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arXiv 2024年

作者： Chen, Long-Fei Tsai, Chao-Wei Li, Jian-Yang Yang, Bin Li, Di Duan, Yan Hsia, Chih-Hao Pan, Zhichen Qian, Lei Quan, Donghui Jiang, Xue-Jian Li, Xiaohu Zhao, Ruining Zuo, Pei School of Physics and Electronic Science Guizhou Normal University Guiyang550025 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guiyang550025 China Research Center for Astronomical Computing Zhejiang Laboratory Hangzhou311100 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Key Laboratory of Radio Astronomy and Technology Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China School of Atmospheric Sciences Sun Yat-sen University Guangdong Zhuhai China Núcleo de Astronomía Facultad de Ingenieríay Ciencias Universidad Diego Portales Chile Department of Astronomy College of Physics and Electronic Engineering Qilu Normal University 2 Wenbo Road Zhangqiu District Jinan250200 China Department of Electronic and Optical Engineering Space Engineering University Beijing China Laboratory for Space Research Faculty of Science The University of Hong Kong Hong Kong Xinjiang Astronomical Observatory Chinese Academy of Sciences No. 150 Science 1-Street Urumqi830011 China CAS Key Laboratory of Optical Astronomy National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Astronomy and Space Sciences University of Chinese Academy of Sciences Beijing100049 China

We used the Five-hundred-meter Aperture Spherical radio Telescope (FAST) to search for the molecular emissions in the L-band between 1.0 and 1.5 GHz toward four comets, C/2020 F3 (NEOWISE), C/2020 R4 (ATLAS), C/2021 A1 (Leonard), and 67P/Churyumov-Gerasimenko during or after their perihelion passages. Thousands of molecular transition lines fall in this low-frequency range, many attributed to complex organic or prebiotic molecules. We conducted a blind search for the possible molecular lines in this frequency range in those comets and could not identify clear signals of molecular emissions in the data. Although several molecules have been detected at high frequencies of great than 100 GHz in comets, our results confirm that it is challenging to detect molecular transitions in the L-band frequency ranges. The non-detection of L-band molecular lines in the cometary environment could rule out the possibility of unusually strong lines, which could be caused by the masers or non-LTE effects. Although the line strengths are predicted to be weak, for FAST, using the ultra-wide bandwidth receiver and improving the radio frequency interference environments would enhance the detectability of those molecular transitions at low frequencies in the future. Copyright © 2024, The Authors. All rights reserved.

关键词： Molecules

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Large language models surpass human experts in predicting neuroscience results

arXiv

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arXiv 2024年

作者： Luo, Xiaoliang Rechardt, Akilles Sun, Guangzhi Nejad, Kevin K. Yáñez, Felipe Yilmaz, Bati Lee, Kangjoo Cohen, Alexandra O. Borghesani, Valentina Pashkov, Anton Marinazzo, Daniele Nicholas, Jonathan Salatiello, Alessandro Sucholutsky, Ilia Minervini, Pasquale Razavi, Sepehr Rocca, Roberta Yusifov, Elkhan Okalova, Tereza Gu, Nianlong Ferianc, Martin Khona, Mikail Patil, Kaustubh R. Lee, Pui-Shee Mata, Rui Myers, Nicholas E. Bizley, Jennifer K. Musslick, Sebastian Bilgin, Isil Poyraz Niso, Guiomar Ales, Justin M. Gaebler, Michael Murty, N. Apurva Ratan Loued-Khenissi, Leyla Behler, Anna Hall, Chloe M. Dafflon, Jessica Bao, Sherry Dongqi Love, Bradley C. Department of Experimental Psychology University College London London United Kingdom Department of Engineering University of Cambridge Cambridge United Kingdom Department of Physiology Anatomy & Genetics University of Oxford Oxford United Kingdom Max Planck Institute for Neurobiology of Behavior – caesar Bonn Germany Bilkent University Ankara Turkey Department of Computer Science University of Bristol Bristol United Kingdom The Alan Turing Institute London United Kingdom Department of Psychiatry Yale University New Haven United States Psychology Emory University Atlanta United States Faculty of Psychology and Educational Sciences Université de Genève Genève Switzerland Neurosurgery Novosibirsk State Medical University Novosibirsk Russia Federal Center of Neurosurgery FSBI Novosibirsk Russia Department of Data Collection and Processing Systems Novosibirsk State Technical University Novosibirsk Russia Department of Data Analysis Ghent University Ghent Belgium Psychology New York University New York United States Department of Cognitive Neurology University of Tübingen Tübingen Germany Computer Science Princeton University Princeton United States ILCC University of Edinburgh Edinburgh United Kingdom Philosophy Psychology and Language Sciences The University of Edinburgh Edinburgh United Kingdom Department of Culture Cognition and Computation Aarhus University Aarhus Denmark Department of Molecular Life Sciences University of Zurich Zurich Switzerland Bioengineering University of Pennsylvania Philadelphia United States Linguistic Research Infrastructure University of Zurich Zurich Switzerland Electronic and Electrical Engineering University College London London United Kingdom Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA United States Institute of Neuroscience and Medicine INM-7: Brain and Behaviour Research Centre Jülich Jülich Germany Medical Faculty Institute of Systems Neuroscience Heinrich Hein

