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Test smells arise from poor design practices and insufficient domain knowledge, which can lower the quality of test code and make it harder to maintain and update. Manually refactoring test smells is time-consuming and error-prone, highlighting the necessity for automated approaches. Current rule-based refactoring methods often struggle in scenarios not covered by predefined rules and lack the flexibility needed to handle diverse cases effectively. In this paper, we propose a novel approach called UTRefactor, a context-enhanced, LLM-based framework for automatic test refactoring in Java projects. UTRefactor extracts relevant context from test code and leverages an external knowledge base that includes test smell definitions, descriptions, and DSL-based refactoring rules. By simulating the manual refactoring process through a chain-of-thought approach, UTRefactor guides the LLM to eliminate test smells in a step-by-step process, ensuring both accuracy and consistency throughout the refactoring. Additionally, we implement a checkpoint mechanism to facilitate comprehensive refactoring, particularly when multiple smells are present. We evaluate UTRefactor on 879 tests from six open-source Java projects, reducing the number of test smells from 2,375 to 265, achieving an 89% reduction. UTRefactor outperforms direct LLM-based refactoring methods by 61.82% in smell elimination and significantly surpasses the performance of a rule-based test smell refactoring tool. Our results demonstrate the effectiveness of UTRefactor in enhancing test code quality while minimizing manual involvement.

Open-source third-party libraries are widely used in software development. These libraries offer substantial advantages in terms of time and resource savings. However, a significant concern arises due to the publicly disclosed vulnerabilities within these libraries. Existing automated vulnerability detection tools often suffer from false positives and fail to accurately assess the propagation of inputs capable of triggering vulnerabilities from client projects to vulnerable code in libraries. In this paper, we propose a novel approach called VULEUT (Vulnerability Exploit Unit Test Generation), which combines vulnerability exploitation reachability analysis and LLM-based unit test generation. VULEUT is designed to automatically verify the exploitability of vulnerabilities in third-party libraries commonly used in client software projects. VULEUT first analyzes the client projects to determine the reachability of vulnerability conditions. And then, it leverages the Large Language Model (LLM) to generate unit tests for vulnerability confirmation. To evaluate the effectiveness of VULEUT, we collect 32 vulnerabilities from various third-party libraries and conduct experiments on 70 real client projects. Besides, we also compare our approach with two representative tools, i.e., TRANSFER and VESTA. Our results demonstrate the effectiveness of VULEUT, with 229 out of 292 generated unit tests successfully confirming vulnerability exploitation across 70 client projects, which outperforms baselines by 24%.

In the mobile development process, creating the user interface (UI) is highly resource intensive. Consequently, numerous studies have focused on automating UI development, such as generating UI from screenshots or design specifications. However, they heavily rely on computer vision techniques for image recognition. Any recognition errors can cause invalid UI element generation, compromising the effectiveness of these automated approaches. Moreover, developing an app UI from scratch remains a time consuming and labor intensive task. To address this challenge, we propose a novel approach called GUIMIGRATOR, which enables the cross platform migration of existing Android app UIs to iOS, thereby automatically generating UI to facilitate the reuse of existing UI. This approach not only avoids errors from screenshot recognition but also reduces the cost of developing UIs from scratch. GUIMIGRATOR extracts and parses Android UI layouts, views, and resources to construct a UI skeleton tree. GUIMIGRATOR generates the final UI code files utilizing target code templates, which are then compiled and validated in the iOS development platform, i.e., Xcode. We evaluate the effectiveness of GUIMIGRATOR on 31 Android open source applications across ten domains. The results show that GUIMIGRATOR achieves a UI similarity score of 78 between migration screenshots, outperforming two popular existing LLMs substantially. Additionally, GUIMIGRATOR demonstrates high efficiency, taking only 7.6 seconds to migrate the datasets. These findings indicate that GUIMIGRATOR effectively facilitates the reuse of Android UI code on iOS, leveraging the strengths of both platforms UI frameworks and making new contributions to cross platform development.

