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. …”

Decrease in loss during the training process. by Muhammad Idrees (92773)

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

Results from Ordinary Least Squares (OLS). by Muhummad Khairul Islam (21955544)

“…Besides, this method is also able to overcome the issues of heteroscedasticity and autocorrelation issues in the dataset. Additionally, we also employ the Ordinary Least Square (OLS) regression method to check the robustness of our estimation. …”

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)

Two-Dimensional Square Lattice of Colloidal Particles Formed by Electrostatic Adsorption in Confined Space by Yurina Aoyama (6919118)

“…In this study, we demonstrate a novel and efficient fabrication methodology for nonclose-packed, two-dimensional (2D) colloidal crystals exhibiting square lattice structures. …”

Two-Dimensional Square Lattice of Colloidal Particles Formed by Electrostatic Adsorption in Confined Space by Yurina Aoyama (6919118)

“…In this study, we demonstrate a novel and efficient fabrication methodology for nonclose-packed, two-dimensional (2D) colloidal crystals exhibiting square lattice structures. …”

Execution Steps of the EOF Algorithm. by Zezhi Lin (22521663)

“…The second mode (EOF2) exhibits negative anomalies at the Jiuzhaigou Valley and along the road to the southwest of the Valley, while positive anomalies appear along two roads, i.e., one extending from the Jiuzhaigou Valley to Shuanghe, and the other from the Chuanzhusi Town government square to western Chuanzhusi. The primary components of EOF2 reveal that, from midnight to 10:00, population increased along these two roads while decreasing over the Jiuzhaigou Valley and the road leading southward to the Chuanzhusi Town government square. …”

Flow Chart of the Study Steps. by Azam Hosseinpour (13434515)

Interaction plot with 95% confidence intervals generated from the GAM regression model 1 in Table 2 examining initial ED SBP and management group interactions. by Randeep S. Jawa (20489181)

GPT-4 results in decreased error rate for all tasks with increasing complexity when compared to GPT-3.5-turbo. by Jacqueline A. Jansen (14894276)

Endocardial and epicardial longitudinal strain and epicardial to endocardial longitudinal strain ratio at different gestational ages [in the square bracket the sample size]. Endoca... by Biagio Castaldi (20673392)

Analysis of errors of the AI model on accuracy and decision times for different tasks during the retrospective reader study. (a) by Kerol Djoumessi (21349992)

The introduction of mutualisms into assembled communities increases their connectance and complexity while decreasing their richness. by Gui Araujo (22170819)

“…<p>Using the invasion model, we investigate the effect of switching on and off (black vs grey) invasions with mutualisms halfway through the simulation (i.e. after 500 assembly events). …”

Feature engineering. by Youli Jiang (15249070)

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

“…This type of monitoring often relies on the use of navigation systems such as the Automatic Identification System (AIS). AIS data has been used to support the defense teams when identifying equipment defects, locating suspicious activity, ensuring ship collision avoidance, and detecting hazardous events. …”

Inherently interpretable deep learning framework highlights clinically relevant image regions. by Kerol Djoumessi (21349992)

Machine Learning Models for High Explosive Crystal Density and Performance by Jack V. Davis (9175514)

“…Notably, our model for crystal density surpassed the accuracy of all current models and decreased the root-mean square error (RMSE) of the previous best model by 20%. …”

S1 Data - by Jan Willem Koten (17743224)

“…Moreover, it is unclear how within-subject time course reliability limits the robust detection of connectivity on the group level. We estimated connectivity from a working memory task. …”