检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Phani, Siginamsetty Abdul, Ashu Kuo, Chang-Fu Chen, Jenhui Dash, Jatindra Kumar Department of Computer Science and Engineering SRM University AP Managalagiri Neerukonda Andhra Pradesh 522502 India Centre for Computational and Integrative Sciences SRM University AP Managalagiri Neerukonda Andhra Pradesh 522502 India Center for Artificial Intelligence in Medicine Chang Gung Memorial Hospital Taoyuan33302 Taiwan Division of Rheumatology Immunology and Allergy Chang Gung Memorial Hospital Taoyuan33302 Taiwan School of Medicine Chang Gung Memorial Hospital Taoyuan33302 Taiwan Department of Computer Science and Information Engineering Chang Gung Memorial Hospital Taoyuan33302 Taiwan Division of Breast Surgery and General Surgery Department of Surgery Chang Gung University Guishan Dist Taoyuan33302 Taiwan

Accurate interpretation of medical images is crucial for effective diagnosis and treatment planning, yet remains challenging due to data complexity, variability, and hallucination issues. To address these challenges, we introduce a Multimodal Medical Retrieval Augmented Generation (MMRAG) approach for automating radiology report generation from chest x-rays and brain MRI scans. This approach involves fine-tuning the idefics-80B parameters model with Quantized Low-Rank Adaptation (QLoRA), enhancing efficiency in processing large-scale multimodal data by reducing model weights and improving inference speed. By converting the dataset into multimodal embeddings and creating a unified vector space for images and text, the proposed system retrieves the most relevant report from a pre-constructed vector store when presented with a new image. Using Retrieval-Augmented Generation (RAG) with the fine-tuned model, it generates comprehensive radiology reports, significantly improving the efficiency and thoroughness of automated medical report generation. The proposed MMRAG potentially reduces radiologist’s workload and enhances diagnostic accuracy by integrating multimodal learning with retrieval-augmented approach, addressing critical challenges in medical imaging, including hallucination mitigation and computational efficiency during inference. Evaluation on publicly available datasets like MIMIC-CXR and CGBrainMRI demonstrates superior performance compared to existing approaches. © 2024, The Authors. All rights reserved.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI

arXiv

引用

arXiv 2022年

作者： Xu, Junshen Moyer, Daniel Grant, P. Ellen Golland, Polina Iglesias, Juan Eugenio Adalsteinsson, Elfar Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA United States Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children’s Hospital BostonMA United States Harvard Medical School BostonMA United States Centre for Medical Image Computing Department of Medical Physics and Biomedical Engineering University College London United Kingdom Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School Boston United States Institute for Medical Engineering and Science MIT CambridgeMA United States

Volumetric reconstruction of fetal brains from multiple stacks of MR slices, acquired in the presence of almost unpredictable and often severe subject motion, is a challenging task that is highly sensitive to the initialization of slice-to-volume transformations. We propose a novel slice-to-volume registration method using Transformers trained on synthetically transformed data, which model multiple stacks of MR slices as a sequence. With the attention mechanism, our model automatically detects the relevance between slices and predicts the transformation of one slice using information from other slices. We also estimate the underlying 3D volume to assist slice-to-volume registration and update the volume and transformations alternately to improve accuracy. Results on synthetic data show that our method achieves lower registration error and better reconstruction quality compared with existing state-of-the-art methods. Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion. Copyright © 2022, The Authors. All rights reserved.

关键词： Brain

来源：评论

学校读者我要写书评

暂无评论

Gender bias in transformers: A comprehensive review of detection and mitigation strategies

Natural Language Processing Journal

引用

Natural Language Processing Journal 2024年 6卷

作者： Praneeth Nemani Yericherla Deepak Joel Palla Vijay Farhana Ferdouzi Liza Department of Computer Science and Engineering IIIT Naya Raipur Chhattisgarh India College of Engineering and Applied Science University of Colorado Boulder CO USA Department of Data Science and Artificial Intelligence IIIT Naya Raipur Chhattisgarh India School of Computing Sciences University of East Anglia (UEA) Norwich United Kingdom

Gender bias in artificial intelligence (AI) has emerged as a pressing concern with profound implications for individuals’ lives. This paper presents a comprehensive survey that explores gender bias in Transformer models from a linguistic perspective. While the existence of gender bias in language models has been acknowledged in previous studies, there remains a lack of consensus on how to measure and evaluate this bias effectively. Our survey critically examines the existing literature on gender bias in Transformers, shedding light on the diverse methodologies and metrics employed to assess bias. Several limitations in current approaches to measuring gender bias in Transformers are identified, encompassing the utilization of incomplete or flawed metrics, inadequate dataset sizes, and a dearth of standardization in evaluation methods. Furthermore, our survey delves into the potential ramifications of gender bias in Transformers for downstream applications, including dialogue systems and machine translation. We underscore the importance of fostering equity and fairness in these systems by emphasizing the need for heightened awareness and accountability in developing and deploying language technologies. This paper serves as a comprehensive overview of gender bias in Transformer models, providing novel insights and offering valuable directions for future research in this critical domain.

关键词： Gender bias Transformer models Bias mitigation Binary gender assumption Self-attention

来源：评论

学校读者我要写书评

暂无评论

Polarization-controlled dynamically switchable high-harmonic generation from all-dielectric metasurfaces governed by dual bound states in the continuum

arXiv

引用

arXiv 2022年

作者： Xiao, Shuyuan Qin, Meibao Duan, Junyi Wu, Feng Liu, Tingting Institute for Advanced Study Nanchang University Nanchang330031 China Jiangxi Key Laboratory for Microscale Interdisciplinary Study Nanchang University Nanchang330031 China School of Physics and Materials Science Nanchang University Nanchang330031 China School of Optoelectronic Engineering Guangdong Polytechnic Normal University Guangzhou510665 China Institute of Photonic Chips University of Shanghai for Science and Technology Shanghai200093 China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Tailoring optical nonlinear effects (e.g. harmonic generation, sum-frequency mixing, etc.) in the recently emerging all-dielectric platform is important for both the fundamental science and industrial development of high-efficiency, ultrafast, and miniaturized photonic devices. In this work, we propose a novel paradigm for dynamically switchable high-harmonic generation in Silicon nanodimer metasurfaces by exploiting polarization-controlled dual bound states in the continuum (BIC). Owing to the high-quality factor of BIC resonances, efficient harmonic signals including the third-harmonic generation and fifth-harmonic generation from a direct process as well as a cascaded process by degenerate four-wave mixing are obtained. Moreover, the BICs and their resonantly enhanced harmonics can be switched on or off with high selectivity respect to the fundamental pump polarization. Compared with previous reports, our work provide a simple but effective tuning strategy by fully exploring the structural symmetry and polarization degree of freedom rather than resorting to additional external stimuli, which would have great advantages in smart designing tunable and switchable nonlinear light source for chip-scale applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Harmonic generation

来源：评论

学校读者我要写书评

暂无评论

Quantum Linear System Solvers: A Survey of Algorithms and Applications

arXiv

引用

arXiv 2024年

作者： Morales, Mauro E.S. Pira, Lirandë Schleich, Philipp Koor, Kelvin Costa, Pedro C.S. An, Dong Aspuru-Guzik, Alán Lin, Lin Rebentrost, Patrick Berry, Dominic W. Joint Center for Quantum Information and Computer Science University of Maryland United States Centre for Quantum Software and Information University of Technology Sydney Australia Centre for Quantum Technologies National University of Singapore Singapore Department of Computer Science University of Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada ContinoQuantum Sydney Australia Beijing International Center for Mathematical Research Peking University Beijing China Department of Chemistry University of Toronto Canada Canadian Institute for Advanced Research Toronto Canada Acceleration Consortium Toronto Canada Department of Chemical Engineering & Applied Chemistry University of Toronto Toronto Canada Department of Materials Science & Engineering University of Toronto Toronto Canada Lebovic Fellow Canadian Institute for Advanced Research Toronto Canada Department of Mathematics University of California Berkeley United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley United States Department of Computer Science National University of Singapore Singapore School of Mathematical and Physical Sciences Macquarie University Sydney Australia

Solving linear systems of equations plays a fundamental role in numerous computational problems from different fields of science. The widespread use of numerical methods to solve these systems motivates investigating the feasibility of solving linear systems problems using quantum computers. In this work, we provide a survey of the main advances in quantum linear systems algorithms, together with some applications. We summarize and analyze the main ideas behind some of the algorithms for the quantum linear systems problem in the literature. The analysis begins by examining the Harrow-Hassidim-Lloyd (HHL) solver. We note its limitations and reliance on computationally expensive quantum methods, then highlight subsequent research efforts which aimed to address these limitations and optimize runtime efficiency and precision via various paradigms. We focus in particular on the post-HHL enhancements which have paved the way towards optimal lower bounds with respect to error tolerance and condition number. By doing so, we propose a taxonomy that categorizes these studies. Furthermore, by contextualizing these developments within the broader landscape of quantum computing, we explore the foundational work that have inspired and informed their development, as well as subsequent refinements. Finally, we discuss the potential applications of these algorithms in differential equations, quantum machine learning, and many-body physics. © 2024, CC BY.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

High-efficiency optical frequency mixing in an all-dielectric metasurface enabled by multiple bound states in the continuum

arXiv

引用

arXiv 2022年

作者： Liu, Tingting Qin, Meibao Wu, Feng Xiao, Shuyuan Institute of Photonic Chips University of Shanghai for Science and Technology Shanghai200093 China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Physics and Materials Science Nanchang University Nanchang330031 China School of Optoelectronic Engineering Guangdong Polytechnic Normal University Guangzhou510665 China Institute for Advanced Study Nanchang University Nanchang330031 China Jiangxi Key Laboratory for Microscale Interdisciplinary Study Nanchang University Nanchang330031 China

We present nonlinear optical four-wave mixing in a silicon nanodisk dimer metasurface. Under the oblique incident plane waves, the designed metasurface exhibits a multi-resonant feature with simultaneous excitations of three quasi-bound states in the continuum (BIC). Through employing these quasi-BICs with maximizing electric field energy at the input bump wavelengths, significant enhancement of third-order nonlinear processes including third-harmonic generation, degenerate and non-degenerate four-wave mixing are demonstrated, giving rise to ten new frequencies in the visible wavelengths. This work may lead to a new frontier of ultracompact optical mixer for applications in optical circuitry, ultrasensitive sensing, and quantum nanophotonics. Copyright © 2022, The Authors. All rights reserved.

关键词： Four wave mixing

来源：评论

学校读者我要写书评

暂无评论

Quantum Computing: Vision and Challenges

arXiv

引用

arXiv 2024年

作者： Gill, Sukhpal Singh Cetinkaya, Oktay Marrone, Stefano Claudino, Daniel Haunschild, David Schlote, Leon Wu, Huaming Ottaviani, Carlo Liu, Xiaoyuan Machupalli, Sree Pragna Kaur, Kamalpreet Arora, Priyansh Liu, Ji Farouk, Ahmed Song, Houbing Herbert Uhlig, Steve Ramamohanarao, Kotagiri School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Dipartimento di Matematica e Fisica Università della Campania "Luigi Vanvitelli" Italy Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN United States Detecon International GmbH Munich Germany DB Cargo Berlin Germany Center for Applied Mathematics Tianjin University Tianjin China Department of Computer Science York Centre for Quantum Technologies University of York York United Kingdom Fujitsu Research of America Inc. Santa ClaraCA United States Information Networking Institute Carnegie Mellon University United States Cymax Group Technologies BC Canada Microsoft Schiphol Netherlands Mathematics and Computational Research Division Argonne National Laboratory IL United States Department of Computer Science Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt Baltimore United States The University of Melbourne VIC Australia

The recent development of quantum computing, which uses entanglement, superposition, and other quantum fundamental concepts, can provide substantial processing advantages over traditional computing. These quantum features help solve many complex problems that cannot be solved otherwise with conventional computing methods. These problems include modeling quantum mechanics, logistics, chemical-based advances, drug design, statistical science, sustainable energy, banking, reliable communication, and quantum chemical engineering. The last few years have witnessed remarkable progress in quantum software and algorithm creation and quantum hardware research, which has significantly advanced the prospect of realizing quantum computers. It would be helpful to have comprehensive literature research on this area to grasp the current status and find outstanding problems that require considerable attention from the research community working in the quantum computing industry. To better understand quantum computing, this paper examines the foundations and vision based on current research in this area. We discuss cutting-edge developments in quantum computer hardware advancement and subsequent advances in quantum cryptography, quantum software, and high-scalability quantum computers. Many potential challenges and exciting new trends for quantum technology research and development are highlighted in this paper for a broader debate. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

CMR×Recon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

arXiv

引用

arXiv 2023年

作者： Wang, Chengyan Lyu, Jun Wang, Shuo Qin, Chen Guo, Kunyuan Zhang, Xinyu Yu, Xiaotong Li, Yan Wang, Fanwen Jin, Jianhua Shi, Zhang Xu, Ziqiang Tian, Yapeng Hua, Sha Chen, Zhensen Liu, Meng Sun, Mengting Kuang, Xutong Wang, Kang Wang, Haoran Li, Hao Chu, Yinghua Yang, Guang Bai, Wenjia Zhuang, Xiahai Wang, He Qin, Jing Qu, Xiaobo Human Phenome Institute Fudan University Shanghai China School of Nursing The Hong Kong Polytechnic University Hong Kong Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Bioengineering/˜Imperial-X Imperial College London United Kingdom School of Data Science Fudan University Shanghai China Department of Radiology Zhongshan Hospital Fudan University Shanghai China School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China Department of Computer Science The University of Texas Dallas United States Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Simens Healthineers Ltd. China Department of Brain Sciences Imperial College London London United Kingdom Department of Computing Imperial College London London United Kingdom

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have not been publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. Manual segmentations of the myocardium and chambers of all the subjects are also provided within the dataset. Scripts of state-of-the-art reconstruction algorithms were also provided as a point of reference. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community. Researchers can access the dataset at https://***/#!Synapse:syn51471091/wiki/. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Spatial-Temporal Graph Information

arXiv

引用

arXiv 2021年

作者： Li, Jinhao Zhang, Ruichang Wang, Hao Liu, Zhi Lai, Hongyang Zhang, Yanru The School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Shenzhen Institute of Advanced Study University of Electronic Science and Technology of China China The Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University MelbourneVIC3800 Australia Department of Computer and Network Engineering University of Electro-Communications Tokyo Japan

Renewable energy resources (RERs) have been increasingly integrated into large-scale power systems. Considering uncertainties and voltage fluctuation issues introduced by RERs, in this paper, we propose a deep reinforcement learning (DRL)based strategy leveraging spatial-temporal (ST) graphical information of power systems, to dynamically search for the optimal power flow (OPF), of power systems with a high uptake of RERs. We formulate the OPF problem as a multi-objective optimization problem considering generation cost, voltage fluctuation, and transmission loss, and employ deep deterministic policy gradient (DDPG) to learn an optimal strategy for OPF. Moreover, given that the nodes in power systems are self-correlated and interrelated in temporal and spatial views, we develop a multi-grained attention-based spatial-temporal graph convolution network (MG-ASTGCN) for extracting ST graphical correlations and features, aiming to provide graphical knowledge of power systems for its sequential DDPG in our DRL algorithm to more effectively solve OPF. We validate our algorithm on modified IEEE 33, 69, and 118-bus radial distribution systems and demonstrate that our algorithm outperforms other benchmark algorithms. Our experimental results also reveal that our MG-ASTGCN can significantly accelerate DDPG’s training process and performance in solving OPF. The proposed DRL-based strategy also improves power systems’ robustness in the presence of node faults, especially for large-scale power systems. © 2021, CC BY.

关键词： Renewable energy resources

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Techniques for Automatic Lateral X-ray Cephalometric Landmark Detection: Is the Problem Solved?

arXiv

引用

arXiv 2024年

作者： Zhang, Hongyuan Wang, Ching-Wei Muzakky, Hikam Dai, Juan Li, Xuguang Ma, Chenglong Wu, Qian Cui, Xianan Xu, Kunlun He, Pengfei Guo, Dongqian Wang, Xianlong Lee, Hyunseok Zhong, Zhangnan Zhu, Zhu Huang, Bingsheng Medical AI Lab School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen518060 China Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taiwan Department of Stomatology Shenzhen University General Hospital Shenzhen University Shenzhen518055 China Institute of Stomatological Research Shenzhen University Shenzhen518055 China Chohotech Technology Co. Ltd. Lianchuang Street Yuhang District Hangzhou311422 China Singapore School of Computer Science Wuhan University Wuhan430072 China Wangxuan Institute of Computer Technology Peking University Beijing100871 China School of Artificial Intelligence Xidian University Xi’an710126 China State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China The Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China Digital Healthcare Department Daegu-Gyeongbuk Medical Innovation Foundation Daegu427724 Korea Republic of School of Life Science South China Normal University Guangzhou510631 China Information and Data Department Children’s Hospital Zhejiang University School of Medicine Hangzhou310000 China

Localization of the craniofacial landmarks from lateral cephalograms is a fundamental task in cephalometric analysis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Cephalometric Landmark Detection (CL-Detection)" dataset, which is the largest publicly available and comprehensive dataset for cephalometric landmark detection. This multi-center and multi-vendor dataset includes 600 lateral X-ray images with 38 landmarks acquired with different equipment from three medical centers. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go for cephalometric landmark detection. Following the 2023 MICCAI CL-Detection Challenge, we report the results of the top ten research groups using deep learning methods. Results show that the best methods closely approximate the expert analysis, achieving a mean detection rate of 75.719% and a mean radial error of 1.518 mm. While there is room for improvement, these findings undeniably open the door to highly accurate and fully automatic location of craniofacial landmarks. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for the community to benchmark future algorithm developments at https: //***/. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：