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Semivortex solitons and their excited states in spin-orbit-coupled binary bosonic condensates

Physical Review E 2024年第6期109卷 064201-064201页

作者： Haiming Deng Jinqing Li Zhaopin Chen Yaohui Liu Dong Liu Chunzhi Jiang Chao Kong Boris A. Malomed School of Physics and Electronic-Electrical Engineering Microelectronics and Optoelectronics Technology Key Laboratory of Hunan Higher Education Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Physics Department and Solid-State Institute Department of Physical Electronics School of Electrical Engineering Faculty of Engineering Instituto de Alta Investigación

It is known that two-dimensional two-component fundamental solitons of the semivortex (SV) type, with vorticities (s+,s−)=(0,1) in their components, are stable ground states (GSs) in the spin-orbit-coupled (SOC) binary Bose-Einstein condensate with the contact self-attraction acting in both components, in spite of the possibility of the critical collapse in the system. However, excited states (ESs) of the SV solitons, with the vorticity set (s+,s−)=(S+,S++1) and S+=1,2,3,..., are unstable in the same system. We construct ESs of SV solitons in the SOC system with opposite signs of the self-interaction in the two components. The main finding is stability of the ES-SV solitons, with the extra vorticity (at least) up to S+=6. The threshold value of the norm for the onset of the critical collapse, Nthr, in these excited states is higher than the commonly known critical value, Nc≈5.85, associated with the single-component Townes solitons, Nthr increasing with the growth of S+. A velocity interval for stable motion of the GS-SV solitons is found too. The results suggest a solution for the challenging problem of the creation of stable vortex solitons with high topological charges.

关键词： Solitons

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An Attention-Based Signed Distance Field Estimation Method for Hand-Object Reconstruction

An Attention-Based Signed Distance Field Estimation Method f...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Xinkang Zhang Xinhan Di Xiaokun Dai Xinrong Chen Academy for engineering & technology Fudan University shanghai China Bloo company shanghai China Academy for engineering & technology Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention Fudan University shanghai China

Joint reconstruction of hands and objects from monocular RGB images is a challenging task. In this work, we present a novel hybrid model for joint reconstruction of hands and objects. The model proposed consists of three key modules. Among them, multi-scale attention feature extractor is designed to enhance cross-scale in-formation extraction. Attention-based graph encoder can encode the graph information of the hands. Interacting attention module is applied to fuse information between hands and object. Test re-sults on ObMan dataset [6] show that our method outperforms the state-of-the-art method around 13.97% and 15.75% in $\mathrm{H}_{\text{se}}$ and $\mathrm{H}_{\text{je}}$ respectively.

关键词： Solid modeling Three-dimensional displays Fuses Estimation Virtual reality User interfaces Reconstruction algorithms

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Megavoltage Computed Tomography (MVCT) Imaging Quality Improvement via Convolutional Neural Network

Megavoltage Computed Tomography (MVCT) Imaging Quality Impro...

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International Conference on image, Vision and intelligent Systems, ICIVIS 2021

作者： Zhao, Zengjing Dong, Jiwen Niu, Sijie Zhang, Yan Zhu, Jian School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China Department of Radiation Oncology Physics and Technology Shandong Cancer Hospital affiliated to Shandong First Medical University Jinan250022 China

ISBN: (纸本)9789811669620

Clinical application of Megavoltage Computed Tomography (MVCT) is limited to three-dimensional visualization and position of the patient. The noise and low soft tissue contrast of MVCT make it difficult to offer accurate target volume delineation for adaptive radiotherapy and accurate image registration for image-guided radiotherapy, resulting in the less clinical application prospect of MVCT. In this work, we developed a deep learning-based approach to improve the MVCT image quality, so as to learn a mapping between MVCT images and paired planning kilovoltage CT (KVCT) images. The root mean squared error (RMSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) were used to quantify. Specifically, the PSNR and SSIM of the processed images improved to 27.94 ± 1.64 dB and 0.93 ± 0.01 compared with 24.67 ± 1.16 dB and 0.88 ± 0.02 in the original MVCT images, and the RMSE decreased from 23.5 ± 3.14HU to 16.34 ± 3.39HU. The processed images were obtained using the proposed method with less noise and higher soft tissue contrast in comparison with machine learning-based method. The processed MVCT increases the feasibility of depicting the anatomical delineation of tumor and organs at risk, and therefore enables the promising application of image-guided and adaptive helical TomoTherapy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Weakly supervised alignment and registration of MR-CT for cervical cancer radiotherapy

arXiv

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arXiv 2024年

作者： Zhang, Jiahao Gu, Yin Sun, Deyu Gao, Yuhua Cui, Ming Zhang, Teng Ma, He College of Medicine and Biological Information Engineering Northeasten University Liaoning Shenyang110169 China Department of Radiation Oncology Gastrointestinal and Urinary and Musculoskeletal Cancer Cancer Hospital of Dalian University of Technology Liaoning Shenyang110110 China Department of Gynecological Radiotherapy Cancer Hospital of China Medical University Liaoning Shenyang110110 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China

Cervical cancer is one of the leading causes of death in women, and brachytherapy is currently the primary treatment method. However, it is important to precisely define the extent of paracervical tissue invasion to improve cancer diagnosis and treatment options. The fusion of the information characteristics of both computed tomography (CT) and magnetic resonance imaging (MRI) modalities may be useful in achieving a precise outline of the extent of paracervical tissue invasion. Registration is the initial step in information fusion. However, when aligning multimodal images with varying depths, manual alignment is prone to large errors and is time-consuming. Furthermore, the variations in the size of the Region of Interest (ROI) and the shape of multimodal images pose a significant challenge for achieving accurate registration. In this paper, we propose a preliminary spatial alignment algorithm and a weakly supervised multimodal registration network. The spatial position alignment algorithm efficiently utilizes the limited annotation information in the two modal images provided by the doctor to automatically align multimodal images with varying depths. By utilizing aligned multimodal images for weakly supervised registration and incorporating pyramidal features and cost volume to estimate the optical flow, the results indicate that the proposed method outperforms traditional volume rendering alignment methods and registration networks in various evaluation metrics. This demonstrates the effectiveness of our model in multimodal image registration. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Sea Surface Temperature Prediction Approach Based on 3D CNN and LSTM with Attention Mechanism

Sea Surface Temperature Prediction Approach Based on 3D CNN ...

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International Conference on Advanced Communication Technology (ICACT)

作者： Baiyou Qiao Zhongqiang Wu Zhong Tang Gang Wu School of Computer Science and Engineering Northeastern University China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University China

ISBN: (数字)9791188428069

ISBN: (纸本)9781665447027

Sea Surface Temperature (SST) is an important physical quantity of the ocean system. The accurate prediction of SST is essential for studying physical ocean phenomena and forecasting the ocean environment information. In this paper, a SST prediction approach based on 3-Diminsional Convolutional Neural Network (3D CNN) and Long Short-Term Memory (LSTM) network with attention mechanism is proposed, which considers the spatial correlation and temporal dependency of SST data. Firstly, the machine learning algorithm XGBoost is used to extract the long period time features of each SST data. Then the 3D CNN is used to capture the spatial correlations among SST field data composed of multiple observation points in a selected sea area, followed by the LSTM model to extract the time dependency features of the SST field time series data, and the attention mechanism is added to weight the output of each step of LSTM model to adjust the prediction results and improve the prediction accuracy of the approach. A series of experimental results show that the proposed approach has lower complexity, higher training efficiency and prediction accuracy, which is significantly better than the existing prediction models.

