Ensuring the reliability of the Rust compiler is of paramount importance, given increasing adoption of Rust for critical systems development, due to its emphasis on memory and thread safety. However, generating valid test programs for the Rust compiler poses significant challenges, given Rust's complex syntax and strict requirements. With the growing popularity of large language models (LLMs), much research in software testing has explored using LLMs to generate test cases. Still, directly using LLMs to generate Rust programs often results in a large number of invalid test cases. Existing studies have indicated that test cases triggering historical compiler bugs can assist in software testing. Our investigation into Rust compiler bug issues supports this observation. Inspired by existing work and our empirical research, we introduce a bracket-based masking and filling strategy called clozeMask. The clozeMask strategy involves extracting test code from historical issue reports, identifying and masking code snippets with specific structures, and using an LLM to fill in the masked portions for synthesizing new test programs. This approach harnesses the generative capabilities of LLMs while retaining the ability to trigger Rust compiler bugs. It enables comprehensive testing of the compiler's behavior, particularly exploring edge cases. We implemented our approach as a prototype ClozeMaster. ClozeMaster has identified 27 confirmed bugs for rustc and mrustc, of which 10 have been fixed by developers. Furthermore, our experimental results indicate that ClozeMaster outperforms existing fuzzers in terms of code coverage and effectiveness.

Metabolism refers to a series of orderly chemical reactions used to maintain life activities in organisms. In healthy individuals, metabolism remains within a normal range. However, specific diseases can lead to abnormalities in the levels of certain metabolites, causing them to either increase or decrease. Detecting these deviations in metabolite levels can aid in diagnosing a disease. Traditional biological experiments often rely on a lot of manpower to do repeated experiments, which is time consuming and labor intensive. To address this issue, we develop a deep learning model based on the auto-encoder and non-negative matrix factorization named as MDA-AENMF to predict the potential associations between metabolites and diseases. We integrate a variety of similarity networks and then acquire the characteristics of both metabolites and diseases through three specific modules. First, we get the disease characteristics from the five-layer auto-encoder module. Later, in the non-negative matrix factorization module, we extract both the metabolite and disease characteristics. Furthermore, the graph attention auto-encoder module helps us obtain metabolite characteristics. After obtaining the features from three modules, these characteristics are merged into a single, comprehensive feature vector for each metabolite-disease pair. Finally, we send the corresponding feature vector and label to the multi-layer perceptron for training. The experiment demonstrates our area under the receiver operating characteristic curve of 0.975 and area under the precision-recall curve of 0.973 in 5-fold cross-validation, which are superior to those of existing state-of-the-art predictive methods. Through case studies, most of the new associations obtained by MDA-AENMF have been verified, further highlighting the reliability of MDA-AENMF in predicting the potential relationships between metabolites and diseases.

Recent advances in sequencing technology have considerably promoted genomics research by providing high-throughput sequencing economically. This great advancement has resulted in a huge amount of sequencing data. Clustering analysis is powerful to study and probe the large-scale sequence data. A number of available clustering methods have been developed in the last decade. Despite numerous comparison studies being published, we noticed that they have two main limitations: only traditional alignment-based clustering methods are compared and the evaluation metrics heavily rely on labeled sequence data. In this study, we present a comprehensive benchmark study for sequence clustering methods. Specifically, i) alignment-based clustering algorithms including classical (e.g., CD-HIT, UCLUST, VSEARCH) and recently proposed methods (e.g., MMseq2, Linclust, edClust) are assessed; ii) two alignment-free methods (e.g., LZW-Kernel and Mash) are included to compare with alignment-based methods; and iii) different evaluation measures based on the true labels (supervised metrics) and the input data itself (unsupervised metrics) are applied to quantify their clustering results. The aims of this study are to help biological analyzers in choosing one reasonable clustering algorithm for processing their collected sequences, and furthermore, motivate algorithm designers to develop more efficient sequence clustering approaches.

Percutaneous coronary intervention (PCI) involves the delivery of a flexible submillimeter guidewire and existing x- ray based approaches impose significant ironing radiation. The use of magnetic resonance imaging (MRI) for intraoperative guidance has the advantages of not only being safe but also having high positioning accuracy and excellent tissue contrast. This paper develops a pneumatically driven MR-conditional delivery mechanism for the ease of manipulation of the guidewire in vivo. It incorporates newly developed rotary pneumatic step motors and a pneumatic slip ring for actuation and decoupling of translational and rotational motions. An effective clamping mechanism for the locking and releasing of the guidewire is also incorporated. The proposed pneumatic slip ring mechanism decouples six gas lines, where four are used to supply a pneumatic step motor for translational motion and two for the clamping mechanism. High friction sil sleeve is used to hold the guidewire firmly. The rotary pneumatic motor has excellent sealing and stability, providing an output torque of 15.75 Nm/MPa. Experiments show that the average error of translational motion is 0.37 mm. Real-time MRI-guided endovascular intervention is performed in a vascular phantom with pulsatile flows to validate its potential clinical use. The imaging artifact test under MRI shows no noticeable distortion and the loss of Signal-to-Noise Ratio (SNR) is less than 2%.

This paper presents the symbol presentation method for Universal Design (UD). Current symbol presentations for UD focus on how to design a simple and perceptible symbol for as many users as possible. As a result, the same symbol is presented to every user and therefore some users with specific attributes (e.g., color-blindness, cultural or physical conditions) can not perceive the symbol appropriately. Thus, we propose the user-adaptive symbol presentation in which optimal components (color, shape and background) for a symbol is automatically selected using Analytic Hierarchy Process (AHP). We also present how compatibility among those components should be taken into account in order to be intuitively perceivable as a whole pictogram. The experimental comparisons show that our proposed user-adaptive symbol presentation has considerable improved satisfaction related to visual effects and distinction of color/shape than the conventional approach.

This paper presents the interactive web interface for inquiring about the long-term care insurance system in Japan. In Japan, health care services provided by the long-term care insurance system takes costs depending on the health care level. Although the system has been revised several times, such revision made the system more complex, so that a web-based system by which every people can accurately and easily ask about the long-term care insurance system is needed. Thus, the proposed interface in this paper has two points to achieve the requirement. First point is to show only basic information related to selected options on the web page. The second point is that dependencies (parent-child) among each category for inquiring should be presented on the web page to make the user understand each ranges of the category meaning. The experimental evaluation shows that our proposed presentation method using the web interface has advantage on perception among categories, human error, and user-friendliness.