DMD
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Related Articles from SNS
Pathology-Targeted EP4 Agonism Reverses Fibrosis in a Rat Model of DMD
Duchenne muscular dystrophy (DMD) presents a critical therapeutic gap in adolescent patients, where fibro-fatty muscle replacement and depletion of the regenerative niche render existing interventions insufficient. Prostaglandin E2 signaling through the EP4 receptor stimulates muscle regeneration, but systemic off-target effects have limited clinical translation of EP4 agonism. We evaluated irodanoprost (IROD), a bone-targeted prodrug of an EP4-selective agonist, in a DMD rat model using...
Why Are DMD Students Lazy? Understanding the Copying Behavior in Few-Step Distillation
Announce Type: new Abstract: Distribution Matching Distillation (DMD) compresses pretrained diffusion models into efficient few-step generators by aligning their noised distributions across all scales. In principle, such distribution-level supervision remains agnostic to specific noise-data pairings of the teacher; this provides the student the freedom to remap latent noise, a behavior consistently observed in low-dimensional settings. Surprisingly, we find that in high-dimensional settings,...
Deep Embedded Multiplicative DMD for Algebra-Preserving Koopman Learning
Announce Type: new Abstract: Koopman theory turns nonlinear dynamics into a linear spectral problem. In computation, however, everything depends on a hard finite-dimensional choice: the observables must be expressive, nearly invariant under the dynamics, and, ideally, compatible with composition. Deep Koopman methods learn flexible coordinates, whereas structure-preserving methods enforce operator identities on fixed dictionaries.
Dad’s warning after son, 12, finally gets 'life-changing' Duchenne drug
Dad’s warning after son, 12, finally gets 'life-changing' Duchenne drug The drug givinostat - used for Duchenne Muscular Dystrophy management - remains unavailable to boys and young men who can no longer walk and this had plunged Alex Clarke's family into uncertainty A 12-year-old boy with a rare muscle-wasting condition has finally started a life-changing NHS treatment after years of uncertainty for his family. Ben Clarke has Duchenne Muscular Dystrophy (DMD), a severe, progressive genetic...
Optimizing Few-Step Generation with Adaptive Matching Distillation
arXiv:2602.07345v2 Announce Type: replace Abstract: Distribution Matching Distillation (DMD) is a powerful acceleration paradigm, yet its stability is often compromised in Forbidden Zone, regions where the real teacher provides unreliable guidance while the fake teacher exerts insufficient repulsive force. In this work, we propose a unified optimization framework that reinterprets prior art as implicit strategies to avoid these corrupted regions. Based on this insight, we introduce Adaptive...
Multiscale Decomposition Reveals Predictable Interannual Variability and Climate Trends in Antarctic Sea Ice Loss
Announce Type: replace Abstract: Antarctic sea ice has undergone unprecedented changes in recent years, raising questions about how this key geophysical system is responding to climate change. Decades of slow expansion were replaced by a precipitous decline in 2014-2017, a subsequent apparent recovery, and a renewed collapse from 2022 to the present. We diagnosed sea ice concentration (SIC) from satellite observations with a hierarchical decomposition method based on Dynamic Mode...
Vision-language Models for Driver Monitoring Systems: A Driver Activity Description Dataset
arXiv:2606.02273v1 Announce Type: new Abstract: Understanding subtle driver actions is essential for building reliable driver monitoring systems. Existing visionlanguage models (VLMs) are trained on general datasets and struggle to recognize fine distinctions in driver behaviors. This paper addresses this limitation by creating a detailed natural language version of the Drive&Act dataset.
Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation
Announce Type: replace Abstract: To achieve real-time interactive video generation, current methods distill pretrained bidirectional video diffusion models into few-step autoregressive (AR) models, facing an architectural gap when full attention is replaced by causal attention. However, existing approaches do not bridge this gap theoretically. They initialize the AR student via ODE distillation, which requires frame-level injectivity, where each noisy frame must map to a unique clean frame...
Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation
arXiv:2602.02214v4 Announce Type: replace Abstract: To achieve real-time interactive video generation, current methods distill pretrained bidirectional video diffusion models into few-step autoregressive (AR) models, facing an architectural gap when full attention is replaced by causal attention. However, existing approaches do not bridge this gap theoretically. They initialize the AR student via ODE distillation, which requires frame-level injectivity, where each noisy frame must map to a...
Reinforcing Few-step Generators via Reward-Tilted Distribution Matching
arXiv:2605.26108v3 Announce Type: replace Abstract: Recent advances in few-step diffusion distillation have enabled efficient image generation, yet aligning these models with human preferences remains challenging. We propose Reward-Tilted Distribution Matching Distillation (RTDMD), a two-stage framework that unifies distribution matching distillation with reward-guided reinforcement learning for few-step flow generators. We show that minimizing the KL divergence to a reward-tilted teacher...