Zero-1-to-3: Domain-level Zero-shot Cognitive Diagnosis via One Batch of Early-bird Students towards Three Diagnostic Objectives

Cognitive diagnosis seeks to estimate the cognitive states of students by exploring their logged practice quiz data. It plays a pivotal role in personalized learning guidance within intelligent education systems. In this paper, we focus on an important, practical, yet often underexplored task: domain-level zero-shot cognitive diagnosis (DZCD), which arises due to the absence of student practice logs in newly launched domains. Recent cross-domain diagnostic models have been demonstrated to be a promising strategy for DZCD. These methods primarily focus on how to transfer student states across domains. However, they might inadvertently incorporate non-transferable information into student representations, thereby limiting the efficacy of knowledge transfer. To tackle this, we propose Zero-1-to-3, a domain-level zero-shot cognitive diagnosis framework via one batch of early-bird students towards three diagnostic objectives. Our approach initiates with pre-training a diagnosis model with dual regularizers, which decouples student states into domain-shared and domain-specific parts. The shared cognitive signals can be transferred to the target domain, enriching the cognitive priors for the new domain, which ensures the cognitive state propagation objective. Subsequently, we devise a strategy to generate simulated practice logs for cold-start students through analyzing the behavioral patterns from early-bird students, fulfilling the domain-adaption goal. Consequently, we refine the cognitive states of cold-start students as diagnostic outcomes via virtual data, aligning with the diagnosis-oriented goal. Finally, extensive experiments on six real-world datasets highlight the efficacy of our model for DZCD and its practical application in question recommendation. The code is publicly available at https://github.com/bigdata-ustc/Zero-1-to-3.Comment: Accepted by AAAI202

The AI-2/luxS Quorum Sensing System Affects the Growth Characteristics, Biofilm Formation, and Virulence of Haemophilus parasuis

Haemophilus parasuis (H. parasuis) is a kind of opportunistic pathogen of the upper respiratory tract of piglets. Under certain circumstances, virulent strains can breach the mucosal barrier and enter the bloodstream, causing severe Glässer's disease. Many virulence factors are found to be related to the pathogenicity of H. parasuis strain, but the pathogenic mechanism remains unclear. LuxS/AI-2, as a kind of very important quorum sensing system, affects the growth characteristics, biofilm formation, antibiotic production, virulence, and metabolism of different strains. In order to investigate the effect of luxS/AI-2 quorum sensing system on the virulence of H. parasuis, a deletion mutant strain (ΔluxS) and complemented strain (C-luxS) were constructed and characterized. The results showed that the luxS gene participated in regulating and controlling stress resistance, biofilm formation and virulence. Compared with wild-type strain, ΔluxS strain decreased the production of AI-2 molecules and the tolerance toward oxidative stress and heat shock, and it reduced the abilities of autoagglutination, hemagglutination, and adherence, whereas it increased the abilities to form biofilm in vitro. In vivo experiments showed that ΔluxS strain attenuated its virulence about 10-folds and significantly decreased its tissue burden of bacteria in mice, compared with the wild-type strain. Taken together, the luxS/AI-2 quorum sensing system in H. parasuis not only plays an important role in growth and biofilm formation, but also affects the pathogenicity of H. parasuis