY.

关键词： Intellectual property core

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Functional Network Connectivity in White Matter: A Group Informed ICA-Based Network Approach

Functional Network Connectivity in White Matter: A Group Inf...

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IEEE International Symposium on Biomedical Imaging

作者： Vaibhavi S. Itkyal Armin Iraji Zening Fu Vince D. Calhoun Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University GA Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology Emory University GA Department of Neuroscience Emory University GA Department of Computer Science Georgia State University Atlanta GA Department of Electrical and Computer Engineering Georgia Institute of Technology GA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

White matter (WM) has traditionally been undervalued in functional brain connectivity studies due to its weaker blood-oxygenation-level-dependent (BOLD) signals compared to gray matter (GM). Conventional atlas-based approaches often overlook the individualized aspects of functional networks, limiting their applicability for WM. In this study, we introduce the first WM intrinsic connectivity network (ICN) templates derived from a large dataset of over 100,000 scans, demonstrating the feasibility of estimating individualized WM networks using multivariate-spatially constrained ICA (scICA). By combining WM and GM ICNs, our work provides an expanded framework for evaluating functional network connectivity (FNC) across both tissue types, offering new insights into the integrative role of WM in functional connectivity. This approach holds particular promise for enhancing the understanding of connectivity alterations in clinical populations, such as individuals with schizophrenia.

关键词： Limiting Cognitive processes Grey matter Independent component analysis Schizophrenia Biomarkers Functional analysis White matter Biomedical imaging

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Provably Secure Authenticated Key-Management Mechanism for e-Healthcare Environment

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IEEE Internet of Things Journal 2025年

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Ghaffar, Zahid Xie, Yong Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan Qinghai Institute of Technology School of Computer and Information Science China Qinghai Provincial Laboratory for Intelligent Computing and Application China Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan250353 China

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively. © 2014 IEEE.

关键词： Electronic health record

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Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential

arXiv

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arXiv 2025年

作者： Gao, Chenyu Xu, Kaiwen Kim, Michael E. Zuo, Lianrui Li, Zhiyuan Archer, Derek B. Hohman, Timothy J. Moore, Ann Zenobia Ferrucci, Luigi Beason-Held, Lori L. Resnick, Susan M. Davatzikos, Christos Prince, Jerry L. Landman, Bennett A. Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Vanderbilt Memory and Alzheimer’s Center Vanderbilt University Medical Center NashvilleTN United States Department of Neurology Vanderbilt University Medical Center NashvilleTN United States Translational Gerontology Branch National Institute on Aging National Institutes of Health BaltimoreMD United States Laboratory of Behavioral Neuroscience National Institute on Aging National Institutes of Health BaltimoreMD United States AI2D Center for AI and Data Science University of Pennsylvania Philadelphia United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States

Defacing is often applied to head magnetic resonance image (MRI) datasets prior to public release to address privacy concerns. The alteration of facial and nearby voxels has provoked discussions about the true capability of these techniques to ensure privacy as well as their impact on downstream tasks. With advancements in deep generative models, the extent to which defacing can protect privacy is uncertain. Additionally, while the altered voxels are known to contain valuable anatomical information, their potential to support research beyond the anatomical regions directly affected by defacing remains uncertain. To evaluate these considerations, we develop a refacing pipeline that recovers faces in defaced head MRIs using cascaded diffusion probabilistic models (DPMs). The DPMs are trained on images from 180 subjects and tested on images from 484 unseen subjects, 469 of whom are from a different dataset. To assess whether the altered voxels in defacing contain universally useful information, we also predict computed tomography (CT)-derived skeletal muscle radiodensity from facial voxels in both defaced and original MRIs. The results show that DPMs can generate high-fidelity faces that resemble the original faces from defaced images, with surface distances to the original faces significantly smaller than those of a population average face (p -4). For shin muscle, the correlation is statistically significant (p 0.05) when any defacing method is applied, suggesting that defacing might not only fail to protect privacy but also eliminate valuable information. We advocate two solutions for data sharing that comply with privacy: 1) share skull-stripped images along with measurements of facial and cranial features extracted before skull-stripping for public access, while acknowledging that this approach inherently compromises many research potentials;or 2) share the unaltered images with privacy enforced through policy restrictions. © 2025, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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Modelling radiological features fusion and explainable ai in pneumonia detection: A graph- based deep learning and transformer approach

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Results in engineering 2025年 26卷

作者： Kaushik, Pratham Jain, Eshika Kukreja, Vinay Hariharan, Shanmugasundaram Krishnamoorthy, Murugaperumal Ahuja, Vandana Bhattacherjee, Abhishek Kaushal, Rajesh Kumar Chen, Shih-Yu Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Artificial Intelligence and Data Science Vardhaman College of Engineering India Department of Electrical and Electronics Engineering Vardhaman College of Engineering India Department of Computer Science & Engineering Swami Vivekanand Institute of Engineering and Technology Punjab Ramnagar India Department of Computer Science Engineering Lovely Professional University Punjab India Department of Computer Applications Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin64002 Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and Technology Yunlin64002 Taiwan

Objectives: This study aims to develop a deep learning-based methodology for pneumonia detection using lung CT scans. The research focuses on integrating U-Net for lung segmentation, graph-based feature representations, and transformer-based models to improve interpretability and diagnostic accuracy. Methodology: We utilized 500 annotated lung CT scans, split into 70 % training (350 images), 15 % validation (75 images), and 15 % testing (75 images). U-Net was employed for lung segmentation, with segmentation performance evaluated using Dice Similarity Coefficient (DSC) and Intersection over Union (IoU). The classification model incorporated feature fusion, combining handcrafted radiological features with transformer-based feature extraction. The model's noise sensitivity and interpretability were assessed using Grad-CAM and attention maps. Results: The segmentation model achieved a DSC of 0.943 on the training set, 0.928 on the validation set, and 0.910 on the test set. The IoU values were 0.890, 0.872, and 0.850, respectively. The model's inference time was 25 milliseconds per image. The classification model achieved an overall accuracy of 94 %, with pneumonia cases showing precision, recall, and F1-scores of 0.95, 0.94, and 0.94, respectively. For normal cases, precision, recall, and F1-scores were 0.92, 0.94, and 0.93. The model's performance improved over 20 epochs, with training accuracy reaching 0.94 and validation accuracy reaching 0.88. The feature fusion method achieved an attention score of 0.88, with node intensity scores of 0.85. Noise sensitivity analysis revealed a performance drop beyond a noise level of 0.3. Conclusions: This study demonstrates the potential of ai-driven solutions for pneumonia detection, achieving high accuracy, robustness, and interpretability. While the model performs well, further optimization is needed to address noise sensitivity and enhance model generalization for real-world applications. © 2025

关键词： Image annotation

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Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks

arXiv

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arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (ai) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

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Flexible Intelligent Metasurfaces for Enhancing MIMO Communications

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IEEE Transactions on Communications 2025年

作者： An, Jiancheng Han, Zhu Niyato, Dusit Debbah, Merouane Yuen, Chau Hanzo, Lajos Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of Nanyang Technological University College of Computing and Data Science Singapore Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

Flexible intelligent metasurfaces (FIMs) show great potential for improving the wireless network capacity in an energy-efficient manner. An FIM is a soft array consisting of several low-cost radiating elements. Each element can independently emit electromagnetic signals, while flexibly adjusting its position even perpendicularly to the overall surface to 'morph' its 3D shape. More explicitly, compared to a conventional rigid antenna array, an FIM is capable of finding an optimal 3D surface shape that provides improved signal quality. In this paper, we study point-to-point multiple-input multiple-output (MIMO) communications between a pair of FIMs. In order to characterize the capacity limits of FIM-aided MIMO transmissions over frequency-flat fading channels, we formulate a transmit optimization problem for maximizing the MIMO channel capacity by jointly optimizing the 3D surface shapes of the transmitting and receiving FIMs as well as the MIMO transmit covariance matrix, subject to the total transmit power constraint and to the maximum perpendicular morphing range of the FIM. To solve this problem, we develop an efficient block coordinate descent (BCD) algorithm. The BCD algorithm iteratively updates the 3D surface shapes of the FIMs and the transmit covariance matrix, while keeping the other fixed, to find a locally optimal solution. Numerical results verify that FIMs can achieve higher MIMO capacity than that of the conventional rigid arrays. In particular, the MIMO channel capacity can be doubled by the proposed BCD algorithm under some setups. © 1972-2012 IEEE.

关键词： Covariance matrix

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