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

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

Subjects:

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

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)

Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

“…Impolite prompting resulted in a significant decrease in disinformation production across all models (davinci-002, 59%; davinci-003, 44%; and gpt-3.5-turbo, 28%) and a slight reduction for gpt-4 (94%).…”

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

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

“…To elucidate descriptor importance, we evaluated interpretable descriptors in terms of importance and compared the accuracy of a statistics-driven machine learning model against a model comprised of descriptors typically assumed to control material density. …”

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

Subjects:

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 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. …”

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

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

Subjects:

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

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. …”

<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. …”

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

Subjects:

The reduced virulence of Δalta1 mutants is not primarily due to a decrease in growth. by Shuhuan Zhang (748003)

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

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

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

“…The Figure reveals two patterns: 1) the maximum level of innovation is higher in large cities (2.53) than in small cities (2.02); 2) among large cities in <b>a</b>, innovation levels in general decrease with nightlight density. …”