Flowchart of DQN algorithm. by Jiandong Qiu (20389944)

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

Subjects:

Prediction performance in CATNAP for VRC07–523-LS + 10–1074. by Brian D. Williamson (6533015)

Prediction performance in CATNAP for 10–1074 + 10E8. by Brian D. Williamson (6533015)

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

“…Importantly, this strategy locates samples adaptively on the transition between facies which improves the performance of conventional \emph{<i>MPS</i>} algorithms. In conclusion, this work shows that preferential sampling can contribute in \emph{<i>MPS</i>} even at very small sampling regimes and, as a corollary, demonstrates that prior models (obtained form a training image) can be used effectively not only to simulate non-sensed variables of the field, but to decide where to measure next.…”

Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP by Xiaoyuan Wang (492534)

Sparse Bayesian Multidimensional Item Response Theory by Jiguang Li (3318924)

“…In this work, we develop a scalable Bayesian EM algorithm to estimate sparse factor loadings from mixed continuous, binary, and ordinal item responses. …”

Schematic overview of SINATRA Pro: A novel framework for discovering biophysical signatures that differentiate classes of proteins. by Wai Shing Tang (9541179)

Adaptive Inference for Change Points in High-Dimensional Data by Yangfan Zhang (6451946)

“…Numerical comparisons using both simulated and real data demonstrate the advantage of our adaptive test and its corresponding estimation method.…”

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

“…We consider two objective function formulations that exist in the literature, which we will refer to as “stacked” and “grouped” objective functions. 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>. …”

Neyman-Pearson Multi-Class Classification via Cost-Sensitive Learning by Ye Tian (220278)

“…Simulations and real data studies validate the effectiveness of our algorithms. …”