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. To evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs were confident in their predictions, they were more likely to be correct, which presages a future where humans and LLMs team together to make discoveries. Our approach is not neuroscience-specific and is transferable to other knowledge-intensive endeavors. © 2024, CC BY.

关键词： Prediction models

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Summary of Scene Text Detection and Recognition

Summary of Scene Text Detection and Recognition

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yao Qin Zhi Zhang Collage of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In recent years, scene text recognition has received much attention, and has a wealth of application scenarios, such as: photo translation, image retrieval, scene understanding and so on. However, the text in the scene is also faced with many problems, such as: light changes, deformation text, text string recognition under background noise interference, text skew and degree of curvature, and a large number of artistic fonts. Solving the above problems will always be a challenging thing. This paper reviews some recent work on text detection, text recognition and end-to-end text detection and recognition, and finally looks forward to the future development direction.

关键词： Industrial electronics Image recognition Text recognition Interference Market research Noise measurement Strain

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Cross calibration of gamma-ray detectors (GRD) of GECAM-C

arXiv

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arXiv 2023年

作者： Zhang, Yan-Qiu Xiong, Shao-Lin Qiao, Rui Guo, Dong-Ya Peng, Wen-Xi Li, Xin-Qiao Xue, Wang-Chen Zheng, Chao Liu, Jia-Cong Tan, Wen-Jun Wang, Chen-Wei Zhang, Peng Wang, Ping Cai, Ce Xiao, Shuo Huang, Yue Feng, Pei-Yi Li, Xiao-Bo Song, Li-Ming Yi, Qi-Bin Zhao, Yi Guo, Zhi-Wei He, Jian-Jian Li, Chao-Yang Liu, Ya-Qing Gong, Ke Du, Yan-Qi Liu, Xiao-Jing Xie, Sheng-Lun Zhao, Guo-Ying Zhao, Xiao-Yun Zhang, Xiao-Lu Zhang, Zhen Zheng, Shi-Jie Wang, Jin Wen, Xiang-Yang An, Zheng-Hua Zhang, Da-Li Gao, Min Sun, Xi-Lei Liang, Xiao-Hua Yang, Sheng Wang, Jin-Zhou Chen, Gang Zhang, Fan Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China Institute of Astrophysics Central China Normal University Wuhan430079 China School of Physics and Optoelectronics Xiangtan University Hunan Xiangtan411105 China College of physics Sciences Technology Hebei University No. 180 Wusi Dong Road Lian Chi District Hebei Province Baoding City071002 China Information Science and Technology Southwest Jiaotong University Chengdu610031 China Qufu Normal University Qufu273165 China Department of Astronomy Beijing Normal University Beijing100875 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China College of Electronic and Information Engineering Tongji University Shanghai201804 China

The gamma-ray detectors (GRDs) of GECAM-C onborad SATech-01 satellite is designed to monitor gamma-ray transients all over the sky from 6 keV to 6 MeV. The energy response matrix is the key to do spectral measurements of bursts, which is usually generated from GEANT4 simulation and partially verified by the ground calibration. In this work, energy response matrix of GECAM-C GRD is cross-calibrated with Fermi/GBM and Swift/BAT using a sample of Gamma-Ray Bursts (GRBs) and Soft Gamma-Ray Repeaters (SGRs). The calibration results show there is a good agreement between GECAM-C and other reasonably well calibrated instrument (i.e. Fermi/GBM and Swift/BAT). We also find that different GRD detectors of GECAM-C also show consistency with each other. All these results indicate that GECAM-C GRD can provide reliable spectral measurements. Copyright © 2023, The Authors. All rights reserved.

