Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx حسب 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? حسب 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. حسب Jacqueline A. Jansen (14894276)

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

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

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Data Sheet 1_Large language models for closed-library multi-document query, test generation, and evaluation.docx حسب 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. …"

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

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

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

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

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

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

الموضوعات:

Feature engineering. حسب Youli Jiang (15249070)

The most common machine learning methods to predict ν_s. حسب 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. حسب 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) حسب Kerol Djoumessi (21349992)

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

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

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<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>" حسب 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. حسب Saul Estrin (8629173)

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