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Improving generative model-based unfolding with Schrödinger bridges

Physical Review D 2024年第7期109卷 076011-076011页

作者： Sascha Diefenbacher Guan-Horng Liu Vinicius Mikuni Benjamin Nachman Weili Nie Physics Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Autonomous Control and Decision Systems Laboratory Georgia Institute of Technology Atlanta Georgia 30332 USA National Energy Research Scientific Computing Center Berkeley Lab Berkeley California 94720 USA Berkeley Institute for Data Science University of California Berkeley California 94720 USA Machine Learning Research Group NVIDIA Research

machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements. Two main approaches have emerged in this research area; one based on discriminative models and one based on generative models. The main advantage of discriminative models is that they learn a small correction to a starting simulation while generative models scale better to regions of phase space with little data. We propose to use Schrödinger bridges and diffusion models to create sbunfold, an unfolding approach that combines the strengths of both discriminative and generative models. The key feature of sbunfold is that its generative model maps one set of events into another without having to go through a known probability density as is the case for normalizing flows and standard diffusion models. We show that sbunfold achieves excellent performance compared to state of the art methods on a synthetic Z+jets dataset.

关键词： machine learning Particle detectors Probability theory

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Sample Reduction Using 1 -Norm Twin Bounded Support Vector machine 2nd

Sample Reduction Using 1 -Norm Twin Bounded Support Vector M...

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2nd International Conference on Neural computing for Advanced Applications, NCAA 2021

作者： Zheng, Xiaohan Zhang, Li Yan, Leilei School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University Suzhou215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China

ISBN: (纸本)9789811651878

Twin support machine (TSVM) has a lower time complexity than support vector machine (SVM), but it has a poor ability to perform sample reduction. In order to improve the ability of TSVM to reduce sample, we propose an 1 -norm twin bounded support machine (1 -TBSVM) inspired by the sparsity of 1 -norm in feature space. The objective function of 1 -TBSVM contains the hinge loss and the 1 -norm terms, both which can induce sparsity. We solve the primal programming problems of 1 -TBSVM to prevent the disappearance of sparsity and avoid the situation that the inverse of matrix does not exist. Thus, 1 -TBSVM has a good sparsity, or a good ability to reduce sample. Experimental results on synthetic and UCI datasets indicate that 1 -TBSVM has a good ability to perform sample reduction and simultaneously enhances the classification performance. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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A Neurobehavioral Evaluation of the Efficacy of 1mA Longitudinal, Anodal TDCS on Multitasking and Transfer Performance

A Neurobehavioral Evaluation of the Efficacy of 1mA Longitud...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Akash K Rao Shashank Uttrani Darshil Shah Vishnu K Menon Arnav Bhavsar Shubhajit Roy Chowdhury Ramsingh Negi Varun Dutt Manipal Academy of Higher Education Applied Cognitive Science Laboratory Indian Institute of Technology Mandi School of Computing and Electrical Engineering Indian Institute of Technology Mandi Cognitive Control and Machine Learning Group at the Institute of Nuclear Medicine and Allied Sciences Defence Research and Development Organization

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Multitasking requires rapid switching of attention and cognitive resources between different tasks in a dynamic environment, relying on cognitive processes, such as working memory, executive control, and selective attention. Although studies have investigated the efficacy of various neurobehavioral interventions in improving multitasking capabilities, the effects of longitudinal anodal transcranial direct current stimulation (tDCS) in enhancing multitasking performance have not been investigated. This research investigated the efficacy of 1mA anodal tDCS administered longitudinally on multitasking performance. 42 participants were randomly and equally divided into the experimental and placebo control conditions in this study conducted for 10 days. All participants executed two multitasking tasks on day 1 and received 1mA anodal/placebo tDCS during task training from day 2 to day 8. Various behavioral and neurophysiological measures have been measured. The findings revealed that tDCS had the propensity to augment multitasking capabilities in the trained task but had limited transfer capabilities. EEG-based brain connectivity analysis also revealed the formation of network hubs in the prefrontal and frontal regions, indicating enhanced cortical activation in the beta band. We intend to use these findings to design interventional frameworks to enhance multitasking performance using tDCS.

关键词： Training Atmospheric measurements Memory management Switches Multitasking Particle measurements Dynamic scheduling Control systems Transcranial direct current stimulation Cybernetics

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T CNN: a novel method for crowd counting via two-task convolutional neural network

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The Visual Computer 2021年第1期39卷 73-85页

作者： Zhang, Li Yan, Leilei Zhang, Mengqian Lu, Jingang Laboratory of Machine Learning and Neuromorphic Computing School of Computer Science and Technology and Joint International Research Soochow University Suzhou China

