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Although math anxiety (MA) and math performance are generally negatively correlated (Barroso et al., 2021), several studies reported variability in the strength of this association (Lyons & Beilock, 2012a; Tsui & Mazzocco, 2006; Wang et al., 2015). The present study investigated emotion regulation and motivation as potential mechanisms underlying this heterogeneity, particularly with regard to the attention patterns underlying successful math performance. A sample of 207 elementary and middle school students completed a math problem-solving task, during which their attention was measured using eye-tracking. Students’ trait level and state level emotion regulation and motivation were assessed using self-reports and physiological measures, respectively. Our findings revealed that the use of reappraisal as an emotion regulation strategy mitigated attentional interference during math problem-solving, which in turn attenuated performance deficits among students with high MA. In addition, students with high MA exhibited more avoidance of the math problems only if their physiological pattern indicated low state motivation. These findings highlight the importance of enhancing both reappraisal and motivation as potential intervention targets to combat math deficits among students with high MA. 

Abstract Previous geodetic and teleseismic observations of the 2021 7.4 Maduo earthquake imply surprising but difficult‐to‐constrain complexity, including rupture across multiple fault segments and supershear rupture. Here, we present an integrated analysis of multi‐fault 3D dynamic rupture models, high‐resolution optical correlation analysis, and joint optical‐InSAR slip inversion. Our preferred model, validated by the teleseismic multi‐peak moment rate release, includes unilateral eastward double‐onset supershear speeds and cascading rupture dynamically triggering two adjacent fault branches. We propose that pronounced along‐strike variation in fracture energy, complex fault geometries, and multi‐scale variable prestress drives this event's complex rupture dynamics. We illustrate how supershear transition has signatures in modeled and observed off‐fault deformation. Our study opens new avenues to combine observations and models to better understand complex earthquake dynamics, including local and potentially repeating supershear episodes across immature faults or under heterogeneous stress and strength conditions, which are potentially not unusual. 

Although Large Language Models (LLMs) succeed in human-guided conversations such as instruction following and question answering, the potential of LLM-guided conversations-where LLMs direct the discourse and steer the conversation's objectives-remains under-explored. In this study, we first characterize LLM-guided conversation into three fundamental components: (i) Goal Navigation; (ii) Context Management; (iii) Empathetic Engagement, and propose GuideLLM as an installation. We then implement an interviewing environment for the evaluation of LLM-guided conversation. Specifically, various topics are involved in this environment for comprehensive interviewing evaluation, resulting in around 1.4k turns of utterances, 184k tokens, and over 200 events mentioned during the interviewing for each chatbot evaluation. We compare GuideLLM with 6 state-of-the-art LLMs such as GPT-4o and Llama-3-70b-Instruct, from the perspective of interviewing quality, and autobiography generation quality. For automatic evaluation, we derive user proxies from multiple autobiographies and employ LLM-as-a-judge to score LLM behaviors. We further conduct a human-involved experiment by employing 45 human participants to chat with GuideLLM and baselines. We then collect human feedback, preferences, and ratings regarding the qualities of conversation and autobiography. Experimental results indicate that GuideLLM significantly outperforms baseline LLMs in automatic evaluation and achieves consistent leading performances in human ratings. 

Accurate diagnosis and prognosis assisted by pathology images are essential for cancer treatment selection and planning. Despite the recent trend of adopting deep-learning approaches for analyzing complex pathology images, they fall short as they often overlook the domain-expert understanding of tissue structure and cell composition. In this work, we focus on a challenging Open-ended Pathology VQA (PathVQA-Open) task and propose a novel framework named Path-RAG, which leverages HistoCartography to retrieve relevant domain knowledge from pathology images and significantly improves performance on PathVQA-Open. Admitting the complexity of pathology image analysis, Path-RAG adopts a human-centered AI approach by retrieving domain knowledge using HistoCartography to select the relevant patches from pathology images. Our experiments suggest that domain guidance can significantly boost the accuracy of LLaVA-Med from 38% to 47%, with a notable gain of 28% for H&E-stained pathology images in the PathVQA-Open dataset. For longer-form question and answer pairs, our model consistently achieves significant improvements of 32.5% in ARCH-Open PubMed and 30.6% in ARCH-Open Books on H\&E images. 

Although Large Language Models (LLMs) succeed in human-guided conversations such as instruction following and question answering, the potential of LLM-guided conversations—where LLMs direct the discourse and steer the conversation’s objectives—remains largely untapped. In this study, we provide an exploration of the LLM-guided conversation paradigm. Specifically, we first characterize LLM-guided conversation into three fundamental properties: (i) Goal Navigation; (ii) Context Management; (iii) Empathetic Engagement, and propose GUIDELLM as a general framework for LLM-guided conversation. We then implement an autobiography interviewing environment as one of the demonstrations of GuideLLM, which is a common practice in Reminiscence Therapy. In this environment, various techniques are integrated with GUIDELLM to enhance the autonomy of LLMs, such as Verbalized Interview Protocol (VIP) and Memory Graph Extrapolation (MGE) for goal navigation, and therapy strategies for empathetic engagement. We compare GUIDELLM with baseline LLMs, such as GPT-4-turbo and GPT-4o, from the perspective of interviewing quality, conversation quality, and autobiography generation quality. Experimental results encompassing both LLM-as-a-judge evaluations and human subject experiments involving 45 participants indicate that GUIDELLM significantly outperforms baseline LLMs in the autobiography interviewing task.