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ChrSNet: Chromosome Straightening using Self-attention Guided Networks

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arXiv 2022年

作者： Zheng, Sunyi Li, Jingxiong Shui, Zhongyi Zhu, Chenglu Zhang, Yunlong Chen, Pingyi Yang, Lin Artificial Intelligence and Biomedical Image Analysis Lab School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China College of Computer Science and Technology Zhejiang University China

Karyotyping is an important procedure to assess the possible existence of chromosomal abnormalities. However, because of the non-rigid nature, chromosomes are usually heavily curved in microscopic images and such deformed shapes hinder the chromosome analysis for cytogeneticists. In this paper, we present a self-attention guided framework to erase the curvature of chromosomes. The proposed framework extracts spatial information and local textures to preserve banding patterns in a regression module. With complementary information from the bent chromosome, a refinement module is designed to further improve fine details. In addition, we propose two dedicated geometric constraints to maintain the length and restore the distortion of chromosomes. To train our framework, we create a synthetic dataset where curved chromosomes are generated from the real-world straight chromosomes by grid-deformation. Quantitative and qualitative experiments are conducted on synthetic and real-world data. Experimental results show that our proposed method can effectively straighten bent chromosomes while keeping banding details and length. Copyright © 2022, The Authors. All rights reserved.

关键词： Chromosomes

Query lower bounds for log-concave sampling

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arXiv 2023年

作者： Chewi, Sinho Dios Pont, Jaume de Li, Jerry Lu, Chen Narayanan, Shyam School of Mathematics at Institute for Advanced Study United States Department of Mathematics at University of California Los Angeles United States Microsoft Research United States Department of Mathematics at Massachusetts Institute of Technology United States Department of Electrical Engineering and Computer Science at Massachusetts Institute of Technology United States

Log-concave sampling has witnessed remarkable algorithmic advances in recent years, but the corresponding problem of proving lower bounds for this task has remained elusive, with lower bounds previously known only in dimension one. In this work, we establish the following query lower bounds: (1) sampling from strongly log-concave and log-smooth distributions in dimension d ≥ 2 requires Ω(log κ) queries, which is sharp in any constant dimension, and (2) sampling from Gaussians in dimension d (hence also from general log-concave and log-smooth distributions in dimension d) requires Ω(min(e √κ log d, d)) queries, which is nearly sharp for the class of Gaussians. Here κ denotes the condition number of the target distribution. Our proofs rely upon (1) a multiscale construction inspired by work on the Kakeya conjecture in geometric measure theory, and (2) a novel reduction that demonstrates that block Krylov algorithms are optimal for this problem, as well as connections to lower bound techniques based on Wishart matrices developed in the matrix-vector query literature. Copyright © 2023, The Authors. All rights reserved.

关键词： Number theory

Self-adaptive vision-language model for 3D segmentation of pulmonary artery and vein

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arXiv 2025年

作者： Guo, Xiaotong Yang, Deqian Wang, Dan Zhao, Haochen Li, Yuan Sui, Zhilin Zhou, Tao Zhang, Lijun Meng, Yanda State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Department of Thoracic Surgery National Clinical Research Center for Cancer Cancer Hospital Shenzhen Hospital China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou310024 China R&D Center Guangxi Huayi Artificial Intelligence Medical Technology Co. Ltd. China School of Computer Science and Engineering Beihang University Beijing China Department of Computer Science University of Exeter Exeter United Kingdom Department of Cardiovascular & Metabolic Medicine University of Liverpool Liverpool United Kingdom Guangzhou Jiayi Software Technology Co. Ltd. China

Accurate segmentation of pulmonary structures is crucial in clinical diagnosis, disease study, and treatment planning. Significant progress has been made in deep learning-based segmentation techniques, but most require much labeled data for training. Consequently, developing precise segmentation methods that demand fewer labeled datasets is paramount in medical image analysis. The emergence of pre-trained vision-language foundation models, such as CLIP, recently opened the door for universal computer vision tasks. Exploiting the generalization ability of these pre-trained foundation models on downstream tasks, such as segmentation, leads to unexpected performance with a relatively small amount of labeled data. However, exploring these models for pulmonary artery-vein segmentation is still limited. This paper proposes a novel framework called Language-guided self-adaptive Cross-Attention Fusion Framework. Our method adopts pre-trained CLIP as a strong feature extractor for generating the segmentation of 3D CT scans, while adaptively aggregating the cross-modality of text and image representations. We propose a s pecially designed adapter module to fine-tune pre-trained CLIP with a self-adaptive learning strategy to effectively fuse the two modalities of embeddings. We extensively validate our method on a local dataset, which is the largest pulmonary artery-vein CT dataset to date and consists of 718 labeled data in total. The experiments show that our method outperformed other state-of-the-art methods by a large margin. Our data and code will be made publicly available upon acceptance. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

Enhanced Population on Ionic Excited States by Synchronized Ionization and Multiphoton Resonance

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arXiv 2023年

作者： Chen, Yewei Lei, Hongbin Wang, Quanjun Xie, Hongqiang Zhang, He Lu, Xu Zhang, Ning Huang, Shunlin Wu, Yuzhu Liu, Jianpeng Zhang, Qian Liu, Yi Zhao, Zengxiu Zhao, Jing Yao, Jinping State Key Laboratory of High Field Laser Physics Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Department of Physics National University of Defense Technology Changsha410073 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Science East China University of Technology Nanchang330013 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China School of Microelectronics Shanghai University Shanghai200444 China

We study population distributions and lasing actions of N2+ drieen by femtosecond lasers with earious waeelengths, and uncoeer an efficient ionic excitation mechanism induced by synchronized ionization and multiphoton resonance. Our results show that the strongest N2+ lasing appears around 1000 nm pump waeelength. At the optimal waeelength, the pump-energy threshold for air lasing generation is reduced by fiee folds compared with that required by the preeious 800 nm pump laser. Simulations based on the ionization-coupling model indicate that although the Stark-assisted three-photon resonance can be satisfied within a broad pump waeelength range, the optimal pump waeelength arises when the dynamic three-photon resonance temporally synchronizes with the ionization injection. In this case, the ionic dipoles created at each half optical cycle haee the same phase. The dipole phase locking promotes the continuous population transfer from ionic ground state to the excited state, gieing rise to a dramatic increase of excited-state population. This work proeides new insight on the photoexcitation mechanism of ions in strong laser fields, and opens up a route for optimizing ionic radiations. Copyright © 2023, The Authors. All rights reserved.

关键词： Excited states

RS-ModCubes: Self-reconfigurable, Scalable, Modular Cubic Robots for Underwater Multitasking

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arXiv 2024年

作者： Zheng, Jiaxi Dai, Guangmin He, Botao Mu, Zhaoyang Meng, Zhaochen Zhang, Tianyi Zhi, Weiming Fan, Dixia Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Zhejiang University Zhejiang Hangzhou310027 China Key Laboratory of Coastal Environment and Resources of Zhejiang Province School of Engineering Westlake University Zhejiang Hangzhou310030 China Department of Computer Science University of Maryland College ParkMD20742 United States Institute of Advanced Technology Westlake Institute for Advanced Study Zhejiang Hangzhou310024 China

This paper introduces a reconfigurable underwater robot system, RS-ModCubes, which allows scalable multi-robot configuration. An RS-ModCubes system consists of multiple ModCube modules, that can travel underwater with 6 DoFs and assemble with each other into a larger structure with onboard electromagnets. This system is designed to support diverse underwater applications through modularity and reconfigurability, eliminating the need to customize mechanical designs for a specific task. We present a modeling framework tailored for such reconfigurable robot systems, with hydrodynamics integrated based on Monte Carlo approximation. A model-based feedforward Proportional Derivative (PD) controller serves as the baseline for control. Inspired by dexterous manipulation, we evaluated the robot’s maximum task wrench space and power efficiency, compared against four commercial underwater robots. RS-ModCubes is validated via both real-world experiments and simulations, including individual and multi-module trajectory tracking and hovering docking. We open-source the design and code to facilitate future research: https://***/***/. © 2024, CC BY.

关键词： Machine design

Editorial for the Special Issue on Laser Micro/Nano-Manufacturing

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engineering 2025年

作者： Hong, Minghui Chen, Lianwei Cao, Tun Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen University Xiamen361005 China Department of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore National Key Laboratory of Optical Field Manipulation Science and Technology Chinese Academy of Sciences Chengdu610209 China State Key Laboratory of Optical Technologies on Nano-Fabrication and Micro-Engineering Institute of Optics and Electronics Chinese Academy of Sciences Chengdu610209 China School of Optoelectronic Engineering and Instrumentation Science Dalian University of Technology Dalian116024 China

Solving Aspect category sentiment analysis as a text generation task

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arXiv 2021年

作者： Liu, Jian Teng, Zhiyang Cui, Leyang Liu, Hanmeng Zhang, Yue School of Computer Science Fudan University School of Engineering Westlake University Institute of Advanced Technology Westlake Institute for Advanced Study

Aspect category sentiment analysis has attracted increasing research attention. The dominant methods make use of pre-trained language models by learning effective aspect category-specific representations, and adding specific output layers to its pre-trained representation. We consider a more direct way of making use of pre-trained language models, by casting the ACSA tasks into natural language generation tasks, using natural language sentences to represent the output. Our method allows more direct use of pre-trained knowledge in seq2seq language models by directly following the task setting during pre-training. Experiments on several benchmarks show that our method gives the best reported results, having large advantages in few-shot and zero-shot settings. © 2021, CC BY-NC-ND.

关键词： Sentiment analysis

Solution for Agri Disaster Using Climate Changes Adaptation by Ml

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Solution for Agri Disaster Using Climate Changes Adaptation ...

International Conference on Contemporary Computing and Informatics (IC3I)

作者： L. Ganesh R. Prasanna Venkatesh Kaushal Kumar Khushboo Kumari Sangeeta Borkakoty A. Raju Robotics and Automation Dept. Rajalakshmi Engineering College Chennai Tamilnadu India Department of Mechanical Engineering SACS MAVMM Engineering College Madurai Tamilnadu India Department of Botany School of Life Sciences Mahatma Gandhi Central University Bihar India Department of Computer Science & Electronic University of Science & Technology Meghalaya Department of Commerce KLEF Vaddeswaram Green Field Vijayawada

This paper proposes using good sized getting to know fashions to recognize lesions of cotton leaves in the context of pictures of the reaping within the subject. Cotton is among the maximum economically essential plant harvests, as it's far grown all over the international. Its spread in tropical areas has made it the goal of a wide variety of farming irritations and illnesses, necessitating succesful plans. moreover, the consequences of commonplace disturbances and disorders are difficult to isolate inside the early stages, and the author may additionally battle to locate the suitable visible verification of a scientific difficulty. To help in resolving the difficulty, it contemporary inquiry proposes a solution that takes into consideration substantial training within the filtering of cotton leaves, making it possible to reveal the cotton yield's health and make higher judgments approximately its management. The studying models GoogleNet like Resnet50, which use convolutional cerebrum associations, performed an accuracy of 86.6 percentage and 89.2 percentage, respectively. assist vector machines, Closest k-pals, faux creativeness associations, and neuro-tender are some of the extraordinary as well as everyday tactics again for treatment of photographs. The convolutional cerebellum networks ended up being 25% extra unique, implying that this method can upload to a more strong and short investigation of the flora looking to fill inside the field.

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia computer science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) computer-aided diagnosis (CAD)