12 papers · 1 filter
The contribution of Soviet scientists to the emergence of high-energy neutrino astrophysics: from the Cherenkov effect to underwater and in-ice telescopes
Bakhromzod Rizoi
The 2026 Nobel Prize in Physics was awarded to Francis Halzen @for decisive contributions to the IceCube Neutrino Observatory and the discovery of high-energy neutrinos of astrophy…
One Frame, Full Heartbeat: ECG-Free Cardiac Cine MRI Synthesis via Phase-Conditioned Flow Matching
Shiyi Wang, Ruochen Sun, Peirong Liu +2
Cine cardiovascular magnetic resonance (CMR) analysis relies on multi-frame sequences capturing the full cardiac cycle. However, standard multi-frame acquisition depends heavily on…
Vision--Language Signals in Constrained RL: Safety Gains Without Anticipation
Samuel Tetteh, Cody Fleming
Safe reinforcement learning seeks policies that maximise task performance while satisfying safety constraints. In driving benchmarks, however, collision costs typically appear only…
A Mean-Field Approach for Safe Routing of Multi-Destination Urban Air Mobility Networks
Nameer Fawwaz Ahmed, Cody Fleming, Yasser Shoukry
As Urban Air Mobility (UAM) systems scale toward high-density operations, managing autonomous Unmanned Aerial Vehicle (UAV) traffic requires control frameworks that are both tracta…
Inverting Self-Triggered Control: Adversarial Reinforcement Learning for Sparse Denial-of-Service Attacks
Adam Haroon, Erick J. Rodríguez-Seda, Tristan Schuler +1
Self-triggered reinforcement learning control (RL-STC) learns the sparsest control schedule that preserves Lyapunov-decreasing stability under a Run-Time Assurance (RTA) override.…
Certified Safety Curation: Distribution-Free Guarantees for Safe Offline Reinforcement Learning
Adam Haroon, Cody Fleming
Safe offline reinforcement learning assumes a cost function on every transition. We ask what remains possible when safety can be judged only by comparing short clips and occasional…
Who Analyses the Analyser? Self-Validating LLM Hazard Analysis with Constitutional Meta-STPA
Samuel Tetteh, Udip Shrestha, Joshua R. Waite +1
Large language models (LLMs) are increasingly trusted to draft the artifacts of safety analysis such as, losses, hazards, Unsafe Control Actions (UCAs), and safety constraints, ins…
Seeing Before Colliding: Anticipatory Safe RL with Frozen Vision-Language Models
Samuel Tetteh, Cody Fleming
The cost signal that constrained-RL algorithms optimize against is almost always reactive: the simulator emits a non-zero cost only after a collision has begun, and the Lagrange mu…
COOPO: Cyclic Offline-Online Policy Optimization Algorithm
Qisai Liu, Zhanhong Jiang, Joshua Russell Waite +3
Offline reinforcement learning struggles with distributional shift and constrained performance due to static dataset limitations, while online RL demands prohibitive environment in…
Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance
Adam Haroon, Erick J. Rodríguez-Seda, Cody Fleming +1
Safe reinforcement learning (RL) typically asks an agent should do. We ask it needs to act, and show that a single policy can jointly learn control…
LexiSafe: Offline Safe Reinforcement Learning with Lexicographic Safety-Reward Hierarchy
Hsin-Jung Yang, Zhanhong Jiang, Prajwal Koirala +3
Offline safe reinforcement learning (RL) is increasingly important for cyber-physical systems (CPS), where safety violations during training are unacceptable and only pre-collected…
LCLA: Language-Conditioned Latent Alignment for Vision-Language Navigation
Nitesh Subedi, Adam Haroon, Samuel Tetteh +3
We propose LCLA (Language-Conditioned Latent Alignment), a framework for vision-language navigation that learns modular perception-action interfaces by aligning sensory observation…