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Articles by An Ran
Computer Science > Software Engineering
Log parsing is essential for system log analysis because it supports tasks such as debugging, monitoring, and anomaly detection by transforming unstructured log messages into structured log templates. However, real-world log datasets exhibit highly imbalanced, long-tailed distributions, where a small number of frequent templates dominate while many rare templates appear only a few times.
Computer Science > Software Engineering
Behavior-Driven Development (BDD) helps technical and non-technical stakeholders share a common understanding of software requirements through natural-language scenarios. Glue code makes these scenarios executable by mapping each step to the corresponding project code. However, developing and maintaining glue code requires knowledge of both the intended behavior and the underlying codebase, making it a labor-intensive part of BDD as requirements evolve.
Tea as the matchmaker: reconstruction of the former Russian concession in Hankow through semiotic landscape
The semiotic landscape serves as a matrix in which cultural codes and historical values are expressed through symbolic forms.
Leading Natural Health Expert in Germany Praises Shen Yun: ‘Beautiful and Entertaining’
FRANKFURT, Germany—On the evening of May 3, after attending Shen Yun’s performance at the Jahrhunderthalle Frankfurt, René Gräber, a well-known columnist in Germany’s natural health sector, expressed deep admiration for the artistry and dedication of the performers. “This performance was beautiful and entertaining,” Mr. Gräber said.
Chinese Audience Members Moved to Tears: ‘Only the Divine Could Accomplish’ Something Like Shen Yun
FRANKFURT, Germany—On the evening of May 2, Shen Yun Performing Arts presented its inaugural performance of the year in Frankfurt, Germany, at the Jahrhunderthalle (Centennial Hall). Among the audience were Simin, a Chinese student studying in Germany, and her mother. Simin, who requested to be identified only by her first name for safety reasons, and her mother were both deeply moved by what they saw, with the mother at times overcome with emotion.
Czech Cultural Heritage Leader: Shen Yun Showcases Ancient Culture, Brings Inner Calm
PRAGUE, Czech Republic—On the afternoon of April 18, Shen Yun Global Arts Company presented its second local performance of the year in the Czech Republic at the Prague Congress Center, which was once again completely sold out. Audience members from across the Czech Republic and neighboring countries—including Poland and Germany—attended the event, with many prominent figures among the guests. Some spectators also traveled from Denmark to attend the performance. After the show, Martin J.
‘An Extraordinary Evening’: Austrian Official Hopes to See Shen Yun in His Hometown
Among them was Herbert Johler, a senior employee at Austria’s Public Employment Service in the state of Vorarlberg, who attended the performance with Karin Grabher, a human resources professional in the glass industry. “The show is extraordinary—so colorful, so diverse,” Mr. Johler said. “The acrobatics and the movements of the male and female dancers are sensational.” Ms. Grabher also praised the performance’s artistic presentation. “It is very, very beautiful and very varied.
German Artist Moved to Tears at Shen Yun, Describes Sense of Cosmic Unity
FÜSSEN, Germany—Margit Kobler, an artist specializing in sacred Tibetan Buddhist art, attended Shen Yun Performing Arts at the Festspielhaus Neuschwanstein in Füssen, Germany, on April 12. She has long worked with Tibetan sculptural traditions and Buddhist sacred art, including sculpture and restoration. She said attending Shen Yun had been a long-held wish and described the experience as deeply moving. “I cannot really express myself the way I would like to, but it was wonderful.
Professional Dancer Calls Shen Yun ‘A Truly Unique and Inspiring Experience’
FÜSSEN, Germany—Bathed in spring sunshine after days of cold and overcast weather, Füssen set a picturesque scene on April 8, as audiences gathered at the Festspielhaus Neuschwanstein for Shen Yun Performing Arts’ second performance in the city. Inside, the sold-out theater resonated with warm applause throughout the performance. The evening concluded with a prolonged standing ovation, prompting three curtain calls from the artists.
Former Dancer Moved to Tears by Shen Yun, Expresses Deep Respect
BREMEN, Germany—“After a 12-year wait, my dream finally came true. It was incredibly moving, and I felt immense joy,” said former dancer Eva Zmijan after attending Shen Yun. “Their dances were so graceful, like fairies and angels. No words were needed—it was all there.” As celestial scenes unfolded on stage, she found herself completely captivated.
Time and person sensitive foundation model for disease prediction and risk stratification
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Abstract Foundation models (FMs) enable generalizable medical AI, but existing retinal FMs perform best on cross-sectional classification and detection and are less effective for predicting disease incidence and progression. We present RETFound Plus, a CFP-based FM trained with temporal modeling on 1,304,292 fundus photographs from 304,345 participants across multiple visits to learn progression-aware representations.
