Tootfinder

OpenCCA: An Open Framework to Enable Arm CCA Research
Andrin Bertschi, Shweta Shinde
https://arxiv.org/abs/2506.05129 https://arxiv.o…

OpenCCA: An Open Framework to Enable Arm CCA Research
Confidential computing has gained traction across major architectures with Intel TDX, AMD SEV-SNP, and Arm CCA. Unlike TDX and SEV-SNP, a key challenge in researching Arm CCA is the absence of hardware support, forcing researchers to develop ad-hoc performance prototypes on non-CCA Arm boards. This approach leads to duplicated efforts, inconsistent performance comparisons, and high barriers to entry. To address this, we present OpenCCA, an open research platform that enables the execution of CC…

Some very quirky facts in the background to the recent Westminster decision to label the protest organisation Palestinian Action as "terrorists", thereby making their existence illegal. [This is the archived version so no paywall.]

DuAL-Net: A Hybrid Framework for Alzheimer's Disease Prediction from Whole-Genome Sequencing via Local SNP Windows and Global Annotations
Eun Hye Lee, Taeho Jo
https://arxiv.org/abs/2506.00673

DuAL-Net: A Hybrid Framework for Alzheimer's Disease Prediction from Whole-Genome Sequencing via Local SNP Windows and Global Annotations
Alzheimer's disease (AD) dementia is the most common form of dementia. With the emergence of disease-modifying therapies, predicting disease risk before symptom onset has become critical. We introduce DuAL-Net, a hybrid deep learning framework for AD dementia prediction using whole genome sequencing (WGS) data. DuAL-Net integrates two components: local probability modeling, which segments the genome into non-overlapping windows, and global annotation-based modeling, which annotates SNPs and reo…

Uncertainty-Aware Genomic Classification of Alzheimer's Disease: A Transformer-Based Ensemble Approach with Monte Carlo Dropout
Taeho Jo, Eun Hye Lee, Alzheimer's Disease Sequencing Project
https://arxiv.org/abs/2506.00662

Uncertainty-Aware Genomic Classification of Alzheimer's Disease: A Transformer-Based Ensemble Approach with Monte Carlo Dropout
INTRODUCTION: Alzheimer's disease (AD) is genetically complex, complicating robust classification from genomic data. METHODS: We developed a transformer-based ensemble model (TrUE-Net) using Monte Carlo Dropout for uncertainty estimation in AD classification from whole-genome sequencing (WGS). We combined a transformer that preserves single-nucleotide polymorphism (SNP) sequence structure with a concurrent random forest using flattened genotypes. An uncertainty threshold separated samples into …