Recent advancements in single-cell sequencing technologies enable the measurement of multiple modalities in individual cells, offering insights into the transcriptome and regulome in various biological systems and human diseases in an unprecedented resolution. However, effectively using these ultra-high-dimensional and large-scale multiomic data to understand gene regulation remains challenging. Inspired by the success of adapting large language models into the genomics field, we develop scMBERT, a BERT framework-based pre-trained deep learning model using single-cell multiomic data. We showed that scMBERT increases model flexibility and performance in downstream tasks like cell type annotation and batch-effect correction, demonstrating the potential of leveraging multiomic data to improve single-cell genomic data analyses.

Secure boot is a fundamental mechanism for establishing a hardware-rooted chain of trust in modern computing systems. While formal verification using interactive theorem provers like Rocq offers strong correctness guarantees, existing approaches are often tightly coupled to specific hardware configurations and cryptographic algorithms, making them brittle and difficult to reuse when system components change. In this work, we present a flexible and modular framework for formally verifying secure boot processes in Rocq. Our approach introduces a three-layer abstraction model-Feature, Axiom, and Theorem-that decouples high-level security properties from lowlevel implementation details. We abstract critical components such as storage media and cryptographic primitives (e.g., encryption, signatures, hash functions) as interfaces with welldefined operators and axiomatic correctness properties. All highlevel proofs are conducted based on these axioms, without depending on concrete implementations. When specific algorithms or hardware are chosen, the axioms become proof obligations to be verified locally. This separation ensures that changes in implementation-such as switching cryptographic primitives or storage types-do not invalidate existing proofs, enabling significant proof reuse. We demonstrate the applicability of our framework in modeling a realistic secure boot chain. Our work lays the foundation for scalable and maintainable formal verification of trust-sensitive system initialization processes.

Approximate nearest neighbor search (ANNS) is essential for applications like recommendation systems and retrieval-augmented generation (RAG) but is highly I/O-intensive and memory-demanding. CPUs face I/O bottlenecks, while GPUs are constrained by limited memory. DRAM-based Processing-in-Memory (DRAM-PIM) offers a promising alternative by providing high bandwidth, large memory capacity, and near-data computation. This work introduces DRIM-ANN, the first optimized ANNS engine leveraging UPMEM’s DRAM-PIM. While UPMEM scales memory bandwidth and capacity, it suffers from low computing power because of the limited processor embedded in each DRAM bank. To address this, we systematically optimize ANNS approximation configurations and replace expensive squaring operations with lookup tables to align the computing requirements with UPMEM’s architecture. Additionally, we propose load-balancing and I/O optimization strategies to maximize parallel processing efficiency. Experimental results show that DRIM-ANN achieves a 2.46× speedup over a 32-thread CPU and up to 2.67× over a GPU when deployed on computationally enhanced PIM platforms.

Collection of clinical information can be a fraught and intimidating process in hospital environments, especially for children with neurological conditions. Gaming and mobile technology offer many potential advantages for data collection where clinically important data can be collected as a side effect of playing games. Niemann-Pick Disease (NPD) is one example of a condition where gaming technology has the potential to greatly help patients and doctors alike. NPD is a severe, often fatal neurological condition where extensive physiological and cognitive information needs to be collected by doctors, typically in hospital environments. To help healthcare providers collect far richer information on NPD patients and reduce the burden/stress of patients and their families alike, a suite of targeted mobile games was developed to capture specific clinical information on NPD patients. These were evaluated with NPD patients and the feedback received was used to tailor the games accordingly. Personalised clinical information obtained through game playing was recorded, analysed and visualised through a flexible and extensible Cloud-based solution including capabilities for tracking patients longitudinally. This work has been directly aligned with the International Niemann-Pick Disease Registry. We suggest that this approach offers the potential for improved processes for data collection and importantly, a less stressful and more cost effective approach for collecting clinical information from and for patients.