arXiv subjects

Di Cui

Publications and source records attributed to Di Cui.

8 recordsLinked to original sources

arXiv2025

RCM-ACT: Imitation Learning with Dynamic RCM Calibration for Autonomous Intraocular Foreign Body Removal

Intraocular foreign body removal demands millimeter-level precision in confined intraocular spaces, yet existing robotic systems predominantly rely on manual teleoperation with steep learning curves. To address the challenges of autonomous manipulation, particularly kinematic uncertainties from variable motion scaling and Remote Center of Motion (RCM) point variation, we propose RCM-ACT, an imitation learning framework for autonomous intraocular foreign body ring manipulation. Our approach integrates RCM dynamic calibration to resolve coordinate system inconsistencies caused by intraocular instrument variation and introduces the RCM-ACT architecture, which combines action chunking transformers with episode-level kinematic realignment. Trained solely on stereo visual data and instrument kinematics from expert demonstrations in an artificial eye model, RCM-ACT successfully completes ring grasping and positioning tasks without explicit depth sensing. Experimental validation demonstrates the successful implementation of end-to-end autonomy under uncalibrated microscopy conditions, achieving a mean 3-D Euclidean grasp deviation of 0.686 mm and 11/20 full-task successes. The results provide a viable framework for developing intelligent eye surgical systems capable of complex intraocular procedures.

cs.RO↗

arXiv2024

UITrans: Seamless UI Translation from Android to HarmonyOS

Seamless user interface (i.e., UI) translation has emerged as a pivotal technique for modern mobile developers, addressing the challenge of developing separate UI applications for Android and HarmonyOS platforms due to fundamental differences in layout structures and development paradigms. In this paper, we present UITrans, the first automated UI translation tool designed for Android to HarmonyOS. UITrans leverages an LLM-driven multi-agent reflective collaboration framework to convert Android XML layouts into HarmonyOS ArkUI layouts. It not only maps component-level and page-level elements to ArkUI equivalents but also handles project-level challenges, including complex layouts and interaction logic. Our evaluation of six Android applications demonstrates that our UITrans achieves translation success rates of over 90.1%, 89.3%, and 89.2% at the component, page, and project levels, respectively. UITrans is available at https://github.com/OpenSELab/UITrans and the demo video can be viewed at https://www.youtube.com/watch?v=iqKOSmCnJG0.

cs.SE↗

arXiv2024

REACCEPT: Automated Co-evolution of Production and Test Code Based on Dynamic Validation and Large Language Models

Synchronizing production and test code, known as PT co-evolution, is critical for software quality in the software development lifecycle. Existing methods for automatic PT co-evolution either utilize predefined heuristic rules or rely on simple application of machine learning techniques. Due to the limitations of underlying techniques, existing methods either only partially automate PT co-evolution (e.g., only automate obsolete test code identification) or result in low accuracy. In this paper, we propose REACCEPT, a novel approach that leverages large language models and dynamic validation to fully automate PT co-evolution (i.e., capable of both identifying and updating obsolete test cases). REACCEPT relies on experience-based prompt template generation, dynamic validation, and retrieval-augmented generation techniques to accomplish automated PT co-evolution. To evaluate REACCEPT's effectiveness, we extensive experiments with a dataset of 537 Java projects and compared REACCEPT's performance with several state-of-the-art methods. Results show that REACCEPT achieved an update accuracy of 60.16% on correctly identified obsolete test code, surpassing the state-of-the-art technique CEPROT by 90%. This confirms that REACCEPT can effectively assist developers in maintaining test code, improving overall software quality and reducing maintenance effort.

cs.SE↗

arXiv2024

Initial Experience of Metabolic Imaging with Hyperpolarized [1-13C]pyruvate MRI in Kidney Transplant Patients

BACKGROUND: Kidney transplant is the treatment of choice for patients with end-stage renal disease. Early detection of allograft injury is important to delay or prevent irreversible damage. PURPOSE: To investigate the feasibility of hyperpolarized (HP) [1-13C]pyruvate MRI for assessing kidney allograft metabolism. SUBJECTS: 6 participants (mean age, 45.2 +- 12.4 years, 2 females) scheduled for kidney allograft biopsy and 5 patients (mean age, 59.6 +- 10.4 years, 2 females) with renal cell carcinoma (RCC). ASSESSMENT: Five of the six kidney allograft participants underwent biopsy after MRI. Estimated glomerular filtration rate (eGFR) and urine protein-to-creatine ratio (uPCR) were collected within 4 weeks of MRI. Kidney metabolism was quantified from HP [1-13C]pyruvate MRI using the lactate-to-pyruvate ratio in allograft kidneys and non-tumor bearing kidneys from RCC patients. RESULTS: Biopsy was performed a mean of 9 days (range 5-19 days) after HP [1-13C]pyruvate MRI. Three biopsies were normal, one showed low-grade fibrosis and one showed moderate microvascular inflammation. All had stable functioning allografts with eGFR > 60 mL/min/1.73 m2 and normal uPCR. One participant who did not undergo biopsy had reduced eGFR of 49 mL/min/1.73 m2 and elevated uPCR. The mean lactate-to-pyruvate ratio was 0.373 in participants with normal findings (n = 3) and 0.552 in participants with abnormal findings (n = 2). The lactate-to-pyruvate ratio was highest (0.847) in the participant with reduced eGFR and elevated uPRC. Native non-tumor bearing kidneys had a mean lactate-to-pyruvate ratio of 0.309. DATA CONCLUSION: Stable allografts with normal findings at biopsy showed lactate-to-pyruvate ratios similar to native non-tumor bearing kidneys, whereas allografts with abnormal findings showed higher lactate-to-pyruvate ratios.

