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arXiv 2025年

作者： Rutala, M.J. Jackman, C.M. Louis, C.K. Azari, A.R. Bagenal, F. Joy, S.P. Kurth, W.S. Keebler, T.B. Giles, R.S. Ebert, R.W. Bowers, C.F. Vogt, M.F. Astronomy & Astrophysics Section School of Cosmic Physics Dublin Institute for Advanced Studies DIAS Dunsink Observatory DublinD15 XR2R Ireland LIRA Observatoire de Paris Université PSL CNRS Sorbonne Université Université de Paris Meudon France Department of Physics University of Alberta EdmontonAB Canada Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada Alberta Machine Intelligence Institute EdmontonAB Canada Laboratory for Atmospheric and Space Physics University of Colorado Boulder BoulderCO United States Department of Earth Planetary and Space Science University of California Los AngelesCA United States Department of Physics and Astronomy University of Iowa Iowa CityIA United States Department of Climate and Space Sciences and Engineering University of Michigan Ann ArborMI United States Space Science Division Southwest Research Institute San AntonioTX United States University of Texas at San Antonio San AntonioTX United States Planetary Science Institute TucsonAZ United States

The interaction between Jupiter’s magnetosphere and the solar wind is not well-constrained: while internal energetic plasma processes are thought to dominate plasma circulation, the solar wind nonetheless exerts significant control over the shape and scale of the whole structure. To better constrain this interaction, we derive new functional forms for Jupiter’s magnetopause and bow shock using data from the Ulysses, Galileo, Cassini, and Juno missions and calibrated solar wind estimates from the Multi-Model Ensemble System for the Heliosphere (MMESH). We design an empirical Bayesian model to estimate the locations of the boundaries using a Markov-chain Monte Carlo (MCMC) algorithm, expanding our model to sample all times, not only boundary crossing events. The boundary surfaces which best describe the data are thus estimated without the need for a full, physics-based magnetohydrodynamic (MHD) treatment of the Jovian magnetosphere and the additional assumptions required for such. The new magnetopause model exhibits significant polar flattening and dawn-dusk asymmetry, and includes a narrowing of the magnetotail when compared to previous models. The new bow shock model is largely axisymmetric. Both boundary models describe surfaces which lie closer to Jupiter than previous models, which has important implications for the modern picture of Jupiter’s dynamic magnetosphere and the expected science results of current and upcoming Jupiter-bound spacecraft. Applying these models to Juno’s trajectory, we estimate that the spacecraft should be expected to spend ∼19% of each orbit in the magnetosheath and ∼4% of each orbit in the solar wind starting from Perijove 64 (PJ64, 21 July 2021). © 2025, CC BY-NC-SA.

关键词： Solar wind

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U-isles: Ischemic stroke lesion segmentation using u-net

U-isles: Ischemic stroke lesion segmentation using u-net

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作者： Cornelio, Lea Katrina S. Del Castillo, Mary Abigail V. Naval, Prospero C. Department of Computer Science Computer Vision & Machine Intelligence Group College of Engineering University of the Philippines Diliman Philippines

. An accurate automation of image segmentation has a great significance in enhancing the processing of biomedical images. This could avoid the tedious task of manually segmenting biomedical images and the data inconsistencies from the varying configurations in MRI scans from hospitals. In this study, a new method in segmenting ischemic stroke lesions is proposed. The dataset is downloaded from Ischemic Stroke Lesion Segmentation 2017 (ISLES 2017) Challenge. It consists of MRI scan images of the brain from the patients. The preprocessing of data involves the application of batch normalization, image resizing, color conversion, and contrast correction. The proposed deep learning technique is a type of a two-dimensional CNN known as U-Net. Experiments are performed to determine the combination of loss function and regularizer which returns the optimal result. The results of different contrast intensities are also compared among themselves. After experimentation and segmentation, the output is post-processed to eliminate isolated small regions and smooth lesion edges. The metric used to measure the accuracy of the model is dice score. © Springer Nature Switzerland AG 2019.

