The relevant data file for this article. by Linsha Liu (20378293)

Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

The method for determining the chemical constituents of unknown materials in the algorithm. by Dong Hyeok Choi (12562048)

Research data for paper: Efficient Event-based Delay Learning in Spiking Neural Networks by Balázs Mészáros (16890225)

“…<p dir="ltr">The data in this repository accompanies the paper 'Efficient Event-based Delay Learning in Spiking Neural Networks'</p><p dir="ltr">The data relates to 4 benchmarks:</p><ol><li>Spiking Heidelberg Digits (SHD).…”

Relevant computed image data. by Zhaoyu Ku (20660546)

Data and code resources. by Sam Hall-McMaster (10343795)

Summary of relevant works. by Bashir Olaniyi Sadiq (22478011)

Scatter diagram of different principal elements. by Jizhong Wang (7441697)

The relevant code in the manuscript can be found in the supporting information data file. by Xini Fang (20861990)

Model-relevant variable data for external patients. by Lei Gao (6566)

Relevant data underlying the findings described in manuscript. by Lin Qi (128224)

Data generation algorithm. by Aluwani Ramalata (21262977)

Relevant Parameters. by Dongyan Wang (195482)

Calculation algorithm implementing the proposed model. by Landry Djopkop Kouanang (17131748)

Calculation algorithm implementing the proposed model. by Landry Djopkop Kouanang (17131748)

The parameter setting of the evolutionary neural network. by Wenbing Shi (5806160)

Subjects:

The genome coding scheme. by Wenbing Shi (5806160)

Subjects:

Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

Element model generation method with geometric distribution errors by Yiqi Liu (21357815)

“…The product surface geometric distribution error is directly attached to the element nodes of the product ideal element model using the error surface reconstruction method and the replacement algorithm of the element node vector height based on the product’s point cloud data. …”

Data Sheet 1_Algorithmic esthetics meets human intuition: the synergy of innovation and relevance shapes sustained attention.pdf by Wen Guan (5612195)