关键词： Gamma rays

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Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution Enhancement

arXiv

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arXiv 2021年

作者： Luo, Hongming Zhou, Fei Liao, Guangsen Qiu, Guoping College of Electronics and Information Engineering Shenzhen University China Peng Cheng Laboratory Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen Key Laboratory of Digital Creative Technology China Shenzhen Institute for Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Guangdong Shenzhen China

In real-world applications, such as sharing photos on social media platforms, images are always not only sub-sampled but also heavily compressed thus often containing various artefacts. Simple methods for enhancing the resolution of such images will exacerbate the artefacts, rendering them visually objectionable. In spite of its high practical values, super-resolving compressed images is not well studied in the literature. In this paper, we propose a novel compressed image super resolution (CISR) framework based on parallel and series integration of artefacts removal and resolution enhancement. Based on a mathematical inference model for estimating a clean low-resolution (LR) image and a clean high-resolution (HR) image from a down-sampled and compressed observation, we have designed a CISR architecture consisting of two deep neural network modules: the artefacts removal module (ARM) and the resolution enhancement module (REM). The ARM and the REM work in parallel with both taking the compressed LR image as their inputs, at the same time they also work in series with the REM taking the output of the ARM as one of its inputs and the ARM taking the output of the REM as its other input. A technique called unfolding is introduced to recursively suppress the compression artefacts and restore the image resolution. A unique feature of our CISR system is that it exploits the parallel and series connections between the ARM and the REM, and recursive optimization to reduce the model's dependency on specific types of degradation thus making it possible to train a single model to super-resolve images compressed by different methods to different qualities. Experiments are conducted on a mixture of JPEG and WebP compressed images without assuming apriori compression type and compression quality factor. To demonstrate our technique's real-world application value, we have also applied the trained models directly to restore social media images which have undergone scaling and compre

关键词： Deep neural networks

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Waterline mapping of inland great lake with subpixel accuracy from GF-3 SAR images 6

Waterline mapping of inland great lake with subpixel accurac...

引用

6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Li, Ning Niu, Shilin Wang, Robert Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China Department of Space Microwave Remote Sensing System Institute of Electronics Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781728129129

High-accuracy waterline mapping with Synthetic Aperture Radar (SAR) images is a challenging task because of the inhomogeneities of SAR imagery caused by the speckle noise and complex terrain. This paper presents a novel method for waterline mapping with subpixel accuracy. The proposed method mainly consists of three steps. Firstly, an improved Non-Local filter is adapted to suppress the speckle noise. Secondly, a Fuzzy C-Means (FCM) clustering algorithm is used to extract the pixel-level waterline. Thirdly, along the pixel-level waterline, a novel subpixel-scale waterline extraction method based on bicubic convolution and Geometric Active Contour (GAC) model is presented to further improve the accuracy of the waterline. Without loss of generality, the Danjiangkou reservoir (DJKR), which is the largest artificial freshwater lake in Asia, is selected as a study case. SAR images from Chinese GaoFen-3 (GF-3) satellite working on different modes are processed to demonstrate the effectiveness of the proposed method. © 2019 IEEE.

关键词： Synthetic aperture radar

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Exploring the Pan-Relationship Between Disease and Symptom Related to Coronary Heart Disease from Chinese Electronic Medical Records 15th

Exploring the Pan-Relationship Between Disease and Symptom R...