By Zheyuan Wang, Yukun Zhou, Yilan Wu, Ke Zou, Zhouyu Guan, Yibing Chen, Gabriel Dawei Yang, Ping Zhang, Changchang Yin, An Ran, Danqi Fang, Benjamin Sommer Thinggaard, Jakob Grauslund, Haoxuan Li, Jia Shu, Tingyao Li, Nan Jiang, Huating Li, Xiangning Wang, Qiang Wu, Charumathi Sabanayagam, Siegfried Wagner, Carol Cheung, Ching-Yu Cheng, Pearse A. Keane, Yih-Chung Tham, Samantha Yew, Xujia Liu, Yixiao Jin, Tingli Chen
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Nature
Verified
A test-time clinically adaptive framework for detecting multiple fundus diseases harnessing ophthalmic foundation models - npj Digital Medicine
Abstract Fundus diseases are leading causes of global vision impairment, often presenting with complex comorbidities that challenge conventional artificial intelligence models. While ophthalmic foundation models (FMs) offer promising capabilities, their clinical translation for multi-disease detection remains limited by issues such as imbalanced data distribution, uncertainty in multi-label predictions, inter-disease confusion, and domain shifts.
[2511.18288] Can Large Language Models Solve Path Constraints in Symbolic Execution?
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
An eyecare foundation model for clinical assistance: a randomized controlled trial - Nature Medicine
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Abstract In the context of an increasing need for clinical assessments of foundation models, we developed EyeFM, a multimodal vision–language eyecare copilot, and conducted a multifaceted evaluation, including retrospective validations, multicountry efficacy validation as a clinical copilot and a double-masked randomized controlled trial (RCT). EyeFM was pretrained on 14.5 million ocular images from five imaging modalities paired with clinical texts from global, multiethnic datasets.
By Tingyao Li, Zhouyu Guan, Yali Jia, Ping Zhang, Dian Zeng, Benjamin Sommer Thinggaard, Yukun Zhou, Huating Li, An Ran, Ying Zheng, Dingqiao Wang, Hongwei Ji, Youxin Chen, Xiangning Wang, Hua-Tao Xie, Weizhi Ma, Huazhu Fu, Wenxiao Wang, Andrzej Grzybowski, You Wu, Lee-Ling Lim, Carol Cheung, Pearse A. Keane, Ya Xing Wang, Yih-Chung Tham, Ching-Yu Cheng
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Nature
Verified
The acceptance of ophthalmic artificial intelligence for eye diseases: a literature review and qualitative analysis - Eye
Abstract Thorough investigations of end-users’ awareness, acceptance, and concerns about ophthalmic artificial intelligence (AI) are essential to ensure its successful implementation. We conducted a literature review on the acceptance of ophthalmic AI to provide an overall insight and qualitatively analysed the quality of eligible studies using a psychological model.
A deep learning system for detecting silent brain infarction and predicting stroke risk - Nature Biomedical Engineering
Abstract Current brain imaging to detect silent brain infarctions (SBIs) is not feasible for the general population. Here, to overcome this challenge, we developed a retinal image-based deep learning system, DeepRETStroke, to detect SBI and refine stroke risk. We use 895,640 retinal photographs to pretrain the DeepRETStroke system, which encodes a domain-specific foundation model for representing eye–brain connections.
Fine-grained Testing for Autonomous Driving Software: a Study on Autoware with LLM-driven Unit Testing
arXiv:2501.09866 (cs) View PDF Subjects: Software Engineering (cs.SE) Cite as: arXiv:2501.09866 [cs.SE] (or arXiv:2501.09866v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2501.09866 Submission history From: Wenhan Wang [ view email] [v1] Thu, 16 Jan 2025 22:36:00 UTC (1,951 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle...
[2409.09271] Python Symbolic Execution with LLM-powered Code Generation
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Research on Quality Control Method of Surface Temperature Observations for Complex Physical Geography
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Deep learning-based image quality assessment for optical coherence tomography macular scans: a multicentre study
P-AH and CYC are joint senior authors. X @KikiTang13, @JasonYam7, @Yih Chung Tham ZT and XW contributed equally. Contributors ZT, ARR and DY supervised by CYC conceived and designed the study. JCY, XZ, SKHS, JC, CYKW, CKMC, TYW, C-YC, CS, YCT, GL, GA and RR provided data for model development, validation and testing. ZT, AL, AKYN, VLY and NK organised all the data. ZT, YLW, AKYN, VLY and NK contributed to the imaging labelling.
