Tootfinder

Grammarly rebrands as Superhuman after its acquisitions of Superhuman Mail in June and Coda in December 2024; the standalone writing tool Grammarly still exists (Stevie Bonifield/The Verge)
https://www.theverge.com/news/808472/grammarly-superhuman-ai-rebrand-rel…

Grammarly is changing its name to Superhuman
Grammarly is rebranding to become Superhuman, an AI agent-powered work platform that combines its writing tool with Coda and Superhuman Mail.

RAU: Reference-based Anatomical Understanding with Vision Language Models
Yiwei Li, Yikang Liu, Jiaqi Guo, Lin Zhao, Zheyuan Zhang, Xiao Chen, Boris Mailhe, Ankush Mukherjee, Terrence Chen, Shanhui Sun
https://arxiv.org/abs/2509.22404

RAU: Reference-based Anatomical Understanding with Vision Language Models
Anatomical understanding through deep learning is critical for automatic report generation, intra-operative navigation, and organ localization in medical imaging; however, its progress is constrained by the scarcity of expert-labeled data. A promising remedy is to leverage an annotated reference image to guide the interpretation of an unlabeled target. Although recent vision-language models (VLMs) exhibit non-trivial visual reasoning, their reference-based understanding and fine-grained localiz…

MSNBC President Rebecca Kutler says the news channel will officially rebrand from MSNBC to MS NOW on November 15, ahead of the planned spinoff into Versant (Alex Weprin/The Hollywood Reporter)
https://www.hollywoodreporter.com/tv/tv-news/msnbc-rebr…

“Same Mission. New Name”: MSNBC Switching to MS NOW Next Month
The rebrand of the cable news channel will take place Nov. 15, with a marketing effort launching next week.

Im Rahmen des Programms von QueerImpuls "Neue Perspektiven für die queere Community" findet in #Stuttgart am 1.11.26 ein queerfeministischer Schreibworkshop für FLINTA* statt. Disclaimer: Die Referentin ist mit mir verheiratet:
https://

Und wieder wird auf Wehrlose gekloppt:
https://ard.social/@tagesschau/115117900683369218

Classification of flag-transitive $2$-$(v, k, \lambda)$ designs with alternating group $A_n$($n \le 10$) as socle
Delu Tian, Qianfen Liao, Zhilin Zhang
https://arxiv.org/abs/2508.20716

Classification of flag-transitive $2$-$(v, k, λ)$ designs with alternating group $A_n$($n \le 10$) as socle
This paper is devoted to the classification of all flag-transitive point-primitive non-trivial $2$-$(v, k, λ)$ designs with the alternating group $A_n$($n \le 10$) as the socle of their automorphism groups, and 87 different designs are obtained up to isomorphism. The results of this study further improve the classification theory of designs under the action of almost simple groups, and provide reference for the follow-up study of similar problems.

Model-Free Dynamic Consensus in Multi-Agent Systems: A Q-Function Perspective
Maryam Babazadeh, Naim Bajcinca
https://arxiv.org/abs/2509.24598 https://arxi…

Model-Free Dynamic Consensus in Multi-Agent Systems: A Q-Function Perspective
This paper presents a new method for achieving dynamic consensus in linear discrete-time homogeneous multi-agent systems (MAS) with marginally stable or unstable dynamics. The guarantee of consensus in this setting involves a set of constraints based on the graph's spectral properties, complicating the design of the coupling gains. This challenge intensifies for large-scale systems with diverse graph Laplacian spectra. The proposed approach reformulates the dynamic consensus problem with a pres…

RefAM: Attention Magnets for Zero-Shot Referral Segmentation
Anna Kukleva, Enis Simsar, Alessio Tonioni, Muhammad Ferjad Naeem, Federico Tombari, Jan Eric Lenssen, Bernt Schiele
https://arxiv.org/abs/2509.22650

RefAM: Attention Magnets for Zero-Shot Referral Segmentation
Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models, often at the cost of additional training and architectural modifications. Meanwhile, large-scale generative diffusion models encode rich semantic information, making them attractive as general-purpose feature extractors. In this work, we introduce a new method that directly exploits features, attention scores, from diffusion transformers for downstr…

X-ray and neural network based in-situ identification of the melt pool during the additive manufacturing of a stainless steel part
Loic Jegou, Valerie Kaftandjian, Thomas Elguedj, Mohamed Tahraoui, Philippe Duvauchelle, Mady Guillemot
https://arxiv.org/abs/2509.22409

X-ray and neural network based in-situ identification of the melt pool during the additive manufacturing of a stainless steel part
Laser Metal Deposition with Powder (LMDp) is an additive manufacturing technique used for repairing metal components or producing parts with intricate geometries. However, a comprehensive understanding of the melt pool dynamics, which significantly influences the final properties of LMDp-fabricated parts, remains limited. Non-destructive testing is highly valuable for conducting in-situ controls during manufacturing. X-ray imaging offers the ability to penetrate metallic parts and detect defect…

An Anisotropic Cross-View Texture Transfer with Multi-Reference Non-Local Attention for CT Slice Interpolation
Kwang-Hyun Uhm, Hyunjun Cho, Sung-Hoo Hong, Seung-Won Jung
https://arxiv.org/abs/2509.20242

An Anisotropic Cross-View Texture Transfer with Multi-Reference Non-Local Attention for CT Slice Interpolation
Computed tomography (CT) is one of the most widely used non-invasive imaging modalities for medical diagnosis. In clinical practice, CT images are usually acquired with large slice thicknesses due to the high cost of memory storage and operation time, resulting in an anisotropic CT volume with much lower inter-slice resolution than in-plane resolution. Since such inconsistent resolution may lead to difficulties in disease diagnosis, deep learning-based volumetric super-resolution methods have b…