Gain-function optimisation of graphical multiple testing procedures for confirmatory clinical trials
论文提出通过最大化显式增益函数来优化图形多重检验过程，以解决临床试验中选择合适图形的问题，从而更好地反映试验目标。
0 citationsRead paper
Selected work
Representative Papers
PaSTel: Anchoring Histology in Spatial Transcriptomics via Multi-Scale Hierarchical Bio-Prior Contrastive Pretraining
This study addresses the limitations of weak gene discriminability and neglected spatial dependencies in spatial transcriptomics pre-training by proposing PaSTel, a hierarchical multimodal framework. This method introduces a novel three-scale biologically informed contrastive learning mechanism spanning point, functional, and regional levels. By integrating TF-IDF reweighting, KEGG pathway anchoring, and spatial clustering, PaSTel achieves deep alignment between histology and gene expression. Experimental results demonstrate that PaSTel consistently outperforms existing vision and omics encoders across multiple downstream tasks. Crucially, this approach effectively bridges the gap between global semantics and spatial structure, significantly enhancing both representation informativeness and transferability for spatial transcriptomic analysis.
0 citationsRead paper
EFSPI CMCSNE SIG position on the 'Expected f2'
This study systematically evaluates and challenges the scientific validity of the “expected f2” approach for comparing dissolution profiles, particularly its suitability as a replacement for the conventional f2 metric under conditions of high variability. Through comprehensive literature review, statistical analysis, and expert consultation, the EFSPI CMCSNE SIG working group reveals fundamental flaws in the method, including the absence of original theoretical justification, mathematical bias, low statistical power, and ambiguous definition. The findings strongly advise against incorporating “expected f2” into regulatory guidance, thereby providing critical scientific evidence to inform policy decisions and addressing a significant gap in the systematic critique of this methodology.
0 citationsRead paper
EVOCHAMBER: Test-Time Co-evolution of Multi-Agent System at Individual, Team, and Population Scales
Existing test-time multi-agent evolution methods struggle to balance cross-agent learning with collaborative specialization. This work proposes a training-free, multi-scale co-evolution framework that dynamically constructs specialized structures through failure-driven collaborative reflection and asymmetric knowledge transfer across individual, team, and population levels. It is the first approach to enable test-time multi-scale co-evolution, allowing specialized agents to emerge spontaneously while preserving collaborative diversity. Built upon the CODREAM protocol, online team assembly, and population lifecycle operations—including forking, merging, pruning, and seeding—the framework implements heterogeneous task pipelines on Qwen3-8B, achieving accuracies of 63.9%, 75.7%, and 87.1% on competition mathematics, code generation, and multi-domain reasoning tasks, respectively. This represents a 32% relative improvement in mathematical performance and consistently yields 4–5 specialized agents.
0 citationsRead paper
Ground-Level Near Real-Time Modeling for PM2.5 Pollution Prediction
Existing PM2.5 prediction models struggle to simultaneously achieve near real-time performance and high spatiotemporal resolution, limiting their utility in public health decision-making. This study proposes a lightweight, grid-free deep learning architecture that integrates readily available multisource data—including topography, meteorology, and land use—through a spatially randomized sampling training strategy to enable high-accuracy PM2.5 interpolation between sparse monitoring stations. The method supports rapid querying at arbitrary locations, near real-time updates, and flexible deployment across multiple spatial scales, significantly enhancing model generalizability in both dense and sparse monitoring regions. By providing an efficient and practical exposure assessment tool, this approach advances support for epidemiological research and public health emergency response.
0 citationsRead paper
Recent publications
Latest Papers
Gain-function optimisation of graphical multiple testing procedures for confirmatory clinical trials
论文提出通过最大化显式增益函数来优化图形多重检验过程，以解决临床试验中选择合适图形的问题，从而更好地反映试验目标。
0 citationsRead paper
PaSTel: Anchoring Histology in Spatial Transcriptomics via Multi-Scale Hierarchical Bio-Prior Contrastive Pretraining
This study addresses the limitations of weak gene discriminability and neglected spatial dependencies in spatial transcriptomics pre-training by proposing PaSTel, a hierarchical multimodal framework. This method introduces a novel three-scale biologically informed contrastive learning mechanism spanning point, functional, and regional levels. By integrating TF-IDF reweighting, KEGG pathway anchoring, and spatial clustering, PaSTel achieves deep alignment between histology and gene expression. Experimental results demonstrate that PaSTel consistently outperforms existing vision and omics encoders across multiple downstream tasks. Crucially, this approach effectively bridges the gap between global semantics and spatial structure, significantly enhancing both representation informativeness and transferability for spatial transcriptomic analysis.
0 citationsRead paper
EFSPI CMCSNE SIG position on the 'Expected f2'
This study systematically evaluates and challenges the scientific validity of the “expected f2” approach for comparing dissolution profiles, particularly its suitability as a replacement for the conventional f2 metric under conditions of high variability. Through comprehensive literature review, statistical analysis, and expert consultation, the EFSPI CMCSNE SIG working group reveals fundamental flaws in the method, including the absence of original theoretical justification, mathematical bias, low statistical power, and ambiguous definition. The findings strongly advise against incorporating “expected f2” into regulatory guidance, thereby providing critical scientific evidence to inform policy decisions and addressing a significant gap in the systematic critique of this methodology.
0 citationsRead paper
EVOCHAMBER: Test-Time Co-evolution of Multi-Agent System at Individual, Team, and Population Scales
Existing test-time multi-agent evolution methods struggle to balance cross-agent learning with collaborative specialization. This work proposes a training-free, multi-scale co-evolution framework that dynamically constructs specialized structures through failure-driven collaborative reflection and asymmetric knowledge transfer across individual, team, and population levels. It is the first approach to enable test-time multi-scale co-evolution, allowing specialized agents to emerge spontaneously while preserving collaborative diversity. Built upon the CODREAM protocol, online team assembly, and population lifecycle operations—including forking, merging, pruning, and seeding—the framework implements heterogeneous task pipelines on Qwen3-8B, achieving accuracies of 63.9%, 75.7%, and 87.1% on competition mathematics, code generation, and multi-domain reasoning tasks, respectively. This represents a 32% relative improvement in mathematical performance and consistently yields 4–5 specialized agents.
0 citationsRead paper
Ground-Level Near Real-Time Modeling for PM2.5 Pollution Prediction
Existing PM2.5 prediction models struggle to simultaneously achieve near real-time performance and high spatiotemporal resolution, limiting their utility in public health decision-making. This study proposes a lightweight, grid-free deep learning architecture that integrates readily available multisource data—including topography, meteorology, and land use—through a spatially randomized sampling training strategy to enable high-accuracy PM2.5 interpolation between sparse monitoring stations. The method supports rapid querying at arbitrary locations, near real-time updates, and flexible deployment across multiple spatial scales, significantly enhancing model generalizability in both dense and sparse monitoring regions. By providing an efficient and practical exposure assessment tool, this approach advances support for epidemiological research and public health emergency response.
0 citationsRead paper