Tootfinder

The Stakeholder Engagement Division, the Infrastructure Security Division, and likely the Integrated Operations Division are believed to have been impacted.
Multiple CISA divisions targeted in shutdown layoffs, people familiar say
https://www.

Multiple CISA divisions targeted in shutdown layoffs, people familiar say
CISA units including its Stakeholder Engagement division are believed to have been hit. A DHS spokesperson said that the RIFs are meant to help get CISA “back on mission.”

Diffusion-DFL: Decision-focused Diffusion Models for Stochastic Optimization
Zihao Zhao, Christopher Yeh, Lingkai Kong, Kai Wang
https://arxiv.org/abs/2510.11590 https://…

Diffusion-DFL: Decision-focused Diffusion Models for Stochastic Optimization
Decision-focused learning (DFL) integrates predictive modeling and optimization by training predictors to optimize the downstream decision target rather than merely minimizing prediction error. To date, existing DFL methods typically rely on deterministic point predictions, which are often insufficient to capture the intrinsic stochasticity of real-world environments. To address this challenge, we propose the first diffusion-based DFL approach, which trains a diffusion model to represent the di…

AFC anarchy: Will all 4 divisions have new champs? Appraising the Broncos, Colts, Patriots and Steelers https://www.nfl.com/news/afc-anarchy-will-all-4-divisions-have-new-champs-appraising-the-broncos-colts-patriots-and-s…

Compute Only 16 Tokens in One Timestep: Accelerating Diffusion Transformers with Cluster-Driven Feature Caching
Zhixin Zheng, Xinyu Wang, Chang Zou, Shaobo Wang, Linfeng Zhang
https://arxiv.org/abs/2509.10312

Compute Only 16 Tokens in One Timestep: Accelerating Diffusion Transformers with Cluster-Driven Feature Caching
Diffusion transformers have gained significant attention in recent years for their ability to generate high-quality images and videos, yet still suffer from a huge computational cost due to their iterative denoising process. Recently, feature caching has been introduced to accelerate diffusion transformers by caching the feature computation in previous timesteps and reusing it in the following timesteps, which leverage the temporal similarity of diffusion models while ignoring the similarity in…

Diffusion-Link: Diffusion Probabilistic Model for Bridging the Audio-Text Modality Gap
KiHyun Nam, Jongmin Choi, Hyeongkeun Lee, Jungwoo Heo, Joon Son Chung
https://arxiv.org/abs/2510.11330

Diffusion-Link: Diffusion Probabilistic Model for Bridging the Audio-Text Modality Gap
Contrastive audio-language pretraining yields powerful joint representations, yet a persistent audio-text modality gap limits the benefits of coupling multimodal encoders with large language models (LLMs). We present Diffusion-Link, a diffusion-based modality-bridging module that generatively maps audio embeddings into the text-embedding distribution. The module is trained at the output embedding from the frozen multimodal encoder and implemented as a lightweight network with three residual MLP…

We cooked a new feature for Godot and iOS lovers. A plugin to Godot to deploy to Xogot on iOS or iPad:
Details:
https://blog.xogot.com/introducing-xogot-connect-remote-debugging-for-godot-on-iphone-and-ipad/

Introducing Xogot Connect — Remote Debugging for Godot on iPhone and iPad
Today we’re excited to announce the availability of Xogot Connect, a new Godot extension available in the Godot Asset Library. Xogot Connnect bridges your desktop Godot editor to Xogot on iOS enabling you to run and debug your games on iPhone or iPad over Wi-Fi. With Xogot Connect, you

DiffEM: Learning from Corrupted Data with Diffusion Models via Expectation Maximization
Danial Hosseintabar, Fan Chen, Giannis Daras, Antonio Torralba, Constantinos Daskalakis
https://arxiv.org/abs/2510.12691

DiffEM: Learning from Corrupted Data with Diffusion Models via Expectation Maximization
Diffusion models have emerged as powerful generative priors for high-dimensional inverse problems, yet learning them when only corrupted or noisy observations are available remains challenging. In this work, we propose a new method for training diffusion models with Expectation-Maximization (EM) from corrupted data. Our proposed method, DiffEM, utilizes conditional diffusion models to reconstruct clean data from observations in the E-step, and then uses the reconstructed data to refine the cond…

FlashVSR: Towards Real-Time Diffusion-Based Streaming Video Super-Resolution
Junhao Zhuang, Shi Guo, Xin Cai, Xiaohui Li, Yihao Liu, Chun Yuan, Tianfan Xue
https://arxiv.org/abs/2510.12747

FlashVSR: Towards Real-Time Diffusion-Based Streaming Video Super-Resolution
Diffusion models have recently advanced video restoration, but applying them to real-world video super-resolution (VSR) remains challenging due to high latency, prohibitive computation, and poor generalization to ultra-high resolutions. Our goal in this work is to make diffusion-based VSR practical by achieving efficiency, scalability, and real-time performance. To this end, we propose FlashVSR, the first diffusion-based one-step streaming framework towards real-time VSR. FlashVSR runs at appro…

Diffusion Transformers with Representation Autoencoders
Boyang Zheng, Nanye Ma, Shengbang Tong, Saining Xie
https://arxiv.org/abs/2510.11690 https://arxiv.…

Diffusion Transformers with Representation Autoencoders
Latent generative modeling, where a pretrained autoencoder maps pixels into a latent space for the diffusion process, has become the standard strategy for Diffusion Transformers (DiT); however, the autoencoder component has barely evolved. Most DiTs continue to rely on the original VAE encoder, which introduces several limitations: outdated backbones that compromise architectural simplicity, low-dimensional latent spaces that restrict information capacity, and weak representations that result f…

Realism Control One-step Diffusion for Real-World Image Super-Resolution
Zongliang Wu, Siming Zheng, Peng-Tao Jiang, Xin Yuan
https://arxiv.org/abs/2509.10122 https://

Realism Control One-step Diffusion for Real-World Image Super-Resolution
Pre-trained diffusion models have shown great potential in real-world image super-resolution (Real-ISR) tasks by enabling high-resolution reconstructions. While one-step diffusion (OSD) methods significantly improve efficiency compared to traditional multi-step approaches, they still have limitations in balancing fidelity and realism across diverse scenarios. Since the OSDs for SR are usually trained or distilled by a single timestep, they lack flexible control mechanisms to adaptively prioriti…