关键词： Image segmentation

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Interpretable Multimodal Learning for Tumor Protein-Metal Binding: Progress, Challenges, and Perspectives

arXiv

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arXiv 2025年

作者： Liu, Xiaokun Rastegari, Sayedmohammadreza Huang, Yijun Cheong, Sxe Chang Liu, Weikang Zhao, Wenjie Tian, Qihao Wang, Hongming Zhou, Shuo Guo, Yingjie Tabakhi, Sina Liu, Xianyuan Zhu, Zheqing Sang, Wei Lu, Haiping Institute of Big Data Science and Industry Shanxi University Taiyuan China School of Computer and Information Technology Shanxi University Taiyuan China Key Laboratory of Evolutionary Science Intelligence of Shanxi Province Shanxi Taiyuan China Faculty of Computer Engineering University of Isfahan Isfahan Iran The First Clinical Medical School Shanxi Medical University Taiyuan China School of Medicine & Population Health University of Sheffield Sheffield United Kingdom School of Computer Science University of Sheffield Sheffield United Kingdom Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom Department of Biochemistry and Molecular Biology School of Basic Medical Sciences Shanxi Medical University Taiyuan China Institute of Medical Technology Shanxi Medical University Taiyuan China

In cancer therapeutics, protein-metal binding mechanisms critically govern drug pharmacokinetics and targeting efficacy, thereby fundamentally shaping the rational design of anticancer metallodrugs. While conventional laboratory methods used to study such mechanisms are often costly, low throughput, and limited in capturing dynamic biological processes, machine learning (ML) has emerged as a promising alternative. Despite increasing efforts to develop protein–metal binding datasets and ML algorithms, the application of ML in tumor protein-metal binding remains limited. Key challenges include a shortage of high-quality, tumor-specific datasets, insufficient consideration of multiple data modalities, and the complexity of interpreting results due to the "black box" nature of complex ML models. This paper summarizes recent progress and ongoing challenges in using ML to predict tumor protein-metal binding, focusing on data, modeling, and interpretability. We present multimodal protein-metal binding datasets and outline strategies for acquiring, curating, and preprocessing them for training ML models. Moreover, we explore the complementary value provided by different data modalities and examine methods for their integration. We also review approaches for improving model interpretability to support more trustworthy decisions in cancer research. Finally, we offer our perspective on research opportunities and propose strategies to address the scarcity of tumor protein data and the limited number of predictive models for tumor protein–metal binding. We also highlight two promising directions for effective metal-based drug design: integrating protein–protein interaction data to provide structural insights into metal-binding events and predicting structural changes in tumor proteins after metal binding. Copyright © 2025, The Authors. All rights reserved.

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DATA AUGMENTATION FOR END-TO-END CODE-SWITCHING SPEECH RECOGNITION

arXiv

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arXiv 2020年

作者： Du, Chenpeng Li, Hao Lu, Yizhou Wang, Lan Qian, Yanmin MoE Key Lab of Artificial Intelligence SpeechLab Department of Computer Science and Engineering AI Institute Shanghai Jiao Tong University Shanghai China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

Training a code-switching end-to-end automatic speech recognition (ASR) model normally requires a large amount of data, while code-switching data is often limited. In this paper, three novel approaches are proposed for code-switching data augmentation. Specifically, they are audio splicing with the existing code-switching data, and TTS with new code-switching texts generated by word translation or word insertion. Our experiments on 200 hours Mandarin-English code-switching dataset show that all the three proposed approaches yield significant improvements on code-switching ASR individually. Moreover, all the proposed approaches can be combined with recent popular SpecAugment, and an addition gain can be obtained. WER is significantly reduced by relative 24.0% compared to the system without any data augmentation, and still relative 13.0% gain compared to the system with only SpecAugment. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech recognition

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Probability analysis of axillary lymph node metastasis in breast cancer patients using particle space-time distribution model

Probability analysis of axillary lymph node metastasis in br...

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作者： Chen, Fang Liu, Jia Zhang, Xinran Liao, Hongen Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing210016 China Department of Biomedical Engineering School of Medicine Tsinghua University Beijing10084 China

The possibility of axillary lymph node metastasis differs in different breast cancer patients and is the strongest prognostic indicator in breast cancer. The existing studies mainly explored the relationship of axillary ultrasound imaging and axillary lymph node metastasis, without exploring whether ultrasound imaging of breast tumour can affect and perform axillary lymph node prediction. Therefore, this Letter proposes a novel particle space-time distribution model to find the correlation between contrast-enhanced ultrasonography of breast tumour and axillary lymphatic metastasis. Starting from the imaging principle of dynamic contrast-enhanced ultrasonography, the particle space-time distribution model not only comprises space-time features of contrast-enhanced ultrasonography with an encoder–decoder network, but also the flow field information of microbubble particles is integrated into the space-time features that better serves the metastasis prediction by enhancing the particle distribution information. Extensive experiments on real patients have demonstrated that dynamic contrast-enhanced ultrasonography of breast tumour can be used to predict the probability of lymphatic metastasis. This conclusion can be interpretable from the clinical and pathological perspectives. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Forecasting

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Random Occlusion Recovery with Noise Channel for Person Re-identification 16th

Random Occlusion Recovery with Noise Channel for Person Re-i...

