Data is generated from ARMA (p, q) models with (p, q) , and the corresponding AIC table is created. by Jesse Wheeler (18462457)

Spectrograms extracted from the generalized S-transform corresponding to the adjustment parameters λ_<i>G</i> = 0.05, λ_<i>L</i> = 0.01. by Li Jiashen (20552690)

Type-1 membership function for distance. by Seung-Min Ryu (21463891)

“…<div><p>In this study, we present an algorithm to estimate the distance between a vehicle and a target object using light from headlights captured by a camera. …”

Type-1 membership function for speed. by Seung-Min Ryu (21463891)

“…<div><p>In this study, we present an algorithm to estimate the distance between a vehicle and a target object using light from headlights captured by a camera. …”

Supplementary Tables from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

The convergence curves of the test functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

Single-peaked reference functions. by Ruiyu Zhan (21602031)

“…Utilizing the diabetes dataset from 130 U.S. hospitals, the LGWO-BP algorithm achieved a precision rate of 0.97, a sensitivity of 1.00, a correct classification rate of 0.99, a harmonic mean of precision and recall (F1-score) of 0.98, and an area under the ROC curve (AUC) of 1.00. …”

IDRAS algorithm outline. by Ron Teichner (21492434)

Cross-validated estimate of MSE error for the LASSO, Ridge and Elastic Net penalties (from top to bottom respectively) as a function of <i>log</i>λ. by Omid Chatrabgoun (17960015)

Cross-validated estimate of MSE error for the LASSO, Ridge and Elastic Net penalties (from top to bottom respectively) as a function of <i>log</i>λ. by Omid Chatrabgoun (17960015)

Heatmap results from the global sensitivity analysis for the generation of villous vessels within a placentone. by Diana C. de Oliveira (9220729)

Supplementary Figure S2 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

Supplementary Figure S7 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

Supplementary Figure S6 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

Supplementary Figure S5 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

Supplementary Figure S8 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

Supplementary Figure S1 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

Supplementary Figure S4 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

Supplementary Figure S9 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

Supplementary Figure S3 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)