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.…”

STL Linear Combination Forecast Graph. by Xiangjuan Liu (618000)

“…Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. Further integration of Spearman correlation analysis and PCA dimensionality reduction created multidimensional feature sets, revealing substantial accuracy improvements: The BiLSTM model achieved an 83.6% cumulative MAE reduction from 1.65 (raw data) to 0.27 (STL-PCA), while traditional models like Prophet showed an 82.2% MAE decrease after feature engineering optimization. …”

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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Relation between training-related changes in unipedal and bipedal stance. 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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The selection of indicators based on their correlation with ground surface data. by Abdolhossein Boali (19996390)

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

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Spatio-temporal data mining modeling for wind erosion risk assessment. by Abdolhossein Boali (19996390)

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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)