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)

“…Performance of ChatGPT-4.5 was compared against human raters on scoring record forms (i.e. word lists, numeric tables, and drawing responses). <b>Method:</b> Thirty-five deidentified BICAMS protocols, including the Symbol Digit Modalities Test (SDMT), California Verbal Learning Test–II (CVLT-II), and Brief Visuospatial Memory Test–Revised (BVMT-R), were independently scored by two trained human raters and ChatGPT-4.5. …”

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)

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

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

“…Retrieval-Augmented Generation (RAG) provides a framework to leverage available, trained LLMs combined with technical area-specific knowledge bases.</p>Methods<p>Herein, two methods are introduced (DaaDy: document as a dictionary and SQAD: structured question answer dictionary), which together enable effective implementation of LLM-RAG question-answering on large documents. …”

The flowchart of the large model for font style transfer. by Weijia Zhu (65481)

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Feature engineering. by Youli Jiang (15249070)

Deep learning-based locomotive syndrome prediction method. by Junichi Kushioka (12236447)

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Comparison of the results of our model and other methods. by Weijia Zhu (65481)

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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)

Comparison of our model with other state-of-the-art methods. by Weijia Zhu (65481)

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Few-Shot Symbol Detection in Engineering Drawings by Laura Jamieson (19759235)

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The most common machine learning methods to predict ν_s. by Fahd Saeed Alakbari (10701871)

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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)

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)

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

<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>" by Kira Shaw (18796168)

“…<p dir="ltr">The CSV file 'Eyreetal_DrainingVein_SourceData' contains the averaged time series traces and extracted metrics from individual experiments used across Figures 1-5 in the manuscript "Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins". The following acronyms included in the CSV file are defined as follows: Hbt is total hemoglobin, Art is artery region, DV is draining vein region, WV is whisker vein region, SEM is standard error mean, TS is time series, max peak is maximum peak, min peak is minima, AUC is area under the curve, WT is wild-type, AD is Alzheimer's disease, ATH is atherosclerosis and MIX is mixed AD/atherosclerosis. …”

Innovation and night light in large and small cities. by Saul Estrin (8629173)

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Geographical distribution of large cities and small cities. by Saul Estrin (8629173)

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