arXiv subjects

Zhiyuan Ruan

Publications and source records attributed to Zhiyuan Ruan.

3 recordsLinked to original sources

arXiv2026

DeepRHP: A Hybrid Variational Autoencoder for Designing Random Heteropolymers as Protein Mimics

Synthetic random heteropolymers (RHPs), consisting of a predefined set of monomers, offer an approach toward the design of protein-like materials. These RHPs, if designed appropriately, can mimic protein behavior and function. As such, there is a need for computational tools to efficiently guide RHP design. We bridge this gap by developing DeepRHP, a modified variational autoencoder (VAE) model under a semi-supervised framework. By equipping a classical VAE with an additional feature-based VAE, DeepRHP forces the latent space to capture structures of critical chemical features as well as individual RHP sequence patterns. In this sense, our method is versatile by allowing any relevant features to be incorporated in a hybrid manner. We demonstrate the effectiveness of DeepRHP by suggesting potential monomer compositions that stabilize membrane proteins (e.g. Aquaporin Z) in non-native environments and cross-validating our prediction with published results. The concordance between our model and true RHP function suggests strong potential in utilizing hybrid autoencoder architectures to guide RHP design for proteins and other biological compounds.

cs.LG↗

arXiv2026

Pomegranate: A Lightweight Compartmentalization Architecture using Virtualization Extensions

The monolithic nature of widely used commodity operating systems means that vulnerabilities in one software component potentially compromise the entire kernel. Formally verifying these systems, or redesigning them altogether as microkernels, according to the principle of least privilege, requires significant effort. Researchers have therefore considered compartmentalization techniques that minimize or totally avoid changes to existing systems. However, current approaches use techniques such as Memory Protection Keys (MPKs), necessitating extensive code analysis to ensure security, or use virtualization by instrumenting the kernel with calls to the glue code that switches compartments. In this work, we present Pomegranate, a framework that uses hardware-assisted virtualization to securely compartmentalize an existing system with minimal to no modifications to its source code. Allowed interactions between compartments are defined using an access-control policy and strictly enforced using Extended Page Tables. Using special sentry functions, Pomegranate is able to check all cross-compartment transitions without trapping into the hypervisor. We demonstrate the efficacy of Pomegranate on a compartmentalized Linux network stack using the igc NIC driver. Experiments show the overheads of our approach are negligible at MTU-sized packets when compartment boundaries are carefully established to avoid excessive inter-compartment communication.

cs.CR↗

arXiv2026

DUALVISION: RGB-Infrared Multimodal Large Language Models for Robust Visual Reasoning

Multimodal large language models (MLLMs) have achieved impressive performance on visual perception and reasoning tasks with RGB imagery, yet they remain fragile under common degradations, such as fog, blur, or low-light conditions. Infrared (IR) imaging, a well-established complement to RGB, offers inherent robustness in these conditions, but its integration into MLLMs remains underexplored. To bridge this gap, we propose DUALVISION, a lightweight fusion module that efficiently incorporates IR-RGB information into MLLMs via patch-level localized cross-attention. To support training and evaluation and to facilitate future research, we also introduce DV-204K, a dataset of ~25K publicly available aligned IR-RGB image pairs with 204K modality-specific QA annotations, and DV-500, a benchmark of 500 IR-RGB image pairs with 500 QA pairs designed for evaluating cross-modal reasoning. Leveraging these datasets, we benchmark both open- and closed-source MLLMs and demonstrate that DUALVISION delivers strong empirical performance under a wide range of visual degradations. Our code and dataset are available at https://abrarmajeedi.github.io/dualvision.

cs.CV↗