Paper List
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EnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic Constants
This paper addresses the core challenge of jointly predicting enzyme kinetic parameters (Kcat and Km) by modeling dynamic enzyme-substrate interaction...
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Tissue stress measurements with Bayesian Inversion Stress Microscopy
This paper addresses the core challenge of measuring absolute, tissue-scale mechanical stress without making assumptions about tissue rheology, which ...
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DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
This study addresses the critical gap between high predictive accuracy and biological interpretability in DeepFRI, revealing that the model often prio...
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Hierarchical Molecular Language Models (HMLMs)
This paper addresses the core challenge of accurately modeling context-dependent signaling, pathway cross-talk, and temporal dynamics across multiple ...
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Stability analysis of action potential generation using Markov models of voltage‑gated sodium channel isoforms
This work addresses the challenge of systematically characterizing how the high-dimensional parameter space of Markov models for different sodium chan...
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Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
This paper addresses the critical limitation of one-size-fits-all HD-tDCS protocols in pediatric populations by developing a personalized optimization...
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Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
This paper addresses the core pain point of low sequence-structure alignment in existing synthetic datasets (e.g., AFDB), which severely limits the pe...
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Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A
This paper addresses the critical bottleneck in therapeutic peptide design: how to efficiently optimize lead peptides with geometric constraints while...
Human-Centred Evaluation of Text-to-Image Generation Models for Self-expression of Mental Distress: A Dataset Based on GPT-4o
School of Culture and Communication, Swansea University, United Kingdom | Department of Informatics, University of Oslo, Norway
The 30-Second View
IN SHORT: This paper addresses the critical gap in evaluating how AI-generated images can effectively support cross-cultural mental distress communication, particularly for international students facing linguistic and cultural barriers.
Innovation (TL;DR)
- Methodology Introduces the first publicly available text-to-image evaluation dataset with human judgment scores specifically for mental health communication, comprising 100 textual descriptions, 400 AI-generated images, and 400 categorical evaluation scores.
- Methodology Develops and evaluates four persona-based prompt templates (basic, illustrator, photographer, creative artist) rooted in contemporary counselling practices, with the illustrator persona achieving the highest total helpfulness score (284 out of possible 600).
- Biology Demonstrates that AI-generated images can facilitate self-expression of mental distress, with 44% of images rated as 'slightly helpful' and 27% as 'helpful', achieving a mean helpfulness score of 2.4 on a 0-6 scale.
Key conclusions
- The illustrator persona prompt achieved the highest total helpfulness score (284) and was selected as the 'best' image in 31% of cases, significantly outperforming other prompts (basic: 252, creative artist: 218, photographer: 210).
- Human evaluation shows minimal correlation with automatic semantic alignment metrics (Spearman's ρ=0.0271, Kendall's τ=0.0201), highlighting the need for emotion-aware evaluation frameworks beyond traditional similarity measures.
- AI-generated images demonstrated positive utility for mental distress expression, with 71% of images rated as at least 'slightly helpful' (score ≥2) and only 29% rated as 'not helpful' (score=0).
Abstract: Effective communication is central to achieving positive healthcare outcomes in mental health contexts, yet international students often face linguistic and cultural barriers that hinder their communication of mental distress. In this study, we evaluate the effectiveness of AI-generated images in supporting self-expression of mental distress. To achieve this, twenty Chinese international students studying at UK universities were invited to describe their personal experiences of mental distress. These descriptions were elaborated using GPT-4o with four persona-based prompt templates rooted in contemporary counselling practice to generate corresponding images. Participants then evaluated the helpfulness of generated images in facilitating the expression of their feelings based on their original descriptions. The resulting dataset comprises 100 textual descriptions of mental distress, 400 generated images, and corresponding human evaluation scores. Findings indicate that prompt design substantially affects perceived helpfulness, with the illustrator persona achieving the highest ratings. This work introduces the first publicly available text-to-image evaluation dataset with human judgment scores in the mental health domain, offering valuable resources for image evaluation, reinforcement learning with human feedback, and multi-modal research on mental health communication.