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A Localization Method of High Energy Transients for All-sky Gamma-ray Monitor

Research in Astronomy and Astrophysics 2024年第10期24卷 25-35页

作者： Yi Zhao Wangchen Xue Shaolin Xiong Qi Luo Yuanhao Wang Jiacong Liu Heng Yu Xiaoyun Zhao Yue Huang Jinyuan Liao Jianchao Sun Xiaobo Li Qibin Yi Ce Cai Shuo Xiao Shenglun Xie Chao Zheng Yanqiu Zhang Chenwei Wang Wenjun Tan Zhiwei Guo Chaoyang Li Zhenghua An Gang Chen Yanqi Du Min Gao Ke Gong Dongya Guo Jiang He Jianjian He Bing Li Gang Li Xinqiao Li Jing Liang Xiaohua Liang Yaqing Liu Xiang Ma Rui Qiao Liming Song Xinying Song Xilei Sun Jin Wang Ping Wang Xiangyang Wen Hong Wu Yanbing Xu Sheng Yang Dali Zhang Fan Zhang Hongmei Zhang Peng Zhang Shu Zhang Zhen Zhang Shijie Zheng Keke Zhang Xingbo Han Haiyan Wu Hu Tai Hao Geng Gaopeng Lu Wei Xu Fanchao Lyu Hongbo Zhang Fangjun Lu Shuangnan Zhang Department of Astronomy Beijing Normal UniversityBeijing 100875China Key Laboratory of Particle Astrophysics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Beijing 100049China School of Physics and Optoelectronics Xiangtan UniversityXiangtan 411105China College of Physics Hebei Normal UniversityShijiazhuang 050024China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal UniversityGuiyang 550001China School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550001China Institute of Astrophysics Central China Normal UniversityWuhan 430079China College of physics Sciences Technology Hebei UniversityBaoding 071002China Physics and Space Science College China West Normal UniversityNanchong 637002China School of Computing and Artificial Intelligence Southwest Jiaotong UniversityChengdu 611756China College of Electronic and information Engineering Tongji UniversityShanghai 201804China Innovation Academy for Microsatellites of Chinese Academy of Sciences Shanghai 201304China National Space Science Center Chinese Academy of SciencesBeijing 100190China School of Earth and Space Sciences University of Science and Technology of ChinaHefei 230026China Electronic Information School Wuhan UniversityWuhan 430072China Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint Institute for Atmospheric SciencesNanjing 210000China Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China

Fast and reliable localization of high-energy transients is crucial for characterizing the burst properties and guiding the follow-up *** based on the relative counts of different detectors has been widely used for all-sky gamma-ray *** are two major methods for this count distribution localization:χ^(2)minimization method and the Bayesian *** we propose a modified Bayesian method that could take advantage of both the accuracy of the Bayesian method and the simplicity of the χ^(2)*** comprehensive simulations,we find that our Bayesian method with Poisson likelihood is generally more applicable for various bursts than the χ^(2)method,especially for weak *** further proposed a location-spectrum iteration approach based on the Bayesian inference,which could alleviate the problems caused by the spectral difference between the burst and location *** method is very suitable for scenarios with limited computation resources or timesensitive applications,such as in-flight localization software,and low-latency localization for rapidly follow-up observations.

关键词： methods:data analysis methods:analytical (stars:)gamma-ray burst:general

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Wavelets and DCT Watermarking for Medical Image

Wavelets and DCT Watermarking for Medical Image

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International Conference on Software Defined Systems (SDS)

作者： Rania Hamami Narima Zermi Larbi Boubchir Amine Khaldi Electronics Department Faculty of Engineering Sciences Laboratories of Automation and Signals of Annaba (LASA) Badji Mokhtar Annaba University Annaba Algeria LIASD research Lab. University of Paris 8 France Computer Science Department Faculty of Sciences and Technology Artificial Intelligence and Information Technology Laboratory (LINATI) University of Kasdi Merbah Ouargla Algeria

In recent years, the transition to the digital world offers users several conveniences, to use, process, store, and transmit their data. In addition, the rapid and continuous evolution of image acquisition systems has allowed a tremendous growth in the use of digital images. Indeed, digitization is a double-edged sword; it offers several benefits on the one hand and poses security problems on the other hand. For this reason, it is mandatory to design digital image protection systems against illegal manipulation. In recent decades, digital tattooing, a brilliant technique proposed to address several aspects of security. The principle of this technique is to implement a digital signal called watermark in another digital signal called host signal (text, image, audio, video…). In this context, our work aims to propose novel digital image watermarking approaches based on wavelets and DCT for the copyright protection of medical images. The proposed methods will allow optimizing the two contradictory demands of watermarking: imperceptibility and robustness.

