fileCont prompt strategy results in decreased error rate for some tasks with increasing complexity. by Jacqueline A. Jansen (14894276)

Tissue, days post-infection (dpi) and the top 10 most significant genes with increased and decreased expression with valid gene symbols for the response contrasts. by Gillian P. McHugo (8965919)

Loss of ECRG4 expression decreases neutrophil mobilization from bone marrow reserves. by Katie D. Pool (20141584)

Clinical diagnostic efficacy comparison of the proposed hybrid model and benchmark machine learning models. Metrics are reported as point estimates with 95% CIs. This table offers a discussion of the measures of high interest in clinical implementation. Sensitivity (true positive rate) reflects the ability to correctly identify patients with CVD, while Specificity (true negative rate) indicates the ability to correctly rule out patients without CVD. The Negative Likelihood Ratio quantifies how much the odds of having the disease decrease with a negative test result, where smaller values indicate stronger diagnostic power.... by Medhat A. Tawfeek (22522087)

“…The Negative Likelihood Ratio quantifies how much the odds of having the disease decrease with a negative test result, where smaller values indicate stronger diagnostic power. …”

Scheme of g-λ model with larger values λ. by Zhanfeng Fan (20390992)

“…<div><p>This paper theoretically explores the propagation attenuation of normally incident P-waves on a single uncoupled joint exhibiting nonlinear deformation behavior. …”

ECoG timescales decrease during spatial attention. by Isabel Raposo (21615517)

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

“…Additionally, the model’s generalization ability is verified through transfer learning. Although the performance of the StackBoost model decreases when applied to different datasets, it still shows considerable transferability, making it a more generalizable prediction model for aqueous solubility.…”

Expression of lipid biosynthetic genes is decreased with polyamine depletion. by Yazmin E. Cruz-Pulido (20285238)

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

“…<p>Recently, there has been significant interest in digitizing engineering drawings due to their complexity and practical benefits. Symbol digitization, a critical aspect in this field, is challenging as utilizing Deep Learning-based methods to recognize symbols of interest requires a large number of training instances for each class of symbols. …”

Lens profiles as Ez (orange lines) force was increased and Az and Pz (green lines) forces were decreased. by Ronald A. Schachar (16674576)

Daily number of adverse outcomes (bars) and area under the receiver operating characteristic curve per day of the PIERS-ML model. by Guiyou Yang (10259597)

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML by Ayush Garg (21090944)

Subjects:

Daily number of adverse outcomes (bars) and area under the precision-recall curve (AUC-PRC) per day of the PIERS-ML model. by Guiyou Yang (10259597)

Group-level narrow- and broad-band spectral changes after hemispherotomy reveal a marked EEG slowing of the isolated cortex, robust across patients. by Michele Angelo Colombo (22446342)

“…This decrease was larger in the disconnected than in the contralateral cortex. …”

The motor torque curves of load increase/decrease (A) the load of 10 N • m (B) the load of 20 N • m. by Taochang Li (20642935)

The motor speed curves of load increase/decrease (A) the load of 10 N • m (B) the load of 20 N • m. by Taochang Li (20642935)

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)

Ether Cleavage Decreases the Ion Exchange Capacity of Anion Exchange Membranes by Shakkira Erimban (6882539)

Ether Cleavage Decreases the Ion Exchange Capacity of Anion Exchange Membranes by Shakkira Erimban (6882539)

Table 1_WGCNA-ML-MR integration: uncovering immune-related genes in prostate cancer.docx by Jing Lv (128179)

“…The intersection of differentially expressed genes and driver genes was taken, and Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were performed. Furthermore, a machine learning algorithm was used to screen for core genes and construct a diagnostic model, which was then validated in an external validation dataset. …”