Tootfinder

Can anyone beat Cuomo? And is it Zohran? (Joel Wertheimer/Silver Bulletin)
https://www.natesilver.net/p/can-anyone-beat-cuomo-and-is-it-zohran
http://www.memeorandum.com/250610/p95#a250610p95

Nachdem es bei den Tauben mit dem Nachwuchs nicht geklappt hat, turteln sie wieder
Von meinem Fenster aus beobachte ich, wenn sich etwas anbahnt: Das sieht nämlich so aus, dass der Täuberich Moves macht, die weibl. Taube sich aber „ziert“. So tut, als wenn sie gar nicht wollte – bevor sie es dann doch zulässt
Ich bin davon ausgegangen, dass dieses Sich-Zieren uns Menschen (Frauen) anerzogen wurde und dass es auf moralische Vorstellungen zurückgeht
Scheint hiermit widerlegt …

Accelerating 3D Gaussian Splatting with Neural Sorting and Axis-Oriented Rasterization
Zhican Wang, Guanghui He, Dantong Liu, Lingjun Gao, Shell Xu Hu, Chen Zhang, Zhuoran Song, Nicholas Lane, Wayne Luk, Hongxiang Fan
https://arxiv.org/abs/2506.07069

Accelerating 3D Gaussian Splatting with Neural Sorting and Axis-Oriented Rasterization
3D Gaussian Splatting (3DGS) has recently gained significant attention for high-quality and efficient view synthesis, making it widely adopted in fields such as AR/VR, robotics, and autonomous driving. Despite its impressive algorithmic performance, real-time rendering on resource-constrained devices remains a major challenge due to tight power and area budgets. This paper presents an architecture-algorithm co-design to address these inefficiencies. First, we reveal substantial redundancy cause…

Kilobyte-Scale, Selector-Free, Temperature-Hard AlScN Ferroelectric Diode Crossbar Arrays
Zirun Han, Chao-Chuan Chen, Dhiren K. Pradhan, David C. Moore, Ravali Gudavalli, Xindi Yang, Kwan-Ho Kim, Hyunmin Cho, Zachary Anderson, Spencer Ware, Harsh Yellai, W. Joshua Kennedy, Nicholas R. Glavin, Roy H. Olsson III, Deep Jariwala
https://

Kilobyte-Scale, Selector-Free, Temperature-Hard AlScN Ferroelectric Diode Crossbar Arrays
We report the fabrication and characterization of kilobyte-scale, selector-free, ferroelectric (FE) diode crossbar memory arrays based on aluminum scandium nitride (AlScN). Utilizing a fully CMOS back-end-of-line (BEOL) compatible process, we fabricated 2-kilobyte (128 $\times$ 128) arrays with device diameters down to 5 $μ$m, achieving memory densities up to 2500 bits/mm$^2$. Large-scale electrical characterization across 1000 randomly selected devices reveals a yield rate of 95.2%, a tight s…

Small Models, Big Support: A Local LLM Framework for Teacher-Centric Content Creation and Assessment using RAG and CAG
Zarreen Reza, Alexander Mazur, Michael T. Dugdale, Robin Ray-Chaudhuri
https://arxiv.org/abs/2506.05925

Small Models, Big Support: A Local LLM Framework for Teacher-Centric Content Creation and Assessment using RAG and CAG
While Large Language Models (LLMs) are increasingly utilized as student-facing educational aids, their potential to directly support educators, particularly through locally deployable and customizable open-source solutions, remains significantly underexplored. Many existing educational solutions rely on cloud-based infrastructure or proprietary tools, which are costly and may raise privacy concerns. Regulated industries with limited budgets require affordable, self-hosted solutions. We introduc…

Trump se pohšdal s Muskem.😆
Doufšm, že "rozvod" bude hodně bolestivý, nepříjemný a "krštký" jako 30 letš všlka!😈
Kdyby měl někdo zšjem, odemčený člšnek na DeníkuN.

FlashMLA-ETAP: Efficient Transpose Attention Pipeline for Accelerating MLA Inference on NVIDIA H20 GPUs
Pencuo Zeren, Qiuming Luo, Rui Mao, Chang Kong
https://arxiv.org/abs/2506.01969

FlashMLA-ETAP: Efficient Transpose Attention Pipeline for Accelerating MLA Inference on NVIDIA H20 GPUs
Efficient inference of Multi-Head Latent Attention (MLA) is challenged by deploying the DeepSeek-R1 671B model on a single Multi-GPU server. This paper introduces FlashMLA-ETAP, a novel framework that enhances MLA inference for the single-instance deployment scenario on NVIDIA H20 GPUs. We propose the Efficient Transpose Attention Pipeline (ETAP), which reconfigures attention computation through transposition to align the KV context length with the \(M\)-dimension in WGMMA operations, significa…

Feel the Force: Contact-Driven Learning from Humans
Ademi Adeniji, Zhuoran Chen, Vincent Liu, Venkatesh Pattabiraman, Raunaq Bhirangi, Siddhant Haldar, Pieter Abbeel, Lerrel Pinto
https://arxiv.org/abs/2506.01944

Feel the Force: Contact-Driven Learning from Humans
Controlling fine-grained forces during manipulation remains a core challenge in robotics. While robot policies learned from robot-collected data or simulation show promise, they struggle to generalize across the diverse range of real-world interactions. Learning directly from humans offers a scalable solution, enabling demonstrators to perform skills in their natural embodiment and in everyday environments. However, visual demonstrations alone lack the information needed to infer precise contac…

woo! #FuckAndrewCuomo

woo! #FuckAndrewCuomo