关键词： Image coding Digital images Watermarking Frequency conversion Software Robustness Discrete wavelet transforms

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Referring Flexible Image Restoration

arXiv

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arXiv 2024年

作者： Guan, Runwei Hu, Rongsheng Zhou, Zhuhao Xue, Tianlang Man, Ka Lok Smith, Jeremy Lim, Eng Gee Ding, Weiping Yue, Yutao Institute of Deep Perception Technology JITRI Wuxi214000 China Faculty of Science and Engineering University of Liverpool LiverpoolL69 3BX United Kingdom XJTLU-JITRI Academy of Technology Xi’an Jiaotong-Liverpool University Suzhou215123 China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou215123 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom Guangzhou511400 China School of Information Science and Technology Nantong University Nantong226019 China

In reality, images often exhibit multiple degradations, such as rain and fog at night (triple degradations). However, in many cases, individuals may not want to remove all degradations, for instance, a blurry lens revealing a beautiful snowy landscape (double degradations). In such scenarios, people may only desire to deblur. These situations and requirements shed light on a new challenge in image restoration, where a model must perceive and remove specific degradation types specified by human commands in images with multiple degradations. We term this task Referring Flexible Image Restoration (RFIR). To address this, we first construct a large-scale synthetic dataset called RFIR, comprising 153,423 samples with the degraded image, text prompt for specific degradation removal and restored image. RFIR consists of five basic degradation types: blur, rain, haze, low light and snow while six main sub-categories are included for varying degrees of degradation removal. To tackle the challenge, we propose a novel transformer-based multi-task model named TransRFIR, which simultaneously perceives degradation types in the degraded image and removes specific degradation upon text prompt. TransRFIR is based on two devised attention modules, Multi-Head Agent Self-Attention (MHASA) and Multi-Head Agent Cross Attention (MHACA), where MHASA and MHACA introduce the agent token and reach the linear complexity, achieving lower computation cost than vanilla self-attention and cross-attention and obtaining competitive performances. Our TransRFIR achieves state-of-the-art performances compared with other counterparts and is proved as an effective architecture for image restoration. We release our project at https://***/GuanRunwei/FIR-CP. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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ML_Alzheimer: Alzheimer Disease Prediction Using Machine Learning

ML_Alzheimer: Alzheimer Disease Prediction Using Machine Lea...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Mohamed S Farag Ibrahim A Gomaa Magdy Abd-Elghany Zeid Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Alzheimer's disease is a fatal brain disorder that impacts predominantly the elderly. The early identification of Alzheimer's illness requires the use of efficient automated methods. For the categorization of Alzheimer's disease, researchers have proposed several distinct methods. To create more effective learning strategies, however, a deeper comprehension of Alzheimer's research is necessary. The condition is diagnosed but not until a later stage. As a consequence, the disease's progression or its symptoms' onset can be slowed if detected earlier. This article uses two datasets and machine learning techniques to predict Alzheimer's disease based on psychological characteristics such as age, gender, number of visits, and Mini-Mental State Examination (MMSE). We ultimately focused on the masculine gender and characteristics. The following results (accuracy) were discovered: SVM: 84%, KNN: 79%, and Random forest: 86%. Neural network: 86%; Naive Bayes: 83%.

