RMSE on the test data when fitting (logistic) quadratic models to binary data across increasing measurement error values . by Jakub Stoklosa (3315042)

Absolute relative bias of Fed-GLMM and meta-analysis estimates relative to gold-standard pooled analysis across simulation replicates. by Zhiyu Yan (74647)

Normality assessment through Anderson-Darling test on sample distribution based on TCGA pan-cancer atlas across genes. by Yi-Wen Hsiao (13767916)

Joint Detection of Change Points in Multichannel Single-Molecule Measurements by Hugh Wilson (11797444)

“…We validate the algorithm on simulated data and characterize the power of detection and false positive rate. …”

Summary statistics of the gene expression profile of 9,835 samples in TCGA pan-cancer atlas. by Yi-Wen Hsiao (13767916)

A graphic overview illustrates the random walk and permutation test in the <i>ceRNApairFiltering</i> method. by Yi-Wen Hsiao (13767916)

Accuracy of Fed-GLMM and meta-analysis estimates relative to the gold-standard pooled analysis. by Zhiyu Yan (74647)

Comparison of computation time and estimate accuracy for Fed-GLMM and meta-analysis relative to gold-standard pooled analysis with increasing computing nodes/EHR subsets. by Zhiyu Yan (74647)

Homogeneity and Structure Identification in Semiparametric Factor Models by Chaohui Guo (9460039)

“…In this article, we consider a semiparametric factor model and present a regularized estimation procedure for linear component identification on the transformed factor that combines B-spline basis function approximations and the smoothly clipped absolute deviation penalty. In addition, a binary segmentation based algorithm is also developed to identify the homogeneous groups in loading parameters, producing more efficient estimation by pooling information across units within the same group. …”

Reverse engineering information flow using stimulus-encoding and stimulus-transfer estimation algorithms. by Gabriel Matías Lorenz (21094672)

Fitting times in a real biological setting. by Michela C. Massi (14599915)

Accessibility of translation initiation sites is the strongest predictor of heterologous protein expression in <i>E. coli</i>. by Bikash K. Bhandari (11524776)

Thesis-RAMIS-Figs_Slides by Felipe Santibañez-Leal (10967991)

“…In this direction, the option of estimating the statistics of the model directly from the training image (performing a refined pattern search instead of simulating data) is a very promising.<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…”

Bootstrap-based inference for multiple mean-variance changepoint models by Yang Li (7082)

“…This method integrates the weighted bootstrap with the Sequential Binary Segmentation (SBS) algorithm. Not only does our technique pinpoint the location and number of change points, but it also determines the type of change for each estimated point, specifying whether the change occurred in the mean, variance, or both. …”

Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods by Jiacong Du (12035845)

“…Building on existing work, we (i) derive and implement efficient cyclic coordinate descent and majorization-minimization optimization algorithms for continuous and binary outcome data, (ii) incorporate adaptive shrinkage penalties, (iii) compare these methods through simulation, and (iv) develop an R package <i>miselect</i>. …”

A Scalable Partitioned Approach to Model Massive Nonstationary Non-Gaussian Spatial Datasets by Benjamin Seiyon Lee (11642352)

“…Examples include count data on disease incidence and binary satellite data on cloud mask (cloud/no-cloud). …”

iNCog-EEG (ideal vs. Noisy Cognitive EEG for Workload Assessment) Dataset by Fariya Bintay Shafi (21692408)

“…Inside each folder, four <b>.EDF</b> files represent the workload conditions:</p><pre><pre>subxx_nw.EDF → No Workload (resting state) <br>subxx_lw.EDF → Low Workload (easy multitasking) <br>subxx_mw.EDF → Moderate Workload (medium multitasking) <br>subxx_hw.EDF → High Workload (hard multitasking) <br></pre></pre><ul><li><b>Subjects 01–30:</b> Clean EEG recordings</li><li><b>Subjects 31–40:</b> Noisy EEG recordings with real-world artifacts</li></ul><p dir="ltr">This structure ensures straightforward differentiation between clean vs. noisy data and across workload levels.</p><h3>Applications</h3><p dir="ltr">This dataset can be applied to a wide range of research areas, including:</p><ul><li>EEG signal denoising and artifact rejection</li><li>Binary and hierarchical <b>cognitive workload classification</b></li><li>Development of <b>robust Brain–Computer Interfaces (BCIs)</b></li><li>Benchmarking algorithms under <b>ideal and noisy conditions</b></li><li>Multitasking and mental workload assessment in <b>real-world scenarios</b></li></ul><p dir="ltr">By combining controlled multitasking protocols with deliberately introduced environmental noise, <b>iNCog-EEG provides a comprehensive benchmark</b> for advancing EEG-based workload recognition systems in both clean and challenging conditions.…”