Mai Anh Tran, Vy Nguyen, Vova Nguyen, Minh Hoa Bui
During urban flash floods, commuters and guiding agencies require awareness towards which roads remain passable when a vehicle actually arrives, rather than relying solely on which links appear open at initial departure. Most existing flood-routing tools apply a single static snapshot to each route; meaning that corridors that appear clear initially can become flooded before arrival. This paper presents a routing engine that ingests a two-hour water forecast, renewed every 15 minutes, and plans each journey around roads that the forecast closes for that specific vehicle class. Under this formulation, any road that closes mid-trip remains closed for that query, which keeps route calculation fast enough for live requests while operating inside a production GraphHopper routing server. Testing on a recorded storm in western Hanoi revealed that a router checking only departure conditions sent 7.8% of routes into roads flooded upon arrival. In comparable trips, the proposed rule produced longer routes in 3.95% of cases, requiring a median of only 9.2 s of additional travel time. Agencies and navigation platforms can employ this engine to schedule trips by vehicle type, allowing operators to establish depth limits and lookahead margins as policy parameters. However, several limitations must be noted, as the water depths originate from a hydrodynamic simulation that was not verified against measured water levels, the rainfall was replayed rather than forecast live, and depth cutoffs reflect policy settings rather than measured physical thresholds. Keywords: flood routing, time-dependent shortest path, sticky edge closure, forecast water depth, vehicle depth limit, Hanoi
Duc Manh Tran, Ratnadira Widyasari, Ivana Clairine Irsan, Huihui Huang, Ting Zhang, David Lo, Rahul Gopinath, Hong Jin Kang
Automated vulnerability repair must often operate with incomplete vulnerability information: developers may lack an accurate vulnerability classification or a provided proof-of-vulnerability (PoV) test, yet still need to produce a repair. Without detailed information, a key challenge is to explore multiple plausible interpretations of the vulnerability before committing to a repair. To address this challenge, we propose ExploreRepair, a vulnerability repair framework that reconciles the vulnerability observations and repair implementations from a team of exploration agents. We first collect these observations and repair implementations from three agents, each tasked with exploring a specific dimension of the vulnerability. Then, a Lesson Aggregator and a Patch Aggregator independently synthesize repairs from these observations and concrete implementations before a final Aggregator reconciles their proposals without benchmark feedback. On PatchEval, ExploreRepair achieves repair success rates of 39.6% and 71.6% with GPT-5.6-Luna and DeepSeek-V4.1-Flash, respectively, exceeding EvoRepair, a state-of-the-art vulnerability repair approach, by 16.0 and 25.4 percentage points. On VulnLoc+, it achieves success rates of 51.4% and 62.9%, respectively, exceeding LoopRepair's variant without PoV tests by 20.0 and 17.1 percentage points.
Megapixel visual synthesis with latent diffusion models requires operating far beyond the resolutions at which these models are typically trained. Cascade refinement is a leading strategy for this setting: it starts from a base image at a native resolution and progressively upsamples it to the target resolution through multiple refinement stages. Each stage typically performs a pixel-space resolution transition followed by latent-space denoising. While prior acceleration efforts mainly focus on reducing denoising cost, we identify a complementary and underexplored bottleneck: the repeated movement of representations through the VAE. We call these operations pixel-space transitions. In cascade pipelines, intermediate latents are often decoded to RGB, resized, and re-encoded before refinement, while final latents are decoded patch by patch with dense overlap to suppress boundary artifacts. We propose UltraFlash, a transition-efficient approach that accelerates these VAE-heavy operations. UltraFlash has two components. Latent Hyperloop replaces intermediate RGB round trips with a learned latent-space shortcut that approximates the decode-resize-encode transformation without repeated VAE decoding and encoding. Sparse Patch-based Decoding accelerates final reconstruction by addressing patch seams caused by inconsistent GroupNorm statistics and reusing cached statistics across patches, enabling sparse VAE decoding with much smaller overlap. Integrated into a state-of-the-art cascade baseline, UltraFlash reduces 4K generation latency by approximately 2x while improving image quality. These results show that optimizing pixel-space transitions can improve both efficiency and fidelity, offering an effective route toward fast, high-quality megapixel visual synthesis.