Tootfinder

So far the 9th #Starship flight test is going close to nominal - the SuperHeavy booster was lost a bit early before its planned ditching, the Starship reach its suborbital trajectory without problems.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning
Chuxin Wang, Yixin Zha, Wenfei Yang, Tianzhu Zhang
https://arxiv.org/abs/2506.21541

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning
Recently, Mamba-based methods have demonstrated impressive performance in point cloud representation learning by leveraging State Space Model (SSM) with the efficient context modeling ability and linear complexity. However, these methods still face two key issues that limit the potential of SSM: Destroying the adjacency of 3D points during SSM processing and failing to retain long-sequence memory as the input length increases in downstream tasks. To address these issues, we propose StruMamba3D,…

Went down this morning to the Nevada County board of supervisors meeting to support our first county Pride Month proclamation.

Control and optimization for Neural Partial Differential Equations in Supervised Learning
Alain Bensoussan, Minh-Binh Tran, Bangjie Wang
https://arxiv.org/abs/2506.20764

Control and optimization for Neural Partial Differential Equations in Supervised Learning
Although there is a substantial body of literature on control and optimization problems for parabolic and hyperbolic systems, the specific problem of controlling and optimizing the coefficients of the associated operators within such systems has not yet been thoroughly explored. In this work, we aim to initiate a line of research in control theory focused on optimizing and controlling the coefficients of these operators-a problem that naturally arises in the context of neural networks and super…

Unlocking my own understanding of and ability to build #Swift macros feels like a superpower.
…something something great responsibility, though.
Synthesizing boilerplate and statically-verifiable elements like custom function calls based on macro input… is magic—the good kind.
`@GET("/logs/{userId}/{timing}")`
↘️

#QBism say they took the name to be distinct from other Bayesians, and the pun on #Cubism reflected its leap out of the singular renaissance perspective, whereas legends say cubism's superpositions had inspired the very quantum mechanics Fuchs et al hopes to replace 😅
‘QBism’: quantum mechanics is not a description of objective reality – it reveals a world of genuine free will
https://theconversation.com/qbism-quantum-mechanics-is-not-a-description-of-objective-reality-it-reveals-a-world-of-genuine-free-will-200487

Pixel 10: Google verschiebt Veranstaltung für Superfans
25 Superfans hätten Ende Juni ein neues Pixel-Gerät sehen dürfen. Die Verschiebung auf September verunsichert die Pixel-Gemeinde.
https://www.

DeOcc-1-to-3: 3D De-Occlusion from a Single Image via Self-Supervised Multi-View Diffusion
Yansong Qu, Shaohui Dai, Xinyang Li, Yuze Wang, You Shen, Liujuan Cao, Rongrong Ji
https://arxiv.org/abs/2506.21544

DeOcc-1-to-3: 3D De-Occlusion from a Single Image via Self-Supervised Multi-View Diffusion
Reconstructing 3D objects from a single image is a long-standing challenge, especially under real-world occlusions. While recent diffusion-based view synthesis models can generate consistent novel views from a single RGB image, they generally assume fully visible inputs and fail when parts of the object are occluded. This leads to inconsistent views and degraded 3D reconstruction quality. To overcome this limitation, we propose an end-to-end framework for occlusion-aware multi-view generation. …

TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness
Pritam Mishra, Coloma Ballester, Dimosthenis Karatzas
https://arxiv.org/abs/2506.20588

TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness
The increasing ubiquity of video content and the corresponding demand for efficient access to meaningful information have elevated video summarization and video highlights as a vital research area. However, many state-of-the-art methods depend heavily either on supervised annotations or on attention-based models, which are computationally expensive and brittle in the face of distribution shifts that hinder cross-domain applicability across datasets. We introduce a pioneering self-supervised vid…

AdvMIM: Adversarial Masked Image Modeling for Semi-Supervised Medical Image Segmentation
Lei Zhu, Jun Zhou, Rick Siow Mong Goh, Yong Liu
https://arxiv.org/abs/2506.20563

AdvMIM: Adversarial Masked Image Modeling for Semi-Supervised Medical Image Segmentation
Vision Transformer has recently gained tremendous popularity in medical image segmentation task due to its superior capability in capturing long-range dependencies. However, transformer requires a large amount of labeled data to be effective, which hinders its applicability in annotation scarce semi-supervised learning scenario where only limited labeled data is available. State-of-the-art semi-supervised learning methods propose combinatorial CNN-Transformer learning to cross teach a transform…