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

Cell-TRACTR detects cells in the first frame at time t 0. Object queries predicted to be a “cell” are converted to track queries. For Track Group Denoising (TGD), random noise is a... by Owen M. O’Connor (11965460)

Relevant computed image data. by Zhaoyu Ku (20660546)

City driving speed query results. by Meilin Zhu (688698)

Cells are initially detected in frame t₀ and then tracked to frame t₁. Object queries are processed in the first layer of the decoder for object detection. In... by Owen M. O’Connor (11965460)

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

Number of tweets collected per query and type. by Sylvia Iasulaitis (8301189)

Example illustrating how object and track queries interact using an attention map processed by Cell-TRACTR across two subsequent frames. The schematic displays the actual output al... by Owen M. O’Connor (11965460)

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)

Performance of ENQUIRE’s gene normalization algorithm. by Luca Musella (19986312)

Relevant Parameters. by Dongyan Wang (195482)

Operational mechanism diagram of deep reinforcement learning retrieval optimization module, based on the user interaction retrieval strategy optimization process, encodes the user query vector, historical interaction record and knowledge graph subgraph representation into state space, designs reward function, and trains the policy network through the proximal policy optimization algorithm (PPO) to optimize the retrieval strategy. by Yali Chen (24494)

“…<p>Operational mechanism diagram of deep reinforcement learning retrieval optimization module, based on the user interaction retrieval strategy optimization process, encodes the user query vector, historical interaction record and knowledge graph subgraph representation into state space, designs reward function, and trains the policy network through the proximal policy optimization algorithm (PPO) to optimize the retrieval strategy.…”

Example of a knowledge query. by Bei Li (90587)

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

Comparison of the EODA algorithm with existing algorithms in terms of recall. by Sunil Kumar (102321)

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Comparison of the EODA algorithm with existing algorithms in terms of precision. by Sunil Kumar (102321)

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Definitions of relevant SSA parameters. by Yeping Shi (14279118)