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. …”

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

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. …”

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

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

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

Assessment values of machine learning models. by Bin Pan (742525)

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

“…The inexpensive, yet highly accurate predictions from our models should enable creation of future artificial intelligence (AI) models that are able to screen large numbers (>10⁶) of compounds to find the highest performing compounds in terms of crystal density, detonation velocity and detonation pressure.…”

Data Sheet 1_Large language models for closed-library multi-document query, test generation, and evaluation.docx by Claire Randolph (19747105)

“…Knowledge tests need to be generated on new material and existing tests revised, tracking knowledge base updates. Large Language Models (LLMs) provide a framework for artificial intelligence-assisted knowledge acquisition and continued learning. …”

Comprehensive evaluation of machine-learning models in the training cohort. by Wentao Yang (205781)

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SHAP interpretation of the optimal machine learning model. by Wentao Yang (205781)

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Average mean decrease Gini across 100 partitions for the top 10 most important variables for binary mixed model and random forest. by Helal El-Zaatari (20442243)

Model and learning rule. by Janis Keck (21587252)

“…The right column has the same pre- and postsynaptic activities as the left column, only in reverse order. In <b>(C)</b>, the learning rule with parameters is used, while in <b>(D)</b> Only in the latter the synaptic weight changes are preserved (in reverse order), while in <b>(C)</b>, postsynaptic activity before presynaptic activity leads to a net weight decrease. …”

Feature engineering. by Youli Jiang (15249070)

Schematic of the model pruning. by Fan Yang (1413)

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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)

“…<b>(b)</b> Ophthalmologists’ decision time decreases overall when the deep learning model makes the correct decision. …”

Performance comparison of the machine learning models in the training cohort (n = 29,309). by Wentao Yang (205781)

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Loss of MAP4K4 expression causes a decrease in the number of neutrophils. by Guihua Wang (286390)

Flowchart for teaching and learning implementation of the 2D aesthetic program cluster in rural schools. by Zhankun Han (20529082)

The refracted opposition-based learning mechanism. by Yang Guo (399435)

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