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Qinglin Wu

Publications and source records attributed to Qinglin Wu.

2 recordsLinked to original sources

Sensing, Traffic, and Construction in Termites

Subterranean and mound-building termites excavate, transport, and build within the same granular substrate that later regulates how they sense, move, and deposit material. From antennal-scale contacts through body-scale traffic to meter-scale architecture, this review synthesizes three linked problems: how workers sense local geometry and physical cues during search and excavation; how traffic moves through narrow, evolving conduits; and how excavation and deposition remodel the substrate that guides later behavior. Across these length scales, noisy local interactions couple sensing, transport, and construction through a shared material medium, leading to emergent order at the colony scale. We emphasize what is established experimentally, where evidence remains sparse or limited to a few model systems, and how emerging imaging, tracking, and modeling tools are making these feedbacks quantitatively accessible. We use this synthesis to motivate a quantitative physics-of-life framework for termite colonies that continually rewrite the medium through which they sense, move, and build.

physics.bio-ph

ScIRGen: Synthesize Realistic and Large-Scale RAG Dataset for Scientific Research

Scientific researchers need intensive information about datasets to effectively evaluate and develop theories and methodologies. The information needs regarding datasets are implicitly embedded in particular research tasks, rather than explicitly expressed in search queries. However, existing scientific retrieval and question-answering (QA) datasets typically address straightforward questions, which do not align with the distribution of real-world research inquiries. To bridge this gap, we developed ScIRGen, a dataset generation framework for scientific QA \& retrieval that more accurately reflects the information needs of professional science researchers, and uses it to create a large-scale scientific retrieval-augmented generation (RAG) dataset with realistic queries, datasets and papers. Technically, we designed a dataset-oriented information extraction method that leverages academic papers to augment the dataset representation. We then proposed a question generation framework by employing cognitive taxonomy to ensure the quality of synthesized questions. We also design a method to automatically filter synthetic answers based on the perplexity shift of LLMs, which is highly aligned with human judgment of answers' validity. Collectively, these methodologies culminated in the creation of the 61k QA dataset, ScIRGen-Geo. We benchmarked representative methods on the ScIRGen-Geo dataset for their question-answering and retrieval capabilities, finding out that current methods still suffer from reasoning from complex questions. This work advances the development of more sophisticated tools to support the intricate information needs of the scientific community.

cs.CL