Paper List
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Pharmacophore-based design by learning on voxel grids
This paper addresses the computational bottleneck and limited novelty in conventional pharmacophore-based virtual screening by introducing a voxel cap...
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CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
This paper addresses the critical limitation of current protein structure prediction models (like AlphaFold3) where high-confidence scores (pLDDT) can...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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.