This paper investigates the issue of crowd counting for crowd images. A novel method named two-task convolutional neural network (T $$^2$$ CNN) is proposed to simultaneously learn two tasks for crowd counting: the tasks of dense degree classification and density map estimation. Basically, T $$^2$$ CNN contains two main modules: dense degree classification (DDC) and density map estimation. Generally, it is sufficient for crowd counting to estimate density maps of images. It is known that images may have different dense degrees and the local regions in the same image have different dense degrees. Thus, dense degree may provide the high-level prior of images and is excepted to help the estimation of density maps. To better estimate density maps of images, T $$^2$$ CNN incorporates the DDC module into the task of density map estimation. T $$^2$$ CNN takes the scale-adaptive convolutional neural network as the estimator of density maps, which adopts multi-scale layers to tackle the scale and perspective variations that exist in crowd images commonly. To verify the effectiveness and robustness of our model, we conduct extensive experiments on the Shanghaitech, UCF_CC_50 and WorldExpo’10 datasets. Experimental results indicate that T $$^2$$ CNN achieves good performance.

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Multi-Scale Clinical-Guided Binocular Fusion Framework for Predicting New-Onset Hypertension Over a Four-Year Period

Multi-Scale Clinical-Guided Binocular Fusion Framework for P...

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IEEE International Symposium on Biomedical Imaging

作者： Haoshen Li Zifan Chen Jie Zhao Heyun Chen Hexin Dong Mingze Yuan Bin Dong Li Zhang Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Hypertension is a major global health concern, linked to various cardiovascular diseases and associated with distinct ocular manifestations. While recent advances in artificial intelligence have enabled accurate diagnosis of current hypertension through fundus images, predicting the future onset of hypertension remains an uncharted domain. In this study, we introduce the multi-scale clinical-guided binocular fusion framework (MCBO), designed to predict the likelihood of developing hypertension within the next four years. MCBO uniquely integrates left and right fundus images and clinical data, utilizing a shared-weight multi-stage Transformer-based encoder. Our multi-scale clinical-guided module (MCM) ensures image feature extraction is clinically contextualized based on clinical information, and our binocular fusion module (BFM) fuses binocular information. Comparative performance against seven baseline models establishes MCBO’s supremacy, with improvements of 6.7% in Area Under Curve (AUC), 6.9% in Accuracy (ACC), 5.1% in Sensitivity (SEN) and 5.5% in Specificity (SPE). This approach offers a promising avenue for proactive hypertension management, underscoring the potential of integrating Deep learning with clinical data for enhanced healthcare outcomes. Our code is available at https://***/HaoshenLi/MCBO.

关键词： Hypertension Deep learning Accuracy Sensitivity Codes Fuses Feature extraction

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Object Segmentation based on Anti-disturb Network in Ecological Surveillance

Object Segmentation based on Anti-disturb Network in Ecologi...

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第33届中国控制与决策会议

作者： Wan Dai Hongpeng Wang Chongshan Fan Yunwei Xin College of Computer Science Nankai University Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Nankai University Software College Nankai University Research Laboratory of Machine Learning and Pervasive Computing Nankai University

Visual tele-observation is an effective way for ecological *** from pedestrian or rigid body detection,wildlife recognition in natural scenes face more complex problems,such as the similarity of background and foreground,wild background clutter,deformation,and other factors,which will lead to a reduction in recognition *** this paper,We propose a novel segmentation network that is named T-S network to segment background and *** network mainly consists of two parts:one is a tracking *** the context similarity of images in the data set,the purpose of tracking is to ensure that the position of the object is accurately known when the same species in the images are synthesized into a video with interval frames;the other is the saliency network,which is the basis of the most likely position of the object obtained from the tracking ***,the saliency network is used to complete the object *** verify the effectiveness of the network and consider the size of the data set,a lightweight CNN is used to identify the *** show that the accuracy of task recognition is improved by 1.89% by using the segmentation method proposed in this paper.

关键词： segmentation recognition tracking network saliency network

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Large language models:game-changers in the healthcare industry

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Science Bulletin 2025年第3期70卷 283-286页

作者： Bin Dong Li Zhang Jiajia Yuan Yang Chen Quanzheng Li Lin Shen Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China Center for Machine Learning Research Peking UniversityBeijing 100871China Peking University Changsha Institute for Computing and Digital Economy Changsha 410205China Center for Data Science Peking UniversityBeijing 100871China Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing)Peking University Cancer Hospital and InstituteBeijing 100142China Massachusetts General Hospital Boston 02114-2696USA Harvard Medical School Boston 02115USA

The healthcare industry faces core challenges,including increasingly complex operational processes and entities,the rapid development of medical knowledge,and the rising demand for interdisciplinary *** complexity of medical processes is evident in every aspect,from patient appointments,diagnoses,and treatments to follow-up tasks,all of which involve intricate data processing and decision-making procedures.

关键词： industry expertise evident

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Multi-Stage Bidirectional Cross-Attention Model for Predicting Prognosis in Multiple Peritoneum Lesions with Clinical Information

Multi-Stage Bidirectional Cross-Attention Model for Predicti...