关键词： Sea surface Three-dimensional displays Correlation Computational modeling Predictive models Feature extraction Ocean temperature

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A Multi-Objective Complex Network Pruning Framework Based on Divide-and-Conquer and Global Performance Impairment Ranking

SSRN

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SSRN 2024年

作者： Shang, Ronghua Zhu, Songling Zhang, Weitong Xu, Songhua Li, Yangyang Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education School of Artificial Intelligence Xidian University Shaanxi Province Xi’an710071 China Department of Health Management Institute of Medical Artificial Intelligence The Second Affiliated Hospital of Xi’an Jiaotong University Xi’an China

Model compression plays a vital role in the practical deployment of deep neural networks (DNNs), and evolutionary multi-objective (EMO) pruning is an essential tool in balancing the compression rate and performance of the DNNs. However, due to its populationbased nature, EMO pruning suffers from the complex optimization space and the resource-intensive structure verification process. To this end, a multi-objective complex network pruning framework based on divide-and-conquer and global performance impairment ranking (EMO-DIR) is proposed in this paper. Firstly, a divide-and-conquer EMO network pruning method is proposed, which decomposes the complex task of EMO pruning on the entire network into easier sub-tasks on multiple sub-networks. Because the network structure to be optimized and tested becomes smaller, this decomposition narrows the pruning optimization space and reduces resource consumption. However, the decomposition also breaks the cooperation between different sub-networks and hinders designing a joint pruning scheme. So, a sub-network training method based on cross-network constraints is designed, which could bridge independent EMO pruning sub-tasks, allowing them to collaborate better and improving the overall performance of the pruned network. Then, a multiple sub-networks joint pruning method based on EMO is proposed. This method combines the Pareto Fronts from EMO pruning results on multiple sub-networks through global performance impairment ranking to design a joint pruning scheme. The rich experiments on CIFAR-10/100 are conducted. EMO-DIR achieves a comparable performance with the state-of-the-art pruning methods. © 2024, The Authors. All rights reserved.

关键词： image classification

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Group-Spectral Superposition and Position Self-Attention Transformer for Hyperspectral image Classification

SSRN

引用

SSRN 2024年

作者： Zhang, Weitong Hu, Mingwei Hou, Sihan Shang, Ronghua Feng, Jie Xu, Songhua Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University Shaanxi Province Xian710071 China Department of Health Management Institute of Medical Artificial Intelligence The Second Affiliated Hospital of Xi’an Jiaotong University Xi’an China

At present, the existing Vision Transformer is sensitive to the original spectral data in the hyperspectral semantic segmentation process. Moreover, the weight relationship between the center pixel and the surrounding pixels is not properly handled when using the spatial-spectral feature method. To solve these problems, this paper proposes the Group-Spectral Superposition and Position Self-Attention Transformer (GSPST) for hyperspectral image classification. Firstly, while preserving the original spectral band data, GSPST groups the spectra and superimposes them in the spectral dimension. The superimposed data and the original spectral data are spliced in the channel dimension and then input into the network for learning. Secondly, GSPST processes the original spectral data and the superimposed spectral data in the spatial-spectral features separately using group convolution. This can offset the noise of the low-level features of the adversarial samples in the spectral dimension, thereby reducing its impact on the classification accuracy. Finally, GSPST modified the internal attention mechanism formula of Transformer to avoid the problem of pixels at different positions in hyperspectral images having the same contribution to image classification. A position self-attention mechanism is proposed by introducing a matrix, which can assign different weights to the center pixel and the rest of the pixels to affect its importance for image classification. By introducing position weight information into the self-attention formula to disassemble redundant information in hyperspectral images, thereby improving model robustness. This mechanism can effectively emphasize the role of center pixels in semantic segmentation. Compared with six state-of-the-art algorithms, simulation results demonstrate that GSPST has higher accuracy after adversarial attacks. © 2024, The Authors. All rights reserved.

关键词： Pixels

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Towards Accurate Unified Anomaly Segmentation

Towards Accurate Unified Anomaly Segmentation

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Wenxin Ma Qingsong Yao Xiang Zhang Zhelong Huang Zihang Jiang S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) USTC Suzhou Institute for Advance Research Suzhou Jiangsu China Stanford University Palo Alto California United State School of Medicine Shanghai University Shanghai China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Unsupervised anomaly detection (UAD) from images strives to model normal data distributions, creating discriminative representations to distinguish and precisely localize anomalies. Despite recent advancements in the efficient and unified one-for-all scheme, challenges persist in accurately segmenting anomalies for further monitoring. Moreover, this problem is obscured by the widely-used AUROC metric under imbalanced UAD settings. This motivates us to emphasize the significance of precise segmentation of anomaly pixels using pAP and DSC as metrics. To address the unsolved segmentation task, we introduce the Unified Anomaly Segmentation (UniAS). UniAS presents a multi-level hybrid pipeline that progressively enhances normal information from coarse to fine, incorporating a novel multi-granularity gated CNN (MGG-CNN) into Transformer layers to explicitly aggregate local details from different granularities. UniAS achieves state-of-the-art anomaly segmentation performance, attaining 65.12/59.33 and 40.06/32.50 in pAP/DSC on the MVTec-AD and VisA datasets, respectively, surpassing previous methods significantly. The codes are shared at https://***/Mwxinnn/UniAS.

关键词： Measurement image segmentation Computer vision Codes Pipelines Logic gates Transformers Data models Monitoring Anomaly detection

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A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality medical images

arXiv

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arXiv 2024年

作者： Hou, Qingshan Cheng, Shuai Cao, Peng Yang, Jinzhu Liu, Xiaoli Zaiane, Osmar R. Tham, Yih Chung Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Alberta Machine Intelligence Institute University of Alberta Edmonton Canada Yong Loo Lin School of Medicine National University of Singapore Singapore

Representation learning offers a conduit to elucidate distinctive features within the latent space and interpret the deep models. However, the randomness of lesion distribution and the complexity of low-quality factors in medical images pose great challenges for models to extract key lesion features. Disease diagnosis methods guided by contrastive learning (CL) have shown significant advantages in lesion feature representation. Nevertheless, the effectiveness of CL is highly dependent on the quality of the positive and negative sample pairs. In this work, we propose a clinical-oriented multi-level CL framework that aims to enhance the model's capacity to extract lesion features and discriminate between lesion and low-quality factors, thereby enabling more accurate disease diagnosis from low-quality medical images. Specifically, we first construct multi-level positive and negative pairs to enhance the model's comprehensive recognition capability of lesion features by integrating information from different levels and qualities of medical images. Moreover, to improve the quality of the learned lesion embeddings, we introduce a dynamic hard sample mining method based on self-paced learning. The proposed CL framework is validated on two public medical image datasets, EyeQ and Chest X-ray, demonstrating superior performance compared to other state-of-the-art disease diagnostic methods. Copyright © 2024, The Authors. All rights reserved.

关键词： image enhancement

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A Nanoprobe-Integrated Capillary Sensing System for Highly Sensitive Sers Detection of Neutral Α-Glucosidase in Semen

SSRN

引用

SSRN 2024年

作者： Wei, Hongguo Xu, Fan Xu, Tao Wang, Yunxin Li, Deliang Ren, Yuetian Yan, Zejun Zhang, Zhe Chen, Keren Chen, Shuo College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Department of Urology The First Affiliated Hospital of Ningbo University Ningbo315010 China Foshan Graduate School of Innovation Northeastern University Foshan528311 China Department of Urology The First Affiliated Hospital of China Medical University Shenyang110001 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110169 China

The incidence of male infertility is increasing year by year, with obstructive azoospermia being the main cause of infertility. The activity of neutral α-glucosidase plays an important role in diagnosing obstructive azoospermia. In order to effectively and non-invasively distinguish obstructive azoospermia, we have developed a rapid and sensitive SERS detection platform for neutral α-glucosidase in semen by combining nanoprobes with capillary systems labeled with capture antibodies. This platform utilizes a minimally invasive sampling method of capillary, avoiding the complex sampling and separation steps in conventional clinical examinations. By combining Ag@MnO2 core-shell composite nanoparticles prepared through rational design with tetramethylbenzidine (TMB), precise differentiation of obstructive azoospermia patients and healthy donors can be achieved in a short time. The successful implementation of this nanoprobe integrated capillary SERS platform provides a novel, efficient, and clinically feasible method for highly sensitive detection of neutral α-glucosidase in semen and precise diagnosis of obstructive azoospermia, with broad prospects for biomedical applications. © 2024, The Authors. All rights reserved.

关键词： Nanoprobes

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