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🇺🇦 #NowPlaying on KEXP's #SeekAndDestroy
Divine Styler:
🎵 In a World of U
#DivineStyler
https://open.spotify.com/track/6nRrqtDGaifLbYoIejlSsV

Scalable Graph Indexing using GPUs for Approximate Nearest Neighbor Search
Zhonggen Li, Xiangyu Ke, Yifan Zhu, Bocheng Yu, Baihua Zheng, Yunjun Gao
https://arxiv.org/abs/2508.08744

Scalable Graph Indexing using GPUs for Approximate Nearest Neighbor Search
Approximate nearest neighbor search (ANNS) in high-dimensional vector spaces has a wide range of real-world applications. Numerous methods have been proposed to handle ANNS efficiently, while graph-based indexes have gained prominence due to their high accuracy and efficiency. However, the indexing overhead of graph-based indexes remains substantial. With exponential growth in data volume and increasing demands for dynamic index adjustments, this overhead continues to escalate, posing a critica…

Was speaking to some journalists recently who didn't realise how long a history the use of radar has in measuring ice thickness over glaciers.
The technique was discovered by pilots, who must have had the frankly terrifying experience of landing on the ice sheet with their radars telling them they still had a few thousand metres of descent to reach the surface...
It's not mentioned in this obituary but I wonder if this guy would have seen it? Perhaps he didn't even have radar to assist in landing?
https://flipboard.com/@newyorktimes/obituaries-bq1u22g8z/-/a-gd4LiLY4RbOMXavSDWOBQQ:a:3195393-/0
newyorktimes@flipboard.com - Conrad Shinn, First Pilot to Land at the South Pole, Dies at 102
https://www.nytimes.com/2025/06/08/obituaries/conrad-shinn-dead.html?utm_source=flipboard&utm_medium=activitypub
Posted into Obituaries @obituaries-newyorktimes

Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models
Jiaqi Cao, Jiarui Wang, Rubin Wei, Qipeng Guo, Kai Chen, Bowen Zhou, Zhouhan Lin
https://arxiv.org/abs/2508.09874

Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models
Large Language Models (LLMs) have shown strong abilities in general language tasks, yet adapting them to specific domains remains a challenge. Current method like Domain Adaptive Pretraining (DAPT) requires costly full-parameter training and suffers from catastrophic forgetting. Meanwhile, Retrieval-Augmented Generation (RAG) introduces substantial inference latency due to expensive nearest-neighbor searches and longer context. This paper introduces Memory Decoder, a plug-and-play pretrained me…

Luft: 20°C - Vann: 14.5°C
Nesten behagelig 😊
I dag med fjærkledd selskap!
#HydroStranda #Bamble #Norsktut

Inside the Stagnation Radius of the Nearest Billion-Solar-Mass Black Hole
J. M. Wrobel, D. W. Pesce, K. E. Nyland
https://arxiv.org/abs/2508.07042 https://…

Inside the Stagnation Radius of the Nearest Billion-Solar-Mass Black Hole
We used the NSF Jansky Very Large Array at a frequency $ν=$ 22\,GHz to study the nearest billion-solar-mass black hole, in the early-type galaxy NGC\,3115 at a distance of 9.7\,Mpc. We localize a faint continuum nucleus, with flux density $S_{\rm 22\,GHz} = 48.2\pm6.4\,μ$Jy, to a FWHM diameter $d_{\rm 22\,GHz} <$ 59\,mas (2.8\,pc). We find no evidence for adjacent emission within a stagnation region of radius $R_{\rm sta} \sim$ 360\,mas (17\,pc) identified in a recent hydrodynamic simulation …

Accelerating High-Dimensional Nearest Neighbor Search with Dynamic Query Preference
Yunjun Gao, Ruijie Zhao, Zhonggen Li, Baihua Zheng, Yifan Zhu, Zhaoqing Chen
https://arxiv.org/abs/2508.07218

Accelerating High-Dimensional Nearest Neighbor Search with Dynamic Query Preference
Approximate Nearest Neighbor Search (ANNS) is a crucial operation in databases and artificial intelligence. Current graph-based ANNS methods, such as HNSW and NSG, have shown remarkable performance but are designed under the assumption of a uniform query distribution. However, in practical scenarios, user preferences and query temporal dynamics lead to some queries being searched for more frequently than others. To fully utilize these characteristics, we propose DQF, a novel Dual-Index Query Fr…

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