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IEEE International Symposium on Biomedical Imaging

作者： Haoshen Li Jieyuan Cai Yiyuan Wei Zifan Chen Heyun Chen Jie Zhao Yanjie Shi Bin Dong Lei Tang Xiaotian Zhang Li Zhang Center for Data Science Peking University China Peking University Cancer Hospital & Institute China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Peritoneal metastasis occurs when cancer cells spread from the primary tumor to the peritoneum, leading to morphological alterations that significantly impact patient survival. The specific changes across multiple peritoneal sites can effectively indicate a patient's risk level and prognosis. However, the considerable variation in peritoneal shape, size, and location poses significant challenges for feature extraction and prognostic analysis. Traditional multi-instance learning approaches typically fuse instance features extracted by the backbone network at the final stage, but their lack of intermediate interaction limits the ability to capture both common and distinct features. To address this limitation, we propose a Multi-Stage BiDirectional Cross-Attention framework (MSBDCA). Our approach enhances feature extraction and prognostic analysis by facilitating interaction among different peritoneal instances and incorporating clinical information. Experimental comparisons with five baseline models demonstrate our method's superiority, achieving improvements of 8.7% in Area Under Curve (AUC) and 5.9% in Concordance Index (C_index). These results suggest a promising direction for survival and prognostic analysis using peritoneal lesions. Our code is available at https://***/HaoshenLi/MSBDCA.

关键词： learning systems Shape Predictive models Feature extraction Metastasis Lesions Clinical diagnosis Prognostics and health management Abdomen Biomedical imaging

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U-Star: AN Asymmetric U-Shaped Network Based on Element-Wise Multiplication to Segment Nuclei in H&E Stained Histological Images

U-Star: AN Asymmetric U-Shaped Network Based on Element-Wise...

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IEEE International Symposium on Biomedical Imaging

作者： Guangzhengao Yang Li Zhang Jie Zhao Zifan Chen Haoshen Li Bin Dong Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China National Biomedical Imaging Center Peking University China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Nuclei segmentation in Hematoxylin and Eosin (H&E) stained images plays a crucial role in cancer diagnosis and pathological evaluation, enabling pathologists to identify abnormal cells and assess their morphology and distribution. While current automated nuclei segmentation methods predominantly employ convolutional neural networks and attention mechanisms, the potential of element-wise multiplication has been largely unexplored. This paper introduces U-Star, a novel asymmetric segmentation network based on the star block that leverages element-wise multiplication. U-Star adopts the classic encoder-decoder architecture and innovatively implements star-connection as an alternative to traditional skip-connections. In experiments on an H&E stained image dataset, U-Star achieved superior performance with a Dice coefficient of 0.8783, accuracy of 0.9089, and IoU of 0.7929, significantly outperforming baseline models. Extensive ablation studies validate the effectiveness of the star-connection and demonstrate the advantages of our proposed framework. Beyond advancing the application of element-wise multiplication techniques, U-Star shows promising potential for broader applications in medical image segmentation.

关键词： Image segmentation Pathology Technological innovation Image analysis Stars Morphology Network architecture Medical diagnostic imaging Tuning Cancer

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MSI-UNet: A Flexible UNet-Based Multi-Scale Interactive Framework for 3D Gastric Tumor Segmentation on CT Scans

MSI-UNet: A Flexible UNet-Based Multi-Scale Interactive Fram...

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IEEE International Symposium on Biomedical Imaging

作者： Heyun Chen Zifan Chen Jie Zhao Haoshen Li Jiazheng Li Yiting Liu Mingze Yuan Peng Bao Xinyu Nan Bin Dong Lei Tang Li Zhang Center for Data Science Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Peking University Changsha Institute for Computing and Digital Economy China Peking University Cancer Hospital&Institute China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Accurate segmentation of gastric tumors is critical yet presents a formidable challenge in medical imaging, where conventional UNet-based frameworks, despite their prevalence, falter on intricate tumor samples due to their limited interactive capacities. The SAM-based segmentation methods address this shortcoming yet with insufficient accuracy. By ingeniously blending images with mask inputs, our MSI-UNet leverages a U-shaped design to deliver pixel-perfect segmentation accuracy, while a novel multi-scale attention module adeptly harnesses interaction points for refined information extraction. When benchmarked on gastric tumor segmentation tasks, MSI-UNet surpasses existing state-of-the-art methods, elevating the Dice Similarity Coefficient (DSC) from 74.82% to 79.3% and minimizing Average Surface Distance (ASD) from 6.46 to 1.98, achieving a comparable accuracy with inter-radiologist consistency of 79.7% DSC. Furthermore, our framework demonstrates superior predictive prowess in survival analysis, enhancing the C-index from 61.7% to 68.68%. Ample experimental comparisons have substantiated that MSI-UNet holds the potential to offer considerable assistance to healthcare professionals in managing and decoding subsequent medical procedures.

关键词： Image segmentation Accuracy Three-dimensional displays Medical services Predictive models Information retrieval Decoding

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