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. …”

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)

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

“…<p><b>Objective:</b> To assess the feasibility, accuracy, and reliability of using ChatGPT-4.5 (early-access), a large language model (LLM), for automated scoring of Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) protocols. …”

The update step size of the attractor set in different regions. by Long Di (9977453)

Female regression coefficients for regression #3 across development in set size = 2 and set size = 4. by Juliana Chase (20469427)

“…All statistics are done identically and are reported in the text. There was a slight decrease in the regression coefficient weight of intercept for set size = 2 (A), but not set size = 4 (F). …”

Effect of session on winning model parameters for set size = 2 and set size = 4 for both male and females. by Juliana Chase (20469427)

“…<p>To test if mice adjusted one-back strategies with experience, we next compared how parameter weights (y-axis) changed across the 6 sessions analyzed for each mouse (x-axis). Since set size = 2 and set size = 4 days were interspersed, we combined set size data and analyzed sessions chronologically. …”

Data Set. by Yang Li (7082)

Parameter sets generated through Latin hypercube sampling. by Alexandra M. Campbell (3284094)

Strategy parameters across development for female mice in set size = 2 and set size = 4 from winning computational model. by Juliana Chase (20469427)

“…However, female mice had a significant decrease in parameter S1 “Inappropriate Lose-Shift” in set size = 4 (H: <i>p</i> = 0.04) with a trend in a similar direction in set size = 2 (C: <i>p</i> = 0.08).…”

Strategy parameters across development for male mice in set size = 2 and set size = 4 from winning computational model. by Juliana Chase (20469427)

“…RL parameter <i>α</i>₊ learning rate and decision noise parameter softmax <i>β</i> were stable across development in both set size = 2 (A-B) and set size = 4 (F-G). Both parameters S1 “Inappropriate Lose-Shift” and S3 “Inappropriate Lose-Stay” decreased significantly with age in set size = 2 (C: <i>p</i> = 0.02, E: <i>p</i> = 0.04), but not in set size = 4 (H,J). …”

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)

Penalty conversions yield large practical decreases, but robustness and generalizability of result are limited by data quality and sample selection. by Ryan J. Treves (13527735)

The PRAME-AS knockout cells showed a decrease in migration, proliferation, stemness, and viability. by Zahra Hosseininia (22265113)

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)

Setting up for VFSS image. by Rie Asayama (693361)

Table 1_Effect of decreased suspended sediment content on chlorophyll-a in Dongting Lake, China.docx by Le Zhang (88249)

“…The reduction in SSC may influence chlorophyll-a (Chl-a) concentrations in water, thereby further affecting the aquatic ecological environment. …”

Data from: Colony losses of stingless bees increase in agricultural areas, but decrease in forested areas by Malena Sibaja Leyton (18400983)

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

“…This research proposes a new RNN architecture that decreases the prediction error up to 50% for cargo vessels when compared to the OU model. …”

Distribution of final population size (A) and mean change in mean MSI over time (B) over 100 simulations for each parameter set shown in Table 3 in the absence of weevils (left) an... by Alexandra M. Campbell (3284094)