Scientific research indicates that for every hour spent in direct patient care, physicians spend nearly two additional hours on administrative tasks, particularly on electronic health records (EHRs) and desk work. This excessive administrative burden not only reduces the time available for patient care but also contributes to physician burnout and inefficiencies in healthcare delivery. To address these challenges, this study introduces MediGen, a fine-tuned large language model (LLM) designed to automate the generation of medical reports from medical dialogues. By leveraging state-of-the-art methodologies for fine-tuning open-source pretrained models, including LLaMA3-8B, MediGen achieves high accuracy in transcribing and summarizing clinical interactions. The fine-tuned LLaMA3-8B model demonstrated promising results, achieving a ROUGE score of 58% and a BERTScore-F1 of 72%, indicating its effectiveness in generating accurate and clinically relevant medical reports. These findings suggest that MediGen has the potential to significantly reduce the administrative workload on physicians, improving both healthcare efficiency and physician well-being., Comment: 4 pages, 3 Figures, 3 Tables, This is a preprint version of the article. The final version will be published in the proceedings of the IEEE conference

This paper conducted a comprehensive study on the performance of a rotary compressor over a rotational speed range of 80Hz to 200Hz through experimental tests and mathematical modeling. A compressor performance test rig was designed to conduct the performance tests, with fast-response pressure sensors and displacement sensors capturing the P-V diagram and dynamic motion of the moving components. Results show that the compressor efficiency degrades at high speeds due to the dominant loss factors of leakage and discharge power loss. Supercharging effects become significant at speeds above 160Hz, and its net effects reduce the compressor efficiency, especially at high speeds. This study identifies and analyzes the loss factors on the mass flow rate and power consumption based on experimental data, and hypothesizes possible mechanisms for each loss factor, which can aid in the design of a high-speed rotary compressor with higher efficiency.

Structures of metal nanoparticles (NPs) significantly influence their catalytic reactivities. Recent in situ experimental observations of dramatic structural changes in NPs have underscored the need to establish a dynamic structure-property relationship that accounts for the reconstruction of NPs in reactive environments. Here, we present the MOSP, a free and open-source graphical user interface (GUI) package designed to simulate the structure and reactivity of metal NPs under operando conditions. MOSP integrates two models: the multiscale structure reconstruction (MSR) model predicting equilibrium metal NP structures under specific reaction conditions and the kinetic Monte Carlo (KMC) model simulating the reaction dynamics. This combination allows for the exploration of the dynamic structure-property relationships of NPs. MOSP enhances user accessibility through its intuitive GUI, facilitating easy input, post-processing, and visualization of simulation data. This article is the release note of MOSP, focusing on its implementation and functionality., Comment: 14 pages, 6 figures

Oxygen vacancies (VO's) are of paramount importance in influencing the properties and applications of ceria (CeO2). Yet, comprehending the distribution and nature of the VO's poses a significant challenge due to the vast number of electronic configurations and intricate many-body interactions among VO's and polarons (Ce3+'s). In this study, we employed a combination of LASSO regression in machine learning, in conjunction with a cluster expansion model and first-principles calculations to decouple the interactions among the Ce3+'s and VO's, thereby circumventing the limitations associated with sampling electronic configurations. By separating these interactions, we identified specific electronic configurations characterized by the most favorable VO-Ce3+ attractions and the least Ce3+-Ce3+/VO-VO repulsions, which are crucial in determining the stability of vacancy structures. Through more than 10^8 Metropolis Monte Carlo samplings of Vo's and Ce3+ in the near-surface of CeO2(111), we explored potential configurations within an 8x8 supercell. Our findings revealed that oxygen vacancies tend to aggregate and are most abundant in the third oxygen layer, primarily due to extensive geometric relaxation-an aspect previously overlooked. This behavior is notably dependent on the concentration of Vo. This work introduces a novel theoretical framework for unraveling the complex vacancy structures in metal oxides, with potential applications in redox and catalytic chemistry., Comment: 22 pages, 1 scheme, 5 figures

Recent first-principle calculations unveiled a distinctive dynamic behavior in water molecule rotation during the melting process of highly confined water, indicating a notable time-scale separation in diffusion. In this short paper, we conducted molecular dynamics (MD) simulations to explore the rotation dynamics during the mono-layer ice nucleation process to investigate the possible intermediate states characterized by the differences in rotation of water molecules. Our study reveals two types of ice clusters with similar ice geometric structure but possess distinctly different rotational behaviors. In terms of molecular rotation, one type cluster is ice like (ILC) and can be regarded as small ice nuclei while the other is supercooled liquid water like (SCC). We found distinct nucleation pathways, thermodynamic properties, and phase transition dynamics to associate with these intermediate clusters, which yielded an unexpectedly complex picture of mono-layer ice nucleation., Comment: 12 pages, 5 figures

