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)

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)

Computational Procedure of the Particle Swarm Optimization (PSO) algorithm. by Meijun Shang (22806461)

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)

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

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

Depicts the schematic of the algorithm. by Dong Hyeok Choi (12562048)

Differences in computing performance between ENQUIRE’s gene normalization algorithm and GNorm2-Bioformer. by Luca Musella (19986312)

Workflow of anonymous collaboration process. by Zhaowei Hu (9234288)

The real NGD values, one-step ahead training set predictions, and multi-step ahead test set predictions using the AI-AFTER algorithm for Kayseri. by Burak Alakent (463512)

Heatmap illustrating which multi-scale feature pixels were classified as “cell” by query selection. This data was collected by processing the full test set for the DeepCell dataset... by Owen M. O’Connor (11965460)

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.…”

Heavy-load transfer steel platform. by Meijun Shang (22806461)

“…A particle swarm optimization (PSO) algorithm was integrated with the FEA model to optimize the cross-sectional dimensions of the primary beams, secondary beams, and foundation boxes, achieving a balance between load-bearing capacity and cost efficiency. …”

Traditional scaffolding reinforcement system. by Meijun Shang (22806461)

“…A particle swarm optimization (PSO) algorithm was integrated with the FEA model to optimize the cross-sectional dimensions of the primary beams, secondary beams, and foundation boxes, achieving a balance between load-bearing capacity and cost efficiency. …”

Iteration curve of the optimization process. by Meijun Shang (22806461)

“…A particle swarm optimization (PSO) algorithm was integrated with the FEA model to optimize the cross-sectional dimensions of the primary beams, secondary beams, and foundation boxes, achieving a balance between load-bearing capacity and cost efficiency. …”

Heatmap illustrating which multi-scale feature pixels were classified as “cell” by query selection. This data was collected by processing the full test set containing 29 movies fro... by Owen M. O’Connor (11965460)

AI-SSRF-SP (Artificial Intelligence - SSRF - Superfacility Platform) developed by the Big Data Science Center (BDSC) at Shanghai Synchrotron Radiation Facility (SSRF) by Alessandro Sepe (10304915)

Subjects:

(a) Geant4 modeling for calculating mass attenuation coefficients. by Dong Hyeok Choi (12562048)