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.…”

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)

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

Subjects:

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)

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)

Subjects:

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

Subjects:

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

Subjects:

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

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

Subjects:

Linear regression. by Myriam Aïche (22290591)

A Locally Linear Dynamic Strategy for Manifold Learning. by Weifan Wang (4669081)

“…For 10-30% noise, where the Hebbian network employs a local linear transform, learning selectively increases signal direction alignment (blue) while simultaneously decreasing noise direction alignment (orange). …”

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

“…(<b>B</b>) Minimum and maximum bias in a long 1D array with rectified cosine tuning. Bias expressed in terms of the distance between neurons in a linear population of equally spaced neurons. …”

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

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

<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)

“…The locomotion values (traces and metrics) are in arbitrary units with larger integers representing a greater displacement of the spherical treadmill, the hemodynamic (Hbt) values (traces and metrics) are a percentage change from the normalised baseline (prior to stimulus presentation), and the corresponding time series vector is presented in seconds. …”

Linear contact model. by Qingwen Li (1510687)

Linear regression analysis. by Young Kyu Lee (14076058)

Deep learning-based locomotive syndrome prediction method. by Junichi Kushioka (12236447)

Subjects: