Comparison of Mean Absolute Error (MAE) in Millimeters as a Function of Kernel Size. by Liu Liu (512237)

Symbols and meanings. by Yan Wen (416285)

K-means results. by Fei Zhang (85787)

“…By employing K-means clustering on possession duration, we categorized possessions from 1,141 NBA games in the 2019–2020 season into high-frequency (HFS), low-frequency (LFS), and normal-frequency segments (NFS). …”

Descriptive (mean±SD) and inferential analysis (repeated measures). by Eduardo Abade (4183585)

Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

“…Introduction<p>The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. …”

Networks favouring more connected individuals results in a significant decrease to the likelihood of network recovery. by Adam Lee Miles (14123916)

Graphical representation of the optimization process in machine learning, focusing on the adjustment of weights during gradient descent. by Liu Liu (512237)

Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

General increases to network connectivity and payoff observed as prioritisation for private information is increased in networks, along with potential decreases to recovery ratios. by Adam Lee Miles (14123916)

The system dynamics model behind the behavioral game is displayed here where all players’ decisions at the beginning of each round of the game affect the common pool groundwater re... by Kat Demaree (16274130)

Feature importance result of SHAP. by Fei Zhang (85787)

“…By employing K-means clustering on possession duration, we categorized possessions from 1,141 NBA games in the 2019–2020 season into high-frequency (HFS), low-frequency (LFS), and normal-frequency segments (NFS). …”

Definition of variables. by Fei Zhang (85787)

“…By employing K-means clustering on possession duration, we categorized possessions from 1,141 NBA games in the 2019–2020 season into high-frequency (HFS), low-frequency (LFS), and normal-frequency segments (NFS). …”

Result of random forest. by Fei Zhang (85787)

“…By employing K-means clustering on possession duration, we categorized possessions from 1,141 NBA games in the 2019–2020 season into high-frequency (HFS), low-frequency (LFS), and normal-frequency segments (NFS). …”

Estimated marginal mean (± SE) age group differences in the average time on ball performed by professional soccer players on a match day for each position. by Emily Escreet (20552634)

Estimated marginal mean (± SE) age group differences in the relative frequency of releases per minute, performed by professional soccer players on a match day for each position. by Emily Escreet (20552634)

Estimated marginal mean (± SE) age group differences in the relative frequency of ball touches per minute, performed by professional soccer players on a match day for each position... by Emily Escreet (20552634)

Estimated marginal mean values representative of the average time on ball and the relative frequency of ball touches and releases per minute of match duration, performed by profess... by Emily Escreet (20552634)

Presentation 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Estimated marginal mean values representative of the inter-positional differences in average time on ball and the relative frequency of ball touches and releases per minute of matc... by Emily Escreet (20552634)