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

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

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

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. …”

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

“…Notably, our model for crystal density surpassed the accuracy of all current models and decreased the root-mean square error (RMSE) of the previous best model by 20%. …”

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 impact of different diffusion model depths on the model. by Weijia Zhu (65481)

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The impact of different model compression modules on the model. by Weijia Zhu (65481)

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The impact of different diffusion model time steps on the model. by Weijia Zhu (65481)

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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)

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

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. …”

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

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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>" 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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Depth pruning can effectively reduce the complexity of the model. by Weijia Zhu (65481)

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