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SKIN_ML: An Efficient Approach for Skin Cancer Detection Using Soft Computing Methods

SKIN_ML: An Efficient Approach for Skin Cancer Detection Usi...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

In today's environment, cancer is a fatal disease. Skin cancer has become a fairly common malignancy due to the spread of several forms of cancer. Skin cancer is divided into two types: melanoma and non-melanoma. Melanoma is one of the most fatal tumors on the planet, and it can spread to other parts of the body if not diagnosed early enough. Our proposed system uses Five alternative methods to predict a skin lesion's borders, texture, and color: a neural network and four standard machine learning classifiers. To enhance their performance, the outputs of these systems are merged using majority voting. Experiments have demonstrated that combining the five strategies yields the maximum level of accuracy. Pre-processing, Segmentation, Feature Extraction, and Classification are four critical phases in skin cancer identification. Skin lesion images were collected for this research from the International Skin Imaging Collaboration (ISIC), which contains over 3297 photos. The accuracy of the NN classifier is 91.9%, compared to 87.2% for the KNN classifier, 81.5% for the Naive Bayes classifier, 72.5% for the SVM classifier, and 68.3% for the DT classifier.

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Feature Rotation Invariance Learning for Point Cloud Analysis

Feature Rotation Invariance Learning for Point Cloud Analysi...

引用

Smart World Congress (SWC), IEEE

作者： Lu Shi Qi Cao Guoqing Zhang Jin Yi Yansen Huang Yigang Cen State Key Laboratory of Advanced Rail Autonomous Operation the School of Computer Science and Technology and Visual Intellgence +X International Cooperation Joint Laboratory of MOE Beijing Jiaotong University Beijing China the School of Computing Science University of Glasgow Singapore With the Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education the State Key Laboratory of Advanced Rail Autonomous Operation and the School of Computer and Information Technology Beijing Jiaotong University Beijing China the College of Civil Engineering Guizhou University Guizhou Lianjian Civil Engineering Quality Testing Monitoring Center Co. LTD Guizhou China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

While deep learning has significantly advanced point cloud analysis, extracting effective features from their disordered structure remains challenging. Existing approaches often rely on complex network architectures or manual feature engineering to achieve rotation invariance, which increases inference latency. This paper offers a novel perspective on this challenge, showing that rotation-sensitive models can achieve performance comparable to rotation-invariant models when trained with rotation-augmented data. Given the significant differences in the feature distributions of rotation-sensitive and rotation-invariant models, we propose Feature Rotation Invariance Learning (FRIL), a framework that leverages Distance Metric Learning and rotation-based constraints to help rotation-sensitive models extract rotation-invariant features. This approach eliminates the need for additional modules, enabling robust performance against rotations. Extensive experiments demonstrate that FRIL consistently outperforms existing rotation-invariant methods across various visual tasks.

关键词： Point cloud compression Representation learning Analytical models Solid modeling Visualization Three-dimensional displays Semantics Cloning Feature extraction data models

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Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle

arXiv

引用

arXiv 2022年

作者： Wu, Junde Fang, Huihui Xiong, Haoyi Duan, Lixin Tan, Mingkui Yang, Weihua Liu, Huiying Xu, Yanwu Baidu Inc. Beijing China School of Computer Science and Technology University of Electronic Science and Technology of China Sichuan Chengdu China School of Software Engineering South China University of Technology Guangdong Guangzhou China Big Data and Artificial Intelligence Institute Shenzhen Eye Hospital Jinan University Guangdong Shenzhen China Institute for Infocomm Research A*STAR Singapore Singapore

On the medical images, many of the tissues/lesions may be ambiguous. That is why the medical segmentation is typically annotated by a group of clinical experts to mitigate the personal bias. However, this clinical routine also brings new challenges to the application of machine learning algorithms. Without a definite ground-truth, it will be difficult to train and evaluate the deep learning models. When the annotations are collected from different graders, a common choice is majority vote, e.g., taking the average of multiple labels. However such a strategy ignores the difference between the grader expertness. In this paper, we consider the task of predicting the segmentation with the calibrated inter-observer uncertainty. We note that in clinical practice, the medical image segmentation is usually used to assist the disease diagnosis. Inspired by this observation, we propose diagnosis-first principle, which is to take disease diagnosis as the criterion to calibrate the inter-observer segmentation uncertainty. Following this idea, a framework named Diagnosis First segmentation Framework (DiFF) is proposed to estimate diagnosis-first segmentation from the raw images. Specifically, DiFF will first learn to fuse the multi-rater segmentation labels to a single ground-truth which could maximize the disease diagnosis performance. We dubbed the fused ground-truth as Diagnosis First Ground-truth (DF-GT). Then, we further propose Take and Give Model (T&G Model) to segment DF-GT from the raw image. In this way, DiFF can learn the segmentation with the calibrated uncertainty that facilitate the disease diagnosis. We verify the effectiveness of DiFF on three different medical segmentation tasks: optic-disc/optic-cup (OD/OC) segmentation on fundus images, thyroid nodule segmentation on ultrasound images, and skin lesion segmentation on dermoscopic images. Experimental results show that the proposed DiFF is able to significantly facilitate the corresponding disease diagnosis (gla

