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Participant recruitment based on unstructured medical texts such as clinical notes and radiology reports has been a challenging yet important task for the cohort establishment in clinical research. Recently, Large Language Models (LLMs)…
The amount of large-scale scientific computing software is dramatically increasing. In this work, we designed a new language, named feature query language (FQL), to collect and extract software features from a quick static code analysis. We…
Flaky tests obstruct software development, and studying and proposing mitigations against them has therefore become an important focus of software engineering research. To conduct sound investigations on test flakiness, it is crucial to…
The world's largest particle accelerator, located at CERN, produces petabytes of data that need to be analysed efficiently, to study the fundamental structures of our universe. ROOT is an open-source C++ data analysis framework, developed…
Digital systems are growing in importance and computing hardware is growing more heterogeneous. Hardware design, however, remains laborious and expensive, in part due to the limitations of conventional hardware description languages (HDLs)…
Interpreting mathematical expressions at runtime is a standard task in scientific software engineering. There are different approaches to this problem from creating an embedded domain-specific language (eDSL) with its own parser and…
Key Performance Indicators (KPIs) are essential time-series metrics for ensuring the reliability and stability of many software systems. They faithfully record runtime states to facilitate the understanding of anomalous system behaviors and…
Density Functional Theory (DFT) is the de facto workhorse for large-scale electronic structure calculations in chemistry and materials science. While plane-wave DFT implementations remain the most widely used, real-space DFT provides…
Parallel dataflow systems have become a standard technology for large-scale data analytics. Complex data analysis programs in areas such as machine learning and graph analytics often involve control flow, i.e., iterations and branching.…
The Australian Square Kilometre Array Pathfinder (ASKAP) presents a number of challenges in the area of source finding and cataloguing. The data rates and image sizes are very large, and require automated processing in a high-performance…
Reproducibility of research is essential for science. However, in the way modern computational biology research is done, it is easy to lose track of small, but extremely critical, details. Key details, such as the specific version of a…
Scientific simulation leveraging high-performance computing (HPC) systems is crucial for modeling complex systems and phenomena in fields such as astrophysics, climate science, and fluid dynamics, generating massive datasets that often…
Fine-grained Entity Typing is a tough task which suffers from noise samples extracted from distant supervision. Thousands of manually annotated samples can achieve greater performance than millions of samples generated by the previous…
Given AI systems like ChatGPT can generate content that is indistinguishable from human-made work, the responsible use of this technology is a growing concern. Although understanding the benefits and harms of using AI systems requires more…
With the proliferation of data across various domains, there is a critical demand for tools that enable non-experts to derive meaningful insights without deep data analysis skills. To address this need, existing automatic fact sheet…
In this work, we make \emph{systematic} optimizations of key encapsulation mechanisms (KEM) based on module learning-with-errors (MLWE), covering algorithmic design, fundamental operation of number-theoretic transform (NTT), approaches to…
The recent progress of artificial intelligence(AI) has shown great potentials for alleviating human burden in various complex tasks. From the view of software engineering, AI techniques can be seen in many fundamental aspects of…
Most evaluations of large language models focus on standard tasks such as factual question answering or short summarization. This research expands that scope in two directions: first, by comparing two retrieval strategies, Graph RAG,…
Large language models (LLMs) are playing an increasingly important role in science and engineering. For example, their ability to parse and understand human and computer languages makes them powerful interpreters and their use in…
While anomaly detection stands among the most important and valuable problems across many scientific domains, anomaly detection research often focuses on AI methods that can lack the nuance and interpretability so critical to conducting…