Cavity optomechanical systems have enabled precision sensing of magnetic fields, by leveraging the optical resonance-enhanced readout and mechanical resonance-enhanced response. Previous studies have successfully achieved scalable and reproducible microcavity optomechanical magnetometry (MCOM) by incorporating Terfenol-D thin films into high-quality ($Q$) factor whispering gallery mode (WGM) microcavities. However, the sensitivity was limited to 585 pT/Hz$^{1/2}$, over 20 times inferior to those using Terfenol-D particles. In this work, we propose and demonstrate a high-sensitivity and scalable MCOM approach by sputtering a FeGaB thin film onto a high-$Q$ SiO$_2$ WGM microdisk. Theoretical studies are conducted to explore the magnetic actuation constant and noise-limited sensitivity by varying the parameters of the FeGaB film and SiO$_2$ microdisk. Multiple magnetometers with different radii are fabricated and characterized. By utilizing a microdisk with a radius of 355 $\mu$m and a thickness of 1 $\mu$m, along with a FeGaB film with a radius of 330 $\mu$m and a thickness of 1.3 $\mu$m, we have achieved a remarkable peak sensitivity of 1.68 pT/Hz$^{1/2}$ at 9.52 MHz. This represents a significant improvement of over two orders of magnitude compared with previous studies employing sputtered Terfenol-D film. Notably, the magnetometer operates without a bias magnetic field, thanks to the remarkable soft magnetic properties of the FeGaB film. Furthermore, as a proof-of-concept, we have demonstrated the real-time measurement of a pulsed magnetic field simulating the corona current in a high-voltage transmission line using our developed magnetometer. These high-sensitivity magnetometers hold great potential for various applications, such as magnetic induction tomography and corona current monitoring.

Theoretical studies on chemical reaction mechanisms have been crucial in organic chemistry. Traditionally, calculating the manually constructed molecular conformations of transition states for chemical reactions using quantum chemical calculations is the most commonly used method. However, this way is heavily dependent on individual experience and chemical intuition. In our previous study, we proposed a research paradigm that uses enhanced sampling in molecular dynamics simulations to study chemical reactions. This approach can directly simulate the entire process of a chemical reaction. However, the computational speed limits the use of high-precision potential energy functions for simulations. To address this issue, we present a scheme for training high-precision force fields for molecular modeling using a previously developed graph-neural-network-based molecular model, molecular configuration transformer. This potential energy function allows for highly accurate simulations at a low computational cost, leading to more precise calculations of the mechanism of chemical reactions. We applied this approach to study a Claisen rearrangement reaction and a Carbonyl insertion reaction catalyzed by Manganese.

Fractional magnetisation plateaus, in which the magnetisation is pinned at a fraction of its saturated value within a range of external magnetic field, are spectacular macroscopic manifestations of the collective quantum behaviours. One prominent example of the plateau phase is found in spin-1/2 triangular-lattice antiferromagnets featuring strong geometrical frustration, and is often interpreted as quantum-fluctuation-stabilised state in magnetic field via the "order-by-disorder" mechanism. Here, we observe an unprecedented 1/3 magnetisation plateau between 5.2 and 7.4 T at 2 K in a spin-1 antiferromagnet Na$_3$Ni$_2$BiO$_6$ with a honeycomb lattice, where conventionally no geometrical frustration is anticipated. By carrying out elastic neutron scattering measurements, we propose the spin structure of the plateau phase to be an unusual partial spin-flop ferrimagnetic order, transitioning from the zigzag antiferromagnetic order in zero field. Our theoretical calculations show that the plateau phase is stabilised by the bond-anisotropic Kitaev interaction. These results provide a new paradigm for the exploration of rich quantum phases in frustrated magnets and exotic Kitaev physics in high-spin systems., Comment: Submitted version, 10 pages, 5 figures. Final version has been published in Nature Physics

