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

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The linearly decreasing convergence factor a. by Junhao Wei (6816803)

Based on our time series data(first from the left), the visualizations of linear residual predictions under different decimation rates (next three) show that as the decimation rate decreases, the range of residual variations also becomes smaller. by Hua Huo (1818133)

“…<p>Based on our time series data(first from the left), the visualizations of linear residual predictions under different decimation rates (next three) show that as the decimation rate decreases, the range of residual variations also becomes smaller.…”

The selection of indicators based on their correlation with ground surface data. by Abdolhossein Boali (19996390)

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Obstacle uniform linear motion scenarios. by Long Di (9977453)

“…Furthermore, a probabilistic obstacle motion prediction framework is established through motion pattern analysis to actively optimize the robot’s motion strategy and reduce tracking errors. Simulation-based experimental results demonstrate that, under complex obstacle motion scenarios, the proposed method achieves a 55.8% reduction in trajectory tracking error compared with recently proposed improved APF methods and a 41.5% decrease relative to Dynamic Movement Primitives (DMP) baselines. …”

Performance evaluation of models based on various indicators in 2008 and 2023. by Abdolhossein Boali (19996390)

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Field-based data for spatio-temporal wind erosion risk analysis. by Abdolhossein Boali (19996390)

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Performance evaluation of ensemble model based on various indicators in 2008 and 2023. by Abdolhossein Boali (19996390)

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Changes in baseline CD4 count through week 12 and week 24 according to the linear regression model. by Tosi M. Mwakyandile (16408705)

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Changes in baseline T-cell activation markers through week 12 and week 24 according to the linear regression model. a. by Tosi M. Mwakyandile (16408705)

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The refracted opposition-based learning mechanism. by Yang Guo (399435)

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Changes in baseline T-cell exhaustion, monocyte activation and platelet activation markers through week 12 and week 24 according to the linear regression model. a. by Tosi M. Mwakyandile (16408705)

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Linear regression. by Myriam Aïche (22290591)

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. …”

Decrease in loss during the training process. by Muhammad Idrees (92773)

Why does task performance decrease with burst length? by Swathi Anil (17382903)

Linear contact model. by Qingwen Li (1510687)

<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>" by Kira Shaw (18796168)

“…<p dir="ltr">The CSV file 'Eyreetal_DrainingVein_SourceData' contains the averaged time series traces and extracted metrics from individual experiments used across Figures 1-5 in the manuscript "Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins". The following acronyms included in the CSV file are defined as follows: Hbt is total hemoglobin, Art is artery region, DV is draining vein region, WV is whisker vein region, SEM is standard error mean, TS is time series, max peak is maximum peak, min peak is minima, AUC is area under the curve, WT is wild-type, AD is Alzheimer's disease, ATH is atherosclerosis and MIX is mixed AD/atherosclerosis. …”

Linear regression analysis. by Young Kyu Lee (14076058)

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