Larger example of stimuli with cue relevant for Task 3. by Marcel F. Hinss (14675457)

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Mean ± SD of the different parameters relative to bipedal stance on soft support with the eyes open recorded in the Chewing and Hand Grip groups. by Cristina Dolciotti (16317330)

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Mean ± SD values of unipedal stance parameters evaluated at the different time points of the postural training session. by Cristina Dolciotti (16317330)

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Time course of Path Length, LFS and Velocity during postural training. by Cristina Dolciotti (16317330)

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Relation of the changes in Y Sway Amplitude and LFS induced by postural training with those in 95% Area. by Cristina Dolciotti (16317330)

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Training-induced changes in bipedal stance. by Cristina Dolciotti (16317330)

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Between-group differences in 95% Area, Y Sway Amplitude and LFS during postural training. by Cristina Dolciotti (16317330)

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Relation between training-related changes in unipedal and bipedal stance. by Cristina Dolciotti (16317330)

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Consolidation of training-induced changes in unipedal stance: 95% Area, Y Sway Amplitude and LFS. by Cristina Dolciotti (16317330)

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Consolidation of training-induced changes in unipedal stance: Path Length, X Sway Amplitude and Velocity. by Cristina Dolciotti (16317330)

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Time course of 95% Area, X and Y Amplitude Sway during postural training. by Cristina Dolciotti (16317330)

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Correlations between unipedal and bipedal stance parameters recorded at the beginning of the training and of the control session. by Cristina Dolciotti (16317330)

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Flow diagram. by Cristina Dolciotti (16317330)

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Biases in larger populations. by Sander W. Keemink (21253563)

“…<p>(<b>A</b>) Maximum absolute bias vs the number of neurons in the population for the Bayesian decoder. Bias decreases with increasing neurons in the population. …”

Participant demographics. * P-values were determined using Fisher-Freeman-Halton test when contingency tables are larger than 2 × 2. by Elie Mulhem (22537863)

Least squares support vector machine model. by Yang Guo (399435)

“…In this paper, a fault diagnosis model of Chiller is designed by combining least squares support vector machine (LSSVM) optimized by hybrid improved northern goshawk optimization algorithm (HINGO) and improved IAdaBoost ensemble learning algorithm. …”

Learn!Bio study: Grouping of Participants. by Katy Andrews (22311170)

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The introduction of mutualisms into assembled communities increases their connectance and complexity while decreasing their richness. by Gui Araujo (22170819)

“…Parameter values: interaction strengths were drawn from a half-normal distribution of zero mean and a standard deviation of 0.2, and strength for consumers was made no larger than the strength for resources. Communities started with 5 non-interacting species. …”

List of network connections predictive of individual SoF values, together with their β weights, in decreasing order. by Yinan Xu (12440894)

Loss of ECRG4 expression decreases neutrophil mobilization from bone marrow reserves. by Katie D. Pool (20141584)

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