Multivariate Temporal Point Process Regression by Xiwei Tang (807045)

“…We develop a highly scalable optimization algorithm for parameter estimation. We derive the large sample error bound for the recovered coefficient tensor, and establish the subgroup identification consistency, while allowing the dimension of the multivariate point process to diverge. …”

Bias-variance decomposition of the Mean Squared Error (MSE). by Konstantina Charmpi (9901053)

Big Data Model Building Using Dimension Reduction and Sample Selection by Lih-Yuan Deng (17081779)

“…Furthermore, such subdata cannot be useful to build alternative models because it is not an appropriate representative sample of the full data. In this article, we propose a novel algorithm for better model building and prediction via a process of selecting a “good” training sample. …”

Reproduction of biological regrowth of severed class IV <i>Drosophila</i> neurons. by Moritz Groden (18018276)

Phase estimates for users of <i>Entrain</i>. by Samuel Christensen (9922554)

Data_Sheet_1_Investigating Bayesian optimization for expensive-to-evaluate black box functions: Application in fluid dynamics.pdf by Mike Diessner (14236922)

Overview of our approach. by Carlos A. Gomez-Uribe (21492788)

Scatter plots for UTS prediction for heat treated and non-heat treated samples. by Vijaykumar S. Jatti (19251057)

“…Scatter plots typically display the x-axis as the actual values and the y-axis as the predicted values. Optimally, the data points ought to align precisely along a 45-degree diagonal line, signifying precise predictions where the anticipated values correspond closely to the actual values. …”

Flow diagram of the reachable workspace. by Zesheng Wang (15244172)

“…A sensitivity analysis is performed using the finite difference method to screen for and eliminate error sources with a negligible impact on the moving platform’s pose. Measurement points are then selected uniformly throughout the workspace using the farthest point sampling algorithm. …”

Size parameters of the 5PUS-RPUR parallel robot. by Zesheng Wang (15244172)

“…A sensitivity analysis is performed using the finite difference method to screen for and eliminate error sources with a negligible impact on the moving platform’s pose. Measurement points are then selected uniformly throughout the workspace using the farthest point sampling algorithm. …”

Structure diagram of branched PUS with errors. by Zesheng Wang (15244172)

“…A sensitivity analysis is performed using the finite difference method to screen for and eliminate error sources with a negligible impact on the moving platform’s pose. Measurement points are then selected uniformly throughout the workspace using the farthest point sampling algorithm. …”

PK subproblem 3. by Zesheng Wang (15244172)

“…A sensitivity analysis is performed using the finite difference method to screen for and eliminate error sources with a negligible impact on the moving platform’s pose. Measurement points are then selected uniformly throughout the workspace using the farthest point sampling algorithm. …”

The two recognized basic PK sub-problems. by Zesheng Wang (15244172)

“…A sensitivity analysis is performed using the finite difference method to screen for and eliminate error sources with a negligible impact on the moving platform’s pose. Measurement points are then selected uniformly throughout the workspace using the farthest point sampling algorithm. …”

Structure diagram of branched RPUR with errors. by Zesheng Wang (15244172)

“…A sensitivity analysis is performed using the finite difference method to screen for and eliminate error sources with a negligible impact on the moving platform’s pose. Measurement points are then selected uniformly throughout the workspace using the farthest point sampling algorithm. …”

5PUS-RPUR parallel robot system. by Zesheng Wang (15244172)

“…A sensitivity analysis is performed using the finite difference method to screen for and eliminate error sources with a negligible impact on the moving platform’s pose. Measurement points are then selected uniformly throughout the workspace using the farthest point sampling algorithm. …”

NZGRC 2022 Poster: Assessing Optimal Flight and Capture Parameters for Post-Thinning Quality Control in Radiata Pine Forest Stands using Unmanned Aerial Vehicles by Atman Dhruva (13442901)

“…These results are expected to inform us of the optimal photogrammetry parameters required for data capture in thinned stands of radiata pine. …”

Supporting data for “Digital immunochromatography: A bead-based bilateral zigzag flow immuno-assay with adaptive differential noise correction for rapid and precise protein detecti... by Zhiting Peng (9722516)

Dataset for: Optimal Transport, Mean Partition, and Uncertainty Assessment in Cluster Analysis by Jia Li (6632237)

“…We overcome that barrier by aligning clusters via optimal transport. Equipped with this technique, we propose a new algorithm to enhance clustering by any baseline method using bootstrap samples. …”

A Composite Likelihood-Based Approach for Change-Point Detection in Spatio-Temporal Processes by Zifeng Zhao (3136278)

“…A computationally efficient pruned dynamic programming algorithm is developed for the challenging criterion optimization problem. …”

Data_Sheet_1_A mixed effects changepoint quantile regression model for longitudinal data with application on COVID-19 data.DOCX by Wafaa I. M. Ibrahim (14672855)

“…In addition, the location of the changepoint is estimated using the usual optimization methods.</p>Results and discussion<p>A simulation study shows that the proposed estimation and inferential procedures perform reasonably well in finite samples. …”