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Related Articles from SNS
Uncovering Turbulent Dynamics in Stenotic Flows from 4D-flow MRI Measurements via Resolvent Analysis and Data Assimilation
arXiv:2606.03838v1 Announce Type: new Abstract: This study presents a hybrid experimental and computational framework that couples in vitro 4D phase-contrast magnetic resonance imaging (4D-flow MRI) measurements with data assimilation and linear modeling to characterize the flow linear amplification mechanisms. We manufacture an idealized stenosis phantom with a cosine-shaped contraction and acquire three-dimensional (3D) mean velocity measurements at Reynolds number 3960 using 4D-flow MRI....
SAM-Flow: Source-Anchored Masked Flow for Training-Free Image Editing
arXiv:2606.06228v1 Announce Type: new Abstract: Training-free image editing has recently attracted increasing attention due to its ability to modify real images using powerful pre-trained diffusion and flow-matching models without additional training. However, existing inversion-based and differential-flow-based methods usually perform global latent transport, which inevitably propagates editing effects to non-target regions and leads to background leakage. To address this problem, we...
Flow-HOA: Generative Joint Optimization for Ambisonics Encoding via Flow Matching
Announce Type: new Abstract: Higher-Order Ambisonics (HOA) encoding from sparse, irregular microphone arrays remains a critical challenge for consumer spatial audio capture in immersive communication and XR. We propose Flow-HOA, a generative framework that jointly optimizes a multi-dimensional objective encompassing time-domain, spectral, and spatial fidelity while producing a deployable, time-invariant bank of Finite Impulse Response (FIR) encoding filters. Using conditional flow matching,...
Better Source, Better Flow: Learning Condition-Dependent Source Distribution for Flow Matching
Announce Type: replace Abstract: Flow matching has recently emerged as a promising alternative to diffusion-based generative models, particularly for text-to-image generation. Despite its flexibility in allowing arbitrary source distributions, most existing approaches rely on a standard Gaussian distribution, a choice inherited from diffusion models, and rarely consider the source distribution itself as an optimization target in such settings.
Let the Dynamics Flow: Stable Flow Matching Dynamical Systems
Announce Type: new Abstract: Flow matching has recently emerged as a powerful approach for imitation learning, enabling scalable, expressive, and multimodal motion policies. However, incorporating formal stability guarantees into these generative models, a prerequisite to ensure safe and generalizable robot behaviors, remains a significant challenge. While modeling robot motions as dynamical systems allows for such stability-based inductive biases, existing frameworks struggle to capture the...
How to Guide Your Flow: Few-Step Alignment via Flow Map Reward Guidance
arXiv:2604.27147v3 Announce Type: replace Abstract: In generative modeling, we often wish to produce samples that maximize a user-specified reward such as aesthetic quality or alignment with human preferences, a problem known as \textit{guidance}. Despite their widespread use, existing guidance methods either require expensive multi-particle, many-step schemes or rely on poorly understood approximations. We reformulate guidance as a \textit{deterministic optimal control problem}, yielding a...
Rainfall and river flow: weekly reports for England
Rainfall and river flow: weekly reports for England Weekly reports on rainfall and river flow in England for the last 3 months. Applies to England Documents Details These reports collect information from the Environment Agency, the Met Office and water companies to report on the: - amount of rain that falls - amount of water flowing in rivers - outlook See reports for previous weeks on the UK government web archive. Updates to this page - Added the rainfall and river flow summary for 20 to...
SABLE: GPU-Based Power Flow Accelerator for Sparsity-Aware Batched Learning
arXiv:2606.07099v1 Announce Type: new Abstract: Recent studies have developed GPU-based approaches for solving AC power flow and successfully applied them to standalone power flow problems. However, integrating these approaches into modern differentiable learning frameworks while preserving sparsity remains challenging. To this end, we present SABLE, a GPU-based sparse batched power flow accelerator for differentiable learning via an implicit power flow layer.
Passive transverse forcing of turbulent boundary-layer flow using sinusoidal surface grooves
arXiv:2606.03555v1 Announce Type: new Abstract: A surface geometry consisting of parallel, meandering streamwise grooves has been experimentally studied as an alternative means of passive transverse forcing of turbulent boundary-layer flow. Contrary to the original expectation, the flow does not exhibit a spanwise-uniform undulation aligned with the grooves; instead, a converging-diverging flow pattern results. This flow pattern can be attributed to the spanwise periodicity of the lateral...
Coreset-Induced Conditional Velocity Flow Matching
Announce Type: replace-cross Abstract: We propose Coreset-Induced Conditional Velocity Flow Matching (CCVFM), a generative model that augments hierarchical rectified flow with a data-informed source distribution. Hierarchical flow matching models the full conditional velocity law in velocity space, but its inner flow is asked to transport isotropic Gaussian noise to a multimodal target velocity distribution from scratch. Our key observation is that this inner source can be replaced by a...