physics.med-ph↗

arXiv2023

QFree: A Universal Value Function Factorization for Multi-Agent Reinforcement Learning

Centralized training is widely utilized in the field of multi-agent reinforcement learning (MARL) to assure the stability of training process. Once a joint policy is obtained, it is critical to design a value function factorization method to extract optimal decentralized policies for the agents, which needs to satisfy the individual-global-max (IGM) principle. While imposing additional limitations on the IGM function class can help to meet the requirement, it comes at the cost of restricting its application to more complex multi-agent environments. In this paper, we propose QFree, a universal value function factorization method for MARL. We start by developing mathematical equivalent conditions of the IGM principle based on the advantage function, which ensures that the principle holds without any compromise, removing the conservatism of conventional methods. We then establish a more expressive mixing network architecture that can fulfill the equivalent factorization. In particular, the novel loss function is developed by considering the equivalent conditions as regularization term during policy evaluation in the MARL algorithm. Finally, the effectiveness of the proposed method is verified in a nonmonotonic matrix game scenario. Moreover, we show that QFree achieves the state-of-the-art performance in a general-purpose complex MARL benchmark environment, Starcraft Multi-Agent Challenge (SMAC).

cs.AI↗

arXiv2022

RMove: Recommending Move Method Refactoring Opportunities using Structural and Semantic Representations of Code

Incorrect placement of methods within classes is a typical code smell called Feature Envy, which causes additional maintenance and cost during evolution. To remove this design flaw, several Move Method refactoring tools have been proposed. To the best of our knowledge, state-of-the-art related techniques can be broadly divided into two categories: the first line is non-machine-learning-based approaches built on software measurement, while the selection and thresholds of software metrics heavily rely on expert knowledge. The second line is machine learning-based approaches, which suggest Move Method refactoring by learning to extract features from code information. However, most approaches in this line treat different forms of code information identically, disregarding their significant variation on data analysis. In this paper, we propose an approach to recommend Move Method refactoring named RMove by automatically learning structural and semantic representation from code fragment respectively. We concatenate these representations together and further train the machine learning classifiers to guide the movement of method to suitable classes. We evaluate our approach on two publicly available datasets. The results show that our approach outperforms three state-of-the-art refactoring tools including PathMove, JDeodorant, and JMove in effectiveness and usefulness. The results also unveil useful findings and provide new insights that benefit other types of feature envy refactoring techniques.

cs.SE↗

arXiv2022

A class of \v{S}id\'ak-type tests based on maximal precedence and exceedance statistic

A class of nonparametric two-sample tests has been proposed in this article. As a generalization of the original \v{S}id\'aks' test, the proposed test statistic is developed as the sum of the maximal precedence and maximal exceedance statistics. Unlike the \v{S}id\'ak-type precedence-exceedance test and the maximal precedence test, the proposed test is suitable for a two-sided alternative while being free from any parametric assumption. Exact distribution of the test statistic is obtained under the null as well as under the Lehmann alternative. Power value comparison has been carried out that shows the competency of the proposed test as a useful alternative to a number of existing tests based on precedence-exceedance statistics. Real-life example is provided to illustrate the application of the proposed test.

stat.ME↗

arXiv2021

From Innovations to Prospects: What Is Hidden Behind Cryptocurrencies?

The great influence of Bitcoin has promoted the rapid development of blockchain-based digital currencies, especially the altcoins, since 2013. However, most altcoins share similar source codes, resulting in concerns about code innovations. In this paper, an empirical study on existing altcoins is carried out to offer a thorough understanding of various aspects associated with altcoin innovations. Firstly, we construct the dataset of altcoins, including source code repositories, GitHub fork relations, and market capitalizations (cap). Then, we analyze the altcoin innovations from the perspective of source code similarities. The results demonstrate that more than 85% of altcoin repositories present high code similarities. Next, a temporal clustering algorithm is proposed to mine the inheritance relationship among various altcoins. The family pedigrees of altcoin are constructed, in which the altcoin presents similar evolution features as biology, such as power-law in family size, variety in family evolution, etc. Finally, we investigate the correlation between code innovations and market capitalization. Although we fail to predict the price of altcoins based on their code similarities, the results show that altcoins with higher innovations reflect better market prospects.

cs.SE↗