AI Applications

GLAFF: A novel framework that significantly improves time series forecasting robustness by fusing global timestamp information with local observations, achieving 12.5% average performance enhancement.

AI-powered dual-target drug design is revolutionized by repurposing pretrained diffusion models, achieving zero-shot transfer learning and outperforming existing methods.

ReMAP repurposes able-bodied motion prediction models for limb-impaired patients using network inversion and retrieval-augmented mapping, significantly improving motion forecasting.

This research shows how to improve Stackelberg game strategies by considering side information, achieving no-regret learning in online settings with stochastic contexts or followers.

BeTopNet uses braid theory to create a topological representation of multi-agent future driving behaviors, improving prediction and planning accuracy in autonomous driving systems.

realSEUDO: Real-time calcium imaging analysis now possible at speeds exceeding 30 Hz, enabling sophisticated closed-loop neuroscience experiments.

RCDN: Robust, camera-insensitive collaborative perception via dynamic 3D neural modeling, overcoming camera failures for high-performance autonomous systems.

Randomized sparse matrix compression boosts large-scale cancer radiotherapy optimization, improving treatment quality without sacrificing speed.

RadarOcc: Revolutionizing autonomous driving with robust 3D occupancy prediction using 4D imaging radar, overcoming limitations of LiDAR and camera-based approaches.

QueST: A novel self-supervised skill abstraction architecture for continuous robot control, achieves state-of-the-art performance on multitask and few-shot learning benchmarks by learning flexible, tr…

Pairwise Softmax Loss (PSL) improves recommendation accuracy by enhancing Softmax Loss (SL) with alternative activation functions, resulting in tighter ranking metric surrogates and better noise resis…

Generative AI’s unique characteristics necessitate new pricing strategies; this paper models a sequential pricing game between competing firms, revealing the first-mover’s performance needs to be sign…

PAD, a novel visual policy learning framework, unifies image prediction and robot action in a joint denoising process, achieving significant performance improvements in robotic manipulation tasks.

Predicting RL agent behavior is key for safety and interaction; this study reveals that explicitly planned agents are significantly easier to predict due to their internal plans.

SpatioTemporal Video Diffusion (STVD) revolutionizes precipitation downscaling using a novel diffusion model, achieving state-of-the-art accuracy in super-resolution while capturing crucial distributi…

A novel algorithm achieves a 0.385-approximation for submodular maximization under cardinality constraints, combining strong theoretical guarantees with practical query complexity.

PowerPM: A foundation model revolutionizing power system analysis by mastering complex ETS data through a novel self-supervised pre-training approach, achieving state-of-the-art performance.

PiMForce: Robust hand pressure estimation using 3D hand posture and sEMG!

Physics-regularized multi-modal image assimilation improves brain tumor localization by integrating data-driven and physics-based cost functions, achieving state-of-the-art performance in capturing tu…

Performative control, where control policies change system dynamics, is analyzed; offering sufficient conditions for unique solutions, and proposing a convergent algorithm for achieving them.