引用

15th International Conference on Intelligent Computing, ICIC 2019

作者： Yang, Ronggen Wang, Yichen Wang, Bo Gong, Lejun Faculty of Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China Jiangsu Key Lab of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210046 China School of Geography and Bioinformatics Nanjing University of Posts and Telecommunication Nanjing China

ISBN: (纸本)9783030267650

Electronic Medical Records (EMRs) arises from the clinical treatment process, which reflects a patient’s physical condition, the disease, the treatment process, etc. It has an important significance for the recovery of the patient. As the amount of electronic medical records continues to increase, people are actively looking for ways to get the valid data contained in electronic medical records. More effective methods are urgently needed for processing EMRs. In this paper, 108 real EMRs of coronary heart disease were analyzed, through named entity recognition and entity relation extraction. Pan-relationship between entities was defined in the process. Experimental results showed the analysis of pan-relation between coronary heart disease and symptom is helpful for treatment of coronary heart disease. © 2019, Springer Nature Switzerland AG.

关键词： Diseases

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Correction to: An empirical study on the joint impact of feature selection and data resampling on imbalance classification

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Applied Intelligence 2022年第7期53卷 8506-8506页

作者： Zhang, Chongsheng Soda, Paolo Bi, Jingjun Fan, Gaojuan Almpanidis, George García, Salvador Ding, Weiping Henan Key Lab of Big Data Analysis and Processing Henan University Henan China Department of Engineering University Campus Bio-Medico of Rome Rome Italy Department of Radiation Sciences Radiation Physics Biomedical Engineering Umea University Umea Sweden DaSCI Andalusian Research Institute University of Granada Granada Spain School of Information Science and Technology Nantong University Nantong China

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An Improved Algorithm for Recruitment Text Categorization 3rd

An Improved Algorithm for Recruitment Text Categorization

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3rd International Conference on Cyberspace data and Intelligence, Cyber DI 2019, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019

作者： Zhao, Hui Liu, Xin Guo, Wenjie Gai, Keke Wang, Ying Educational Information Technology Laboratory Henan University KaifengHenan475004 China School of Software Henan University KaifengHenan475004 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Henan Intelligent Data Processing Engineering Research Center KaifengHenan475004 China

ISBN: (纸本)9789811519215

With the rapid development of the Internet, online recruitment has gradually become a mainstream. In the process of obtaining the text of recruitment information, a large volume of texts are not part of recruitment information. Currently, common text categorization algorithms include k-Nearest Neighbor, Support Vector Machine (SVM) and Naive Bayes. In addition, there are numerous related technical terms in the recruitment information, which affects the accuracy of the ordinary Bayesian text categorization algorithm. However, there is not uniform format for the text information of recruitment. This paper improves the original Naive Bayes algorithm and proposes a Reinforcement Naive Bayes (R-NB) algorithm to enhance the accuracy of recruitment information categorization. Experiments have demonstrated that the improved algorithm has a higher categorization accuracy and practicability than the original algorithm. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Nearest neighbor search

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Forgery Detection and Tampering Localization of Double JPEG Compression Based on First Digit Features of DCT Coefficients and KNR 2

Forgery Detection and Tampering Localization of Double JPEG ...

引用

2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

作者： Liu, Hongli Liu, Benyong College of Big Data and Information Engineering Guizhou University Guiyang China Institute of Intelligent Information Processing Guizhou University Guiyang China

ISBN: (纸本)9781538618035

A337 improved forgery detection and tampering localization method is proposed for double JPEG compression images based on first digit feature of discrete cosine transform (DCT) coefficients and kernel-based nonlinear representor (KNR). First, a to-be-checked JPEG image is divided into overlapping blocks of size 64 × 64, and the first digit (1∼9) features of alternating current (AC) DCT coefficients at the first nine positions of every 8 × 8 blocks are obtained in each image block, followed by principal component analysis (PCA) transform to form compact features. And then the KNR classifier is used to judge whether the corresponding image block has been re-compressed. Finally, the test results of double compression are used to locate the tampered area of JPEG image. Experimental results show that in comparison with representative algorithms, the improved algorithm achieves better results, and is robust to operations such as rotation, resizing and feathering. Moreover, KNR classifier outperforms classical SVM classifier in recognition effect and efficiency. © 2018 IEEE.

关键词： Principal component analysis

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