In software development, developers extensively utilize third-party libraries to avoid implementing existing functionalities. When a new third-party library vulnerability is disclosed, project maintainers need to determine whether their projects are affected by the vulnerability, which requires developers to invest substantial effort in assessment. However, existing tools face a series of issues: static analysis tools produce false alarms, dynamic analysis tools require existing tests and test generation tools have low success rates when facing complex vulnerabilities. Vulnerability exploits, as code snippets provided for reproducing vulnerabilities after disclosure, contain a wealth of vulnerability-related information. This study proposes a new method based on vulnerability exploits, called VESTA (Vulnerability Exploit-based Software Testing Auto-Generator), which provides vulnerability exploit tests as the basis for developers to decide whether to update dependencies. VESTA extends the search-based test generation methods by adding a migration step, ensuring the similarity between the generated test and the vulnerability exploit, which increases the likelihood of detecting potential library vulnerabilities in a project. We perform experiments on 30 vulnerabilities disclosed in the past five years, involving 60 vulnerability-project pairs, and compare the experimental results with the baseline method, TRANSFER. The success rate of VESTA is 71.7\% which is a 53.4\% improvement over TRANSFER in the effectiveness of verifying exploitable vulnerabilities.

The success of deep networks in medical image segmentation relies heavily on massive labeled training data. However, acquiring dense annotations is a time-consuming process. Weakly-supervised methods normally employ less expensive forms of supervision, among which scribbles started to gain popularity lately thanks to its flexibility. However, due to lack of shape and boundary information, it is extremely challenging to train a deep network on scribbles that generalizes on unlabeled pixels. In this paper, we present a straightforward yet effective scribble supervised learning framework. Inspired by recent advances of transformer based segmentation, we create a pluggable spatial self-attention module which could be attached on top of any internal feature layers of arbitrary fully convolutional network (FCN) backbone. The module infuses global interaction while keeping the efficiency of convolutions. Descended from this module, we construct a similarity metric based on normalized and symmetrized attention. This attentive similarity leads to a novel regularization loss that imposes consistency between segmentation prediction and visual affinity. This attentive similarity loss optimizes the alignment of FCN encoders, attention mapping and model prediction. Ultimately, the proposed FCN+Attention architecture can be trained end-to-end guided by a combination of three learning objectives: partial segmentation loss, a customized masked conditional random fields and the proposed attentive similarity loss. Extensive experiments on public datasets (ACDC and CHAOS) showed that our framework not just out-performs existing state-of-the-art, but also delivers close performance to fully-supervised benchmark. Code will be available upon publication., Comment: 11 pages, 3 figures, a modified version was submitted to Computerized Medical Imaging and Graphics and is under review

Many deep learning based automated medical image segmentation systems, in reality, face difficulties in deployment due to the cost of massive data annotation and high latency in model iteration. We propose a dynamic interactive learning framework that addresses these challenges by integrating interactive segmentation into end-to-end weak supervised learning with streaming tasks. We develop novel replay and label smoothing schemes that overcome catastrophic forgetting and improve online learning robustness. For each image, our multi-round interactive segmentation module simultaneously optimizes both front-end predictions and deep learning segmenter. In each round, a 3D "proxy mask" is propagated from sparse user inputs based on image registration, serving as weak supervision that enable knowledge distillation from the unknown ground truth. In return, the trained segmenter explicitly guides next step's user interventions according to a spatial residual map from consecutive front or back-end predictions. Evaluation on 3D segmentation tasks (NCI-ISBI2013 and BraTS2015) shows that our framework generates online learning performances that match offline training benchmark. In addition, with a 62% reduction in total annotation efforts, our framework produces competitive dice scores comparing to online and offline learning which equipped with full ground truth. Furthermore, such a framework, with its flexibility and responsiveness, could be deployed behind hospital firewall that guarantees data security and easy maintenance., Comment: 24 pages, 8 figures, under review

The advent of Industry 4.0 has precipitated the incorporation of Artificial Intelligence (AI) methods within industrial contexts, aiming to realize intelligent manufacturing, operation as well as maintenance, also known as industrial intelligence. However, intricate industrial milieus, particularly those relating to energy exploration and production, frequently encompass data characterized by long-tailed class distribution, sample imbalance, and domain shift. These attributes pose noteworthy challenges to data-centric Deep Learning (DL) techniques, crucial for the realization of industrial intelligence. The present study centers on the intricate and distinctive industrial scenarios of Nuclear Power Generation (NPG), meticulously scrutinizing the application of DL techniques under the constraints of finite data samples. Initially, the paper expounds on potential employment scenarios for AI across the full life-cycle of NPG. Subsequently, we delve into an evaluative exposition of DL's advancement, grounded in the finite sample perspective. This encompasses aspects such as small-sample learning, few-shot learning, zero-shot learning, and open-set recognition, also referring to the unique data characteristics of NPG. The paper then proceeds to present two specific case studies. The first revolves around the automatic recognition of zirconium alloy metallography, while the second pertains to open-set recognition for signal diagnosis of machinery sensors. These cases, spanning the entirety of NPG's life-cycle, are accompanied by constructive outcomes and insightful deliberations. By exploring and applying DL methodologies within the constraints of finite sample availability, this paper not only furnishes a robust technical foundation but also introduces a fresh perspective toward the secure and efficient advancement and exploitation of this advanced energy source.

Structural Health Monitoring (SHM) is crucial for the safety and maintenance of various infrastructures. Due to the large amount of data generated by numerous sensors and the high real-time requirements of many applications, SHM poses significant challenges. Although the cloud-centric stream computing paradigm opens new opportunities for real-time data processing, it consumes too much network bandwidth. In this paper, we propose ECStream, an Edge Cloud collaborative fine-grained stream operator scheduling framework for SHM. We collectively consider atomic and composite operators together with their iterative computability to model and formalize the problem of minimizing bandwidth usage and end-to-end operator processing latency. Preliminary evaluation results show that ECStream can effectively balance bandwidth usage and end-to-end operator computation latency, reducing bandwidth usage by 73.01% and latency by 34.08% on average compared to the cloud-centric approach.

In this paper, we present our solution to the New frontiers for Zero-shot Image Captioning Challenge. Different from the traditional image captioning datasets, this challenge includes a larger new variety of visual concepts from many domains (such as COVID-19) as well as various image types (photographs, illustrations, graphics). For the data level, we collect external training data from Laion-5B, a large-scale CLIP-filtered image-text dataset. For the model level, we use OFA, a large-scale visual-language pre-training model based on handcrafted templates, to perform the image captioning task. In addition, we introduce contrastive learning to align image-text pairs to learn new visual concepts in the pre-training stage. Then, we propose a similarity-bucket strategy and incorporate this strategy into the template to force the model to generate higher quality and more matching captions. Finally, by retrieval-augmented strategy, we construct a content-rich template, containing the most relevant top-k captions from other image-text pairs, to guide the model in generating semantic-rich captions. Our method ranks first on the leaderboard, achieving 105.17 and 325.72 Cider-Score in the validation and test phase, respectively.

In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks., Comment: Tech report, project page https://nice.lgresearch.ai/

Despite the simplicity of the water molecule, the kinetics of ice nucleation under natural conditions can be complex. We investigated spontaneously grown ice nuclei using all-atom molecular dynamics simulations and found significant differences between the kinetics of ice formation through spontaneously formed and ideal nuclei. Since classical nucleation theory can only provide a good description of ice nucleation in ideal conditions, we propose a generalized nucleation theory that can better characterize the kinetics of ice crystal nucleation in general conditions. This study provides an explanation on why previous experimental and computational studies have yielded widely varying critical nucleation sizes.

Multiscale molecular modeling is widely applied in scientific research of molecular properties over large time and length scales. Two specific challenges are commonly present in multiscale modeling, provided that information between the coarse and fine representations of molecules needs to be properly exchanged: One is to construct coarse grained models by passing information from the fine to coarse levels; the other is to restore finer molecular details given coarse grained configurations. Although these two problems are commonly addressed independently, in this work, we present a theory connecting them, and develop a methodology called Cycle Coarse Graining (CCG) to solve both problems in a unified manner. In CCG, reconstruction can be achieved via a tractable deep generative model, allowing retrieval of fine details from coarse-grained simulations. The reconstruction in turn delivers better coarse-grained models which are informed of the fine-grained physics, and enables calculation of the free energies in a rare-event-free manner. CCG thus provides a systematic way for multiscale molecular modeling, where the finer details of coarse-grained simulations can be efficiently retrieved, and the coarse-grained models can be improved consistently., Comment: 16 pages, 5 figures