Performance of Artificial Intelligence in Detecting Diabetic Macular Edema From Fundus Photography and Optical Coherence Tomography Images: A Systematic Review and Meta-analysis
Diabetic macular edema (DME) is the leading cause of vision loss in people with diabetes. Application of artificial intelligence (AI) in interpreting fundus photography (FP) and optical coherence tomography (OCT) images allows prompt detection and intervention. To evaluate the performance of AI in detecting DME from FP or OCT images and identify potential factors affecting model performances. We searched seven electronic libraries up to 12 February 2023.
East and West Ask丨Megelbek Sermetaliyev: How can the media promote dialogue among civilizations?
Urumqi, Nov. 11 (Xinhua) -- How can the media promote dialogue among civilizations? - Interview with Mejerbek Sermetaliyev, Director of the Kabar State News Agency of Kyrgyzstan Author: Gou Jipeng, Chen Xin, An Ran This year marks the 10th anniversary of the Belt and Road Initiative. Over the past <> years, media exchanges between China and the Belt and Road countries have become increasingly close.
Developing a privacy-preserving deep learning model for glaucoma detection: a multicentre study with federated learning
Background Deep learning (DL) is promising to detect glaucoma. However, patients’ privacy and data security are major concerns when pooling all data for model development. We developed a privacy-preserving DL model using the federated learning (FL) paradigm to detect glaucoma from optical coherence tomography (OCT) images. Methods This is a multicentre study. The FL paradigm consisted of a ‘central server’ and seven eye centres in Hong Kong, the USA and Singapore.
What can GPT-4 do for Diagnosing Rare Eye Diseases? A Pilot Study
Ophthalmology and Therapy, Sep 2023 Generative pretrained transformer-4 (GPT-4) has gained widespread attention from society, and its potential has been extensively evaluated in many areas. However, investigation of GPT-4’s use in medicine, especially in the ophthalmology field, is still limited.
[2308.08836] Are They All Good? Studying Practitioners' Expectations on the Readability of Log Messages
Developers write logging statements to generate logs that provide run-time information for various tasks. The readability of log messages in the logging statements (i.e., the descriptive text) is rather crucial to the value of the generated logs. Immature log messages may slow down or even obstruct the process of log analysis.
Deep learning in optical coherence tomography: Where are the gaps?
Abstract Optical coherence tomography (OCT) is a non-invasive optical imaging modality, which provides rapid, high-resolution and cross-sectional morphology of macular area and optic nerve head for diagnosis and managing of different eye diseases. However, interpreting OCT images requires experts in both OCT images and eye diseases since many factors such as artefacts and concomitant diseases can affect the accuracy of quantitative measurements made by post-processing algorithms.
[2305.05835] Reference-based OCT Angiogram Super-resolution with Learnable Texture Generation
arXiv:2305.05835 (eess) Download a PDF of the paper titled Reference-based OCT Angiogram Super-resolution with Learnable Texture Generation, by Yuyan Ruan and 4 other authors Download PDF Submission history From: Yuyan Ruan Miss [ view email] [v1] Wed, 10 May 2023 01:48:01 UTC (41,171 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media...
A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study
Summary Background There is no simple model to screen for Alzheimer's disease, partly because the diagnosis of Alzheimer's disease itself is complex—typically involving expensive and sometimes invasive tests not commonly available outside highly specialised clinical settings. We aimed to develop a deep learning algorithm that could use retinal photographs alone, which is the most common method of non-invasive imaging the retina to detect Alzheimer's disease-dementia.
Correction: Deep learning in glaucoma with optical coherence tomography: a review
An Ran Ran1, Clement C. Tham ORCID: orcid.org/0000-0003-4407-69071,2, Poemen P. Chan1,2, Ching-Yu Cheng ORCID: orcid.org/0000-0003-0655-885X3,4,5, Yih-Chung Tham ORCID: orcid.org/0000-0002-6752-797X3,4, Tyler Hyungtaek Rim3,4 & Carol Y. Cheung1 Eye (2020)Cite this article Subjects The original article was published on 07 October 2020 Correction to: Eye In the original Article, Dr. Poemen P. Chan’s name was originally misstated as “Poemen C. Chan”.
[1910.06302] Detecting Glaucoma Using 3D Convolutional Neural Network of Raw SD-OCT Optic Nerve Scans
Authors:Erfan Noury, Suria S. Mannil, Robert T. Chang, An Ran Ran, Carol Y. Cheung, Suman S. Thapa, Harsha L. Rao, Srilakshmi Dasari, Mohammed Riyazuddin, Sriharsha Nagaraj, Reza Zadeh (Submitted on 14 Oct 2019) Abstract: We propose developing and validating a three-dimensional (3D) deep learning system using the entire unprocessed OCT optic nerve volumes to distinguish true glaucoma from normals in order to discover any additional imaging biomarkers within the cube through saliency mapping.
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