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)

Kolmogorov-Smirnov (KS) test for the different parameter value pairs at a 95% confidence level. We tested the null hypothesis for all parameters: The RMSEs obtained for one pair of... by Cyrine Chenaoui (22177098)

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

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. …”

This is the data set that we used to reach the conclusions drawn in the manuscript with related metadata and methods. by Farayi Kaseke (19560197)

AI KRegression by CHIN-CHENG CHU (20718575)

All TPM values. by Hyuk Sung Yoon (20208672)

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)

Characteristic size values. by Junxia Zhou (14381598)

Predicting pattern diversity decreases as a function of and . by Selim Haj Ali (21222613)

“…(a) and (b): Average negative predictive value for and , respectively for and . (b) and (d): Average proportion of cases predicted as pattern diversity decreases for and , respectively for and .…”

Paeameter ranges and optimal values. by Zhen Zhao (159931)

Values for all quantifications. by Sravya Paluri (5475260)

Comparison of different P-values. by Mengyao Zhou (9266132)

Estimated mean values for light interception. by Torsten Schober (20485754)

Salience of authority-vice phrases by year. by Daniel Hart (440152)

“…Similarly, we anticipated that the salience of phrases consistent with fidelity to members of one’s social group (“allegiance”, “one for all”, <i>loyalty-virtue</i>), would decrease, and those suggestive of social corrosion (“betrays”, “back stabs”, <i>loyalty-vice</i>) would increase. …”

Salience of fear words by year by country. by Daniel Hart (440152)

“…Similarly, we anticipated that the salience of phrases consistent with fidelity to members of one’s social group (“allegiance”, “one for all”, <i>loyalty-virtue</i>), would decrease, and those suggestive of social corrosion (“betrays”, “back stabs”, <i>loyalty-vice</i>) would increase. …”

Salience of authority-virtue phrases by year. by Daniel Hart (440152)

“…Similarly, we anticipated that the salience of phrases consistent with fidelity to members of one’s social group (“allegiance”, “one for all”, <i>loyalty-virtue</i>), would decrease, and those suggestive of social corrosion (“betrays”, “back stabs”, <i>loyalty-vice</i>) would increase. …”

Salience of loyalty-virtue phrases by year. by Daniel Hart (440152)

“…Similarly, we anticipated that the salience of phrases consistent with fidelity to members of one’s social group (“allegiance”, “one for all”, <i>loyalty-virtue</i>), would decrease, and those suggestive of social corrosion (“betrays”, “back stabs”, <i>loyalty-vice</i>) would increase. …”