copy and paste this google map to your website or blog!
Press copy button and paste into your blog or website.
(Please switch to 'HTML' mode when posting into your blog. Examples: WordPress Example, Blogger Example)
Input Form:Deal with this potential dealer,buyer,seller,supplier,manufacturer,exporter,importer
(Any information to deal,buy, sell, quote for products or service)
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance . . . In molecular representation learning, mainstream GNN-based methods (Yi et al 2022; Fang et al 2022; Yu and Gao 2022; Chen et al 2024; Pei et al 2024) model the topolog-ical structure of molecules by representing atoms as nodes and bonds as edges Most approaches commonly employ the message-passing (MP) mechanism (Gilmer et al 2020), known as MP-GNNs, to aggregate and update representa
of Papers Presented to the American Mathematical Society Abstracts of Papers Presented to the American Mathematical Society (ISSN 0192-5857) is published four times a year by the American Mathematical Society at 201 Charles Street, Providence, RI 02904-2213 The subscription price for Volume 40 (2019) is US$203 list, US$162 40 institutional member, US$121 80 individual member Please add US$5 25 for delivery within the United States; US$12 for
Toward Adaptive Large Language Models Structured Pruning via . . . Abstract Structured pruning for large language models (LLMs) has garnered significant academic interest due to its ability to ef-ficiently compress and accelerate LLMs by eliminating re-dundant weight groups at a coarse-grained granularity Cur-rent structured pruning methods for LLMs typically depend on a singular granularity for assessing weight importance, re-sulting in notable performance
Eastern Shoshone Working Dictionary Compiled by David Leedom Shaul This is a working dictionary based of the published and publicly accessible sources for Eastern Shoshone
3D Deep Shape Descriptor - CVF Open Access The blue, green and red curves are results for the clean, moderately noisy and highly models respectively The results show DeepSD is tolerant to noise as the red and blue curves are slightly less convex than the Figure 7: Top: Precision-Recall plot shape retrieval perfor-mance on incomplete models Bottom: partial shape re-trieval