smPPARγOE decreased the transcripts of aortic smooth muscle contractile proteins and the smooth muscle transcriptional co-activator, myocardin. by Jennifer M. Kleinhenz (811618)

CNN Output. by Leah R. Enders (18536102)

Number of Fixations, Dwell Time, and Mean Distance to targets and distractors. by Leah R. Enders (18536102)

Mask R-CNN training loss graph. by Dennis Dennis (20490126)

Color space representation across different CNN layers and different CNN architectures. by JohnMark Taylor (8329326)

“…<p>a. A schematic illustration of two possible scenarios of color space correlation across layers within a CNN, using the same notations as those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0253442#pone.0253442.g002" target="_blank">Fig 2A</a>. …”

Color space representation across objects within a CNN layer. by JohnMark Taylor (8329326)

Number of Fixations and Dwell Time on objects during Math Task. by Leah R. Enders (18536102)

Total Gaze-Validated objects in the VE. by Leah R. Enders (18536102)

Magnitude of within-object color coding within each CNN layer. by JohnMark Taylor (8329326)

Cross-orientation inhibition was learned by our DN model and the three-layer CNN, but not significantly by the subunit model. by Max F. Burg (10934141)

Data_Sheet_1_CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data.PDF by Eva Dorschky (5539877)

DataSheet1_Adaptive SPP–CNN–LSTM–ATT wind farm cluster short-term power prediction model based on transitional weather classification.ZIP by Guili Ding (16701822)

Experiment 1 model performance. by Omisa Jinsi (13367452)

Effects of task optimization on pareidolia representations in CNNs. by Pranjul Gupta (20623949)

Changes in the separation and spread of evidence distributions induced by energy, contrast, and variability manipulations. by Medha Shekhar (19502698)

“…Note that the energy results in both panels are equivalent to the 4-layer CNN results from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012578#pcbi.1012578.g003" target="_blank">Fig 3</a>. …”

Detailed evaluation of the automated cell counting method. by Nafiseh Atapour (19727499)

“…Among the 2,216 empty image patches, 50% had a residual density less or equal to 70 mm^-3, and the mean residual density amounted to 170 mm^-3, which shows that the impact of the background on the U-Net CNN results is negligible. …”

Experiment 2. by Lalit Pandey (13195488)

Sensitivity examination of YOLOv4 regarding test image distortion and training dataset attribute for apple flower bud classification by Wenan Yuan (12857036)

Data_Sheet_1_Deep Convolutional Neural Network Based Interictal-Preictal Electroencephalography Prediction: Application to Focal Cortical Dysplasia Type-II.docx by Yoon Gi Chung (9604061)

“…Intracranial EEG recordings of nine epilepsy patients with FCD type-II (four female, five male; mean age: 10.7 years) were analyzed. Seizure onset and channel ranking were annotated by two epileptologists. …”

Parasitemia titration results. by Paul Lebel (11250722)

“…The light blue data show the result of human collaboration on the putative infected and lowest-confidence healthy cells. The Faster R-CNN method was used to detect cells directly on the raw images (yellow). b) The data from a) were re-plotted after compensation for recall and FPR, for an array of confidence threshold values applied to the data. c) Recall and FPR compensation values are plotted as a function of confidence threshold, which both decrease with threshold value, as healthy cells’ median confidence is on the order of 99.9%. d) Manual counting of Giemsa-stained smears by three experienced technicians are plotted with solid red curves (human 1: squares, human 2: hexagrams, human 3: pentagrams), and their arithmetic mean plotted in solid black (circles). …”