引用

16th International Conference on Intelligent Computing, ICIC 2020

作者： Zhang, Kun Wu, Di Yuan, Changan Qin, Xiao Wu, Hongjie Zhao, Xingming Zhang, Lijun Du, Yuchuan Wang, Hanli Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Guangxi Academy of Science Nanning530025 China School of Computer and Information Engineering Nanning Normal University Nanning530299 China School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Fudan University Shanghai200433 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Shanghai China Collaborative Innovation Center of Intelligent New Energy Vehicle and School of Automotive Studies Tongji University Shanghai201804 China The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Department of Transportation Engineering Tongji University Shanghai201804 China Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing and Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

ISBN: (纸本)9783030607982

Person re-identification, as the basic task of a multi-camera surveillance system, plays an important role in a variety of surveillance applications. However, the current mainstream person re-identification model based on deep learning requires a lot of labeled data, which takes a lot of time and manpower. In this study, we proposed a person re-identification method based on random occlusion recovery with noise channel. We add random occlusion blocks to the original image, use the GAN model for repair, and use the repaired image to expand the original training set. After that, the generated image is adjusted through the noise channel. Finally, we use the enhanced data set to train the baseline model. Our model achieves the state-of-the-art on Market-1501 dataset, proving that the method is effective. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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One to many: Adaptive instrument segmentation via meta learning and dynamic online adaptation in robotic surgical video

arXiv

引用

arXiv 2021年

作者： Zhao, Zixu Jin, Yueming Lu, Bo Ng, Chi-Fai Dou, Qi Liu, Yun-Hui Heng, Pheng-Ann The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The T Stone Robotics Institute The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China The Department of Surgery The Chinese University of Hong Kong Hong Kong

Surgical instrument segmentation in robot-assisted surgery (RAS) - especially that using learning-based models - relies on the assumption that training and testing videos are sampled from the same domain. However, it is impractical and expensive to collect and annotate sufficient data from every new domain. To greatly increase the label efficiency, we explore a new problem, i.e., adaptive instrument segmentation, which is to effectively adapt one source model to new robotic surgical videos from multiple target domains, only given the annotated instruments in the first frame. We propose MDAL, a meta-learning based dynamic online adaptive learning scheme with a two-stage framework to fast adapt the model parameters on the first frame and partial subsequent frames while predicting the results. MDAL learns the general knowledge of instruments and the fast adaptation ability through the video-specific meta-learning paradigm. The added gradient gate excludes the noisy supervision from pseudo masks for dynamic online adaptation on target videos. We demonstrate empirically that MDAL outperforms other state-of-the-art methods on two datasets (including a real-world RAS dataset). The promising performance on ex-vivo scenes also benefits the downstream tasks such as robot-assisted suturing and camera control. Copyright © 2021, The Authors. All rights reserved.

关键词： Surgical equipment

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Faster stochastic alternating direction method of multipliers for nonconvex optimization

arXiv

引用

arXiv 2020年

作者： Huang, Feihu Chen, Songcan Huang, Heng Department of Electrical & Computer Engineering University of Pittsburgh PA15261 United States College of Computer Science & Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China MIIT Key Laboratory of Pattern Analysis & Machine Intelligence JD Finance America Corporation United States

In this paper, we propose a faster stochastic alternating direction method of multipliers (ADMM) for nonconvex optimization by using a new stochastic path-integrated differential estimator (SPIDER), called as SPIDER-ADMM. Moreover, we prove that the SPIDER-ADMM achieves a record-breaking incremental first-order oracle (IFO) complexity of O(n + n1/2∊−1) for finding an ∊-approximate stationary point, which improves the deterministic ADMM by a factor O(n1/2), where n denotes the sample size. As one of major contribution of this paper, we provide a new theoretical analysis framework for nonconvex stochastic ADMM methods with providing the optimal IFO complexity. Based on this new analysis framework, we study the unsolved optimal IFO complexity of the existing nonconvex SVRG-ADMM and SAGA-ADMM methods, and prove they have the optimal IFO complexity of O(n + n2/3∊−1). Thus, the SPIDER-ADMM improves the existing stochastic ADMM methods by a factor of O(n1/6). Moreover, we extend SPIDER-ADMM to the online setting, and propose a faster online SPIDER-ADMM. Our theoretical analysis shows that the online SPIDER-ADMM has the IFO complexity of O(∊−3 2 ), which improves the existing best results by a factor of O(∊−2 1 ). Finally, the experimental results on benchmark datasets validate that the proposed algorithms have faster convergence rate than the existing ADMM algorithms for nonconvex optimization. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

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Nearest neighborhood-based deep clustering for source data-absent unsupervised domain adaptation

arXiv

引用

arXiv 2021年

作者： Tang, Song Yang, Yan Ma, Zhiyuan Hendrich, Norman Zeng, Fanyu Ge, Shuzhi Sam Zhang, Changshui Zhang, Jianwei The Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China The State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Group Department of Informatics Universität Hamburg Hamburg Germany The Institute of Machine Intelligence University of Shanghai for Science and Technology Shanghai China The State Key Lab. for Novel Software Technology Nanjing University Nanjing China The Engineering Research Center of Wideband Wireless Communication Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China The Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore The Department of Automation Tsinghua University Beijing China

In the classic setting of unsupervised domain adaptation (UDA), the labeled source data are available in the training phase. However, in many real-world scenarios, owing to some reasons such as privacy protection and information security, the source data is inaccessible, and only a model trained on the source domain is available. This paper proposes a novel deep clustering method for this challenging task. Aiming at the dynamical clustering at feature-level, we introduce extra constraints hidden in the geometric structure between data to assist the process. Concretely, we propose a geometry-based constraint, named semantic consistency on the nearest neighborhood (SCNNH), and use it to encourage robust clustering. To reach this goal, we construct the nearest neighborhood for every target data and take it as the fundamental clustering unit by building our objective on the geometry. Also, we develop a more SCNNH-compliant structure with an additional semantic credibility constraint, named semantic hyper-nearest neighborhood (SHNNH). After that, we extend our method to this new geometry. Extensive experiments on three challenging UDA datasets indicate that our method achieves state-of-the-art results. The proposed method has significant improvement on all datasets (as we adopt SHNNH, the average accuracy increases by over 3.0% on the large-scaled dataset). Code is available at https://***/tntek/N2DCX. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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Deep Convolution Neural Network Based Artificial intelligence Improves Diagnosis of Thyroid Scintigraphy for Thyrotoxicosis: A Dual Center Study

Research Square

引用

Research Square 2020年

作者： Yang, Pei Pi, Yong He, Tao Zhou, Ke Zhong, Xiao Liu, Yemei Sun, Jiangming Wei, Jianan Xiang, Yongzhao Jiang, Lisha Li, Lin Yi, Zhang Zhao, Zhen Cai, Huawei Department of Nuclear Medicine West China Hospital Chengdu610041 China Machine Intelligence Laboratory College of Computer Science Sichuan University Chengdu610065 China Department of Nuclear Medicine Panzhihua Central Hospital Panzhihua617067 China

Background: 99mTc-pertechnetate thyroid scintigraphy is a valid avenue for distinguishing causes of thyrotoxicosis in the clinic, but the interpretation of thyroid scintigraphic images is subjected with significant variation among different inter-observers. We aim to develop an artificial intelligence (AI) system to improve the diagnosis of thyrotoxicosis. Materials and methods: We constructed an AI model based on a deep neural network with 2468 thyroid scintigraphic images collected from West China Hospital, and evaluated the diagnostic accuracy for classifying four patterns of thyrotoxicosis: ‘diffusely increased,’ ‘diffusely decreased,’ ‘focal increased,’ and ‘heterogeneous uptake.’ Then, we compared the diagnostic performance of the AI model and five physicians with 200 testing cohorts from two centers. Results: We constructed the AI model, which has the best performance in internal database validation based on four kinds of standout pre-trained networks. This AI model achieves satisfactory performance in classifying four patterns of thyrotoxicosis with an overall accuracy of 91.92% for internal and 86.75% for external data validation. In the following contrastive study, the AI model represented improved diagnostic accuracy and consistency than 5 physicians for interpreting data from West China Hospital (88% vs. 66~73%) and Panzhihua Central Hospital (83% vs. 53%~79%), respectively. Conclusion: Deep convolution neural network based AI model represented considerable performance in classifying four patterns of thyroid scintigraphic images;this may help physicians diagnose causes of thyrotoxicosis and reduced the physicians’ error rate. © 2020, CC BY.

关键词： Hospitals

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