关键词： Diagnosis

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News Events Element Extraction of Chinese-Vietnamese Cross-language Using Reading Comprehension 20

News Events Element Extraction of Chinese-Vietnamese Cross-l...

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20th Chinese National Conference on Computational Linguistics, CCL 2021

作者： Zhu, Enchang Yu, Zhengtao Gao, Shengxiang Huang, Yuxin Guo, Junjun Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of Artificial Intelligence Kunmong University of Science and Technology Yunnan Kunming650500 China

Argument extraction of news text events, the task of extracting extract the event argument that describe the topic events in the news text, such as time, location, people, and *** event element extraction methods have poor performance on resource-scarce languages, and it is difficult to model the semantics of long *** this paper, we propose News Events Element extraction of Chinese-Vietnamese Cross-language Using Reading ***, we use the news long text key sentence retrieval module to filter noisy *** we use the cross-language reading comprehension model to transfer rich resource language knowledge to Vietnamese to improve the performance of Vietnamese news event element extraction. Experiments on the self-built Chinese-Vietnamese bilingual data set prove the effectiveness of this method. © 2021 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Semantics

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On the Stability and Generalization of Triplet Learning

arXiv

引用

arXiv 2023年

作者： Chen, Jun Chen, Hong Jiang, Xue Gu, Bin Li, Weifu Gong, Tieliang Zheng, Feng College of Informatics Huazhong Agricultural University Wuhan430070 China College of Science Huazhong Agricultural University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China Key Laboratory of Smart Farming for Agricultural Animals Wuhan430070 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates School of Computer Science and Technology Xi'an Jiaotong University Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Ministry of Education Xi’an710049 China

Triplet learning, i.e. learning from triplet data, has attracted much attention in computer vision tasks with an extremely large number of categories, e.g., face recognition and person re-identification. Albeit with rapid progress in designing and applying triplet learning algorithms, there is a lacking study on the theoretical understanding of their generalization performance. To fill this gap, this paper investigates the generalization guarantees of triplet learning by leveraging the stability analysis. Specifically, we establish the first general high-probability generalization bound for the triplet learning algorithm satisfying the uniform stability, and then 1 obtain the excess risk bounds of the order (Equation presented). for both stochastic gradient descent (SGD) and regularized risk minimization (RRM), where 2n is approximately equal to the number of training samples. Moreover, an optimistic generalization bound in expectation as fast as O(n−1) is derived for RRM in a low noise case via the on-average stability analysis. Finally, our results are applied to triplet metric learning to characterize its theoretical underpinning. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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Unitary Approximate Message Passing for Matrix Factorization

arXiv

引用

arXiv 2022年

作者： Yuan, Zhengdao Guo, Qinghua Eldar, Yonina C. Li, Yonghui The Artificial Intelligence Technology Engineering Research Center Open University of Henan Zhengzhou450002 China The School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia The Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia

We consider matrix factorization (MF) with certain constraints, which finds wide applications in various areas. Leveraging variational inference (VI) and unitary approximate message passing (UAMP), we develop a Bayesian approach to MF with an efficient message passing implementation, called UAMP-MF. With proper priors imposed on the factor matrices, UAMP-MF can be used to solve many problems that can be formulated as MF, such as non-negative matrix factorization, dictionary learning, compressive sensing with matrix uncertainty, robust principal component analysis, and sparse matrix factorization. Extensive numerical examples are provided to show that UAMP-MF significantly outperforms state-of-the-art algorithms in terms of recovery accuracy, robustness and computational complexity. © 2022, CC BY-NC-SA.

关键词： Message passing

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