Research
Papers and datasets worth knowing, ranked by significance and community attention.
GLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System for Robust and Real-Time Global Localization and Mapping
We propose GLIO2, a tightly-coupled LiDAR-Inertial-GNSS system whose GPU-parallel front-end jointly optimizes scan-to-multiscan LiDAR, IMU pre-integration, and raw GNSS measurements in a single sliding-window factor graph, sustaining real-time operation on edge hardware.
Democratizing MoE inference on commodity GPUs with CoMoE
We present CoMoE, a communication-efficient MoE inference system that resolves this mismatch through novel host-centric routing.
Expert Coupling in MoE Pretraining: Reducing All-to-All Overhead with Correlated Placement and Token Shuffling
Mixture-of-Experts (MoE) layers replace the feed-forward block of a Transformer with E expert networks, and each token is routed to k of these experts.
Cova-PINN: Cross-Domain Conservation Physics-Informed Neural Network for Fluid-Solid Conjugate Heat Transfer in Complex Geometries
Multi-domain physics-informed neural networks (PINNs) flexibly model medium-specific representations to solve fluid--solid conjugate heat transfer (CHT).
CPU-Auth: Device Fingerprinting for Authentication via DVFS Side-Channel
This work explores CPU-Auth, a novel authentication mechanism based on unique variations in the physical characteristics of the CPU of a computing device.
PoreML: A Data-Driven Framework for Learning Multiphase Flow in Porous Media
Multiphase flow in porous microstructures is central to CO$2$ storage, fuel-cell operation, and flip-chip packaging.
Embedded Evaluation of Task Admission Coalescing in Decentralized Multi-Robot Systems
Multi-robot task allocators in dynamic missions commonly admit newly released tasks immediately, potentially invoking allocation for each new arrival.
X-OPM: Explainable Automatic Digital On-Chip Power Modeling for Enhanced Robustness
Grounded in the design principles of synchronous digital VLSI circuits, X-OPM introduces a robust feature engineering framework that uses tree-based models to capture feature interactions and linear models for prediction.
Accelerating the Development of PLGA In Situ Forming Depots Through AI-Driven Multi-Objective Optimization
Developing long-acting injectable formulations requires the simultaneous optimization of drug loading, release kinetics, viscosity, injectability, stability and other objectives.