Tootfinder

Fox News' Ben Domenech tells Ilhan Omar to buy a one-way ticket to Somalia (Media Matters for America)
https://www.mediamatters.org/fox-news/fox-news-ben-domenech-tells-ilhan-omar-buy-one-way-ticket-somalia
http://www.memeorandum.com/250618/p3#a250618p3

Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
Hyeon Jeon, Hyunwook Lee, Yun-Hsin Kuo, Taehyun Yang, Daniel Archambault, Sungahn Ko, Takanori Fujiwara, Kwan-Liu Ma, Jinwook Seo
https://arxiv.org/abs/2506.14820

Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
Visual analytics using dimensionality reduction (DR) can easily be unreliable for various reasons, e.g., inherent distortions in representing the original data. The literature has thus proposed a wide range of methodologies to make DR-based visual analytics reliable. However, the diversity and extensiveness of the literature can leave novice analysts and researchers uncertain about where to begin and proceed. To address this problem, we propose a guide for reading papers for reliable visual ana…

DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification
Matt Poyser, Toby P. Breckon
https://arxiv.org/abs/2506.14667

DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification
In order to address the scalability challenge within Neural Architecture Search (NAS), we speed up NAS training via dynamic hard example mining within a curriculum learning framework. By utilizing an autoencoder that enforces an image similarity embedding in latent space, we construct an efficient kd-tree structure to order images by furthest neighbour dissimilarity in a low-dimensional embedding. From a given query image from our subsample dataset, we can identify the most dissimilar image wit…

Resource Optimization with MPI Process Malleability for Dynamic Workloads in HPC Clusters
Sergio Iserte, Iker Mart\'in-\'Alvarez, Krzysztof Rojek, Jos\'e I. Aliaga, Maribel Castillo, Weronika Folwarska, Antonio J. Pe\~na
https://arxiv.org/abs/2506.14743

Resource Optimization with MPI Process Malleability for Dynamic Workloads in HPC Clusters
Dynamic resource management is essential for optimizing computational efficiency in modern high-performance computing (HPC) environments, particularly as systems scale. While research has demonstrated the benefits of malleability in resource management systems (RMS), the adoption of such techniques in production environments remains limited due to challenges in standardization, interoperability, and usability. Addressing these gaps, this paper extends our prior work on the Dynamic Management of…

Extracting transient Koopman modes from short-term weather simulations with sparsity-promoting dynamic mode decomposition
Zhicheng Zhang, Yoshihiko Susuki, Atsushi Okazaki
https://arxiv.org/abs/2506.14083

Extracting transient Koopman modes from short-term weather simulations with sparsity-promoting dynamic mode decomposition
Convective features-here represented as warm bubble-like patterns-reveal essential, high-level information about how short-term weather dynamics evolve within a high-dimensional state space. We introduce a data-driven framework that uncovers transient dynamics captured by Koopman modes responsible for these structures and traces their emergence, growth, and decay. Our approach incorporates the sparsity-promoting dynamic mode decomposition into the framework of Koopman mode decomposition, yieldi…

Particle-Grid Neural Dynamics for Learning Deformable Object Models from RGB-D Videos
Kaifeng Zhang, Baoyu Li, Kris Hauser, Yunzhu Li
https://arxiv.org/abs/2506.15680

Particle-Grid Neural Dynamics for Learning Deformable Object Models from RGB-D Videos
Modeling the dynamics of deformable objects is challenging due to their diverse physical properties and the difficulty of estimating states from limited visual information. We address these challenges with a neural dynamics framework that combines object particles and spatial grids in a hybrid representation. Our particle-grid model captures global shape and motion information while predicting dense particle movements, enabling the modeling of objects with varied shapes and materials. Particles…

🇺🇦 #NowPlaying on KEXP's #DriveTime
Des Demonas:
🎵 The Duke Ellington Bridge
#DesDemonas
https://des-demonas.bandcamp.com/track/the-duke-elington-bridge
https://open.spotify.com/track/5E1F4ZhdWKkIrPcEGLVYF7

An Exact and Efficient Sampler for Dynamic Discrete Distributions
Lilith Orion Hafner, Adriano Meligrana
https://arxiv.org/abs/2506.14062 https://

An Exact and Efficient Sampler for Dynamic Discrete Distributions
Sampling from a dynamic discrete distribution involves sampling from a dynamic set of weighted elements, where elements can be added or removed at any stage of the sampling process. Although efficient for static sets, the Alias method becomes impractical in dynamic settings due to the need to reconstruct the sampler after each update, which incurs a computational cost proportional to the size of the distribution, making it unsuitable for applications requiring frequent insertions, deletions, or…

Appeals panel temporarily lifts order deeming Trump firing of federal employee labor board head unlawful (Ella Lee/The Hill)
https://thehill.com/regulation/court-battles/5358101-appeals-panel-temporarily-lifts-order-deeming-trump-firing-of-federal-employee-labor-board-head-unlawful/
http://www.memeorandum.com/250618/p154#a250618p154

DynaGuide: Steering Diffusion Polices with Active Dynamic Guidance
Maximilian Du, Shuran Song
https://arxiv.org/abs/2506.13922 https://

DynaGuide: Steering Diffusion Polices with Active Dynamic Guidance
Deploying large, complex policies in the real world requires the ability to steer them to fit the needs of a situation. Most common steering approaches, like goal-conditioning, require training the robot policy with a distribution of test-time objectives in mind. To overcome this limitation, we present DynaGuide, a steering method for diffusion policies using guidance from an external dynamics model during the diffusion denoising process. DynaGuide separates the dynamics model from the base pol…