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

Experimental setup and trajectory estimation methodology. by Nouman Javed (16624785)

The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

Pre-processing of raw images for pose estimation using physical model-trained convolutional neural network model. by Aniket Ravan (3174171)

NuSeT efficiently addresses common segmentation challenges. by Linfeng Yang (516708)

Supplementary Material for: Automated Detection and Diameter Estimation for Mouse Mesenteric Artery Using Semantic Segmentation by Higaki A. (11035161)

“…We observed a strong positive correlation between the wall/lumen ratio before dissection and the lumen expansion ratio (<i>R</i> = 0.832, <i>p</i> < 0.01). Using multivariate binary logistic regression, 2 models estimating whether the vessel met the size criteria (lumen size of 160–240 μm) were generated with an area under the receiver operating characteristic curve of 0.761 for the upper limit and 0.747 for the lower limit. …”

Fig 12 - by Nisha Yadav (366131)

Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf by Cecilia Lindig-León (7889777)

“…Here, we propose a solution to address the limitation of identifiable motor activities by using combined MIs (i.e., MIs involving 2 or more body parts at the same time). And we propose two new multilabel uses of the Common Spatial Pattern (CSP) algorithm to optimize the signal-to-noise ratio, namely MC2CMI and MC2SMI approaches. …”

Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers by Seunghyun Lee (1372719)

“…Computationally, we propose a scalable estimation pipeline of a layerwise nonlinear spectral initialization followed by a penalized stochastic approximation EM algorithm. …”