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. …”

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

A novel RNN architecture to improve the precision of ship trajectory predictions by Martha Dais Ferreira (18704596)

“…To solve these challenges, Recurrent Neural Network (RNN) models have been applied to STP to allow scalability for large data sets and to capture larger regions or anomalous vessels behavior. …”

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)

AI KRegression by CHIN-CHENG CHU (20718575)

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)

Tree cover limits occupancy of a declining game bird by Bradley Kubecka (7387181)

“…Probability of bobwhite occupancy decreased as canopy cover increased (β<em>_Tree</em> = -0.74, 95% CrI: -1.29 – -0.28); occupancy was over 19 times higher when canopy cover was 44% versus the mean observed value of 80.8% (range: 38–96%). …”

Impact of changes in on pharmaceutical pricing